NURSING INFORMATICS AND THE FOUNDATION OF KNOWLEDGE

THIRD EDITION

The Pedagogy

Nursing Informatics and the Foundation of Knowledge, Third Edition drives comprehension through a variety of strategies geared toward meeting the learning needs of students, while also generating enthusiasm about the topic. This interactive approach addresses diverse learning styles, making this the ideal text to ensure mastery of key concepts. The pedagogical aids that appear in most chapters include the following:

NURSING INFORMATICS AND THE FOUNDATION OF KNOWLEDGE

THIRD EDITION

Dee McGonigle, PhD, RN, CNE, FAAN, ANEF Chair, Virtual Learning Environments and Professor, Graduate Programs

Chamberlain College of Nursing Member, Informatics and Technology Expert Panel (ITEP)

American Academy of Nursing Member, Serious Gaming and Virtual Environments Special Interest Group for the Society for Simulation in Healthcare (SSH)

Kathleen Mastrian, PhD, RN Associate Professor and Program Coordinator for Nursing

Pennsylvania State University, Shenango Sr. Managing Editor, Online Journal of Nursing Informatics (OJNI)

World Headquarters Jones & Bartlett Learning 5 Wall Street Burlington, MA 01803 978-443-5000 info@jblearning.com www.jblearning.com

Jones & Bartlett Learning books and products are available through most bookstores and online booksellers. To contact Jones & Bartlett Learning directly, call 800-832-0034, fax 978-443-8000, or visit our website, www.jblearning.com.

Substantial discounts on bulk quantities of Jones & Bartlett Learning publications are available to corporations, professional associations, and other qualified organizations. For details and specific discount information, contact the special sales department at Jones & Bartlett Learning via the above contact information or send an email to specialsales@jblearning.com.

Copyright © 2015 by Jones & Bartlett Learning, LLC, an Ascend Learning Company

All rights reserved. No part of the material protected by this copyright may be reproduced or utilized in any form, electronic or mechanical, including photocopying, recording, or by any information storage and retrieval system, without written permission from the copyright owner.

The content, statements, views, and opinions herein are the sole expression of the respective authors and not that of Jones & Bartlett Learning, LLC. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not constitute or imply its endorsement or recommendation by Jones & Bartlett Learning, LLC and such reference shall not be used for advertising or product endorsement purposes. All trademarks displayed are the trademarks of the parties noted herein. Nursing Informatics and the Foundation of Knowledge, Third Edition is an independent publication and has not been authorized, sponsored, or otherwise approved by the owners of the trademarks or service marks referenced in this product.

There may be images in this book that feature models; these models do not necessarily endorse, represent, or participate in the activities represented in the images. Any screenshots in this product are for educational and instructive purposes only. Any individuals and scenarios featured in the case studies throughout this product may be real or fictitious, but are used for instructional purposes only.

The authors, editor, and publisher have made every effort to provide accurate information. However, they are not responsible for errors, omissions, or for any outcomes related to the use of the contents of this book and take no responsibility for the use of the products and procedures described. Treatments and side effects described in this book may not be applicable to all people; likewise, some people may require a dose or experience a side effect that is not described herein. Drugs and medical devices are discussed that may have limited availability controlled by the Food and Drug Administration (FDA) for use only in a research study or clinical trial. Research, clinical practice, and government regulations often change the accepted standard in this field. When consideration is being given to use of any drug in the clinical setting, the health care provider or reader is responsible for determining FDA status of the drug, reading the package insert, and reviewing prescribing information for the most up-to-date recommendations on dose, precautions, and contraindications, and determining the appropriate usage for the product. This is especially important in the case of drugs that are new or seldom used.

Production Credits Executive Publisher: William Brottmiller Senior Editor: Amanda Martin Editorial Assistant: Rebecca Myrick Production Editor: Keith Henry Senior Marketing Manager: Jennifer Stiles Photo Research and Permissions Coordinator: Joe Veiga VP, Manufacturing and Inventory Control: Therese Connell Composition: diacriTech Cover Design: Kristin E. Parker Cover Image: (top) © antishock/ShutterStock, Inc., (bottom) © James Daniels/ShutterStock, Inc. Printing and Binding: Edwards Brothers Malloy Cover Printing: Edwards Brothers Malloy

To order this product, use ISBN: 978-1-284-04351-8

Library of Congress Cataloging-in-Publication Data Nursing informatics and the foundation of knowledge / [edited by] Dee McGonigle, Kathleen Mastrian.—3e.

p. ; cm. Includes bibliographical references and index. ISBN 978-1-284-04158-3 (paperback) I. McGonigle, Dee, editor of compilation. II. Mastrian, Kathleen Garver, editor of compilation. [DNLM: 1. Nursing Informatics. 2. Knowledge. WY 26.5] RT50.5 651.5’04261—dc23

2013046566

6048

Printed in the United States of America 18 17 16 15 14 10 9 8 7 6 5 4 3 2 1

Special Acknowledgments We want to express our sincere appreciation to the staff at Jones & Bartlett Learning, especially Amanda, Becky, and Keith, for their continued encouragement, assistance, and support during the writing process and publication of our book.

Contents

Preface Acknowledgments Authors’ Note Contributors

SECTION I: BUILDING BLOCKS OF NURSING INFORMATICS

1 Nursing Science and the Foundation of Knowledge Kathleen Mastrian and Dee McGonigle

Introduction Quality and Safety Education for Nurses Summary References

2 Introduction to Information, Information Science, and Information Systems Dee McGonigle and Kathleen Mastrian

Introduction Information Information Science Information Processing Information Science and the Foundation of Knowledge Introduction to Information Systems Information Systems Summary References

3 Computer Science and the Foundation of Knowledge Model June Kaminski

Introduction The Computer as a Tool for Managing Information and Generating Knowledge Components What Is the Relationship of Computer Science to Knowledge? How Does the Computer Support Collaboration and Information Exchange? What Is the Human–Technology Interface? Looking to the Future Summary Working Wisdom Application Scenario Internet and Software Resources References

4 Introduction to Cognitive Science and Cognitive Informatics Dee McGonigle and Kathleen Mastrian

Introduction Cognitive Science Sources of Knowledge Nature of Knowledge How Knowledge and Wisdom Are Used in Decision Making Cognitive Informatics CI and Nursing Practice What Is AI? Summary References

5 Ethical Applications of Informatics Kathleen Mastrian, Dee McGonigle, and Nedra Farcus

Introduction Ethics Bioethics Ethical Issues and Social Media Ethical Dilemmas and Morals Ethical Decision Making Theoretical Approaches to Healthcare Ethics Applying Ethics to Informatics Case Analysis Demonstration New Frontiers in Ethical Issues Summary References

SECTION II: PERSPECTIVES ON NURSING INFORMATICS

6 Overview of Nursing Informatics Ramona Nelson and Nancy Staggers

Introduction Metastructures, Concepts, and Tools of NI The Future of NI Summary References

7 Informatics Roles and the Knowledge Work of Nursing Julie A. Kenney and Ida Androwich

Introduction The Nurse as a Knowledge Worker The Knowledge Needs and Competencies of Nurses What Is Nursing Informatics Specialty Practice? The Future of Nursing Informatics Summary References

8 Information and Knowledge Needs of Nurses in the 21st Century Lynn M. Nagle, Nicholas Hardiker, Kathleen Mastrian, and Dee McGonigle

Introduction Definition and Goal of Informatics Health Information Technologies Impacting Nursing Nurses Creating and Deriving New Knowledge Generating Nursing Knowledge Challenges in Getting There The Future Summary References

9 Legislative Aspects of Nursing Informatics: HITECH and HIPAA Kathleen M. Gialanella, Kathleen Mastrian, and Dee McGonigle

Introduction Overview of the HITECH Act How a National HIT Infrastructure Is Being Developed How the HITECH Act Changed HIPAA Implications for Nursing Practice Summary References

SECTION III: NURSING INFORMATICS ADMINISTRATIVE APPLICATIONS: PRECARE AND CARE SUPPORT

10 Systems Development Life Cycle: Nursing Informatics and Organizational Decision Making Dee McGonigle and Kathleen Mastrian Introduction Waterfall Model Rapid Prototyping or Rapid Application Development

Object-Oriented Systems Development Dynamic System Development Method Computer-Aided Software Engineering Tools Open Source Software and Free/Open Source Software Interoperability Summary References

11 Administrative Information Systems Marianela Zytkowski, Susan Paschke, Dee McGonigle, and Kathleen Mastrian Introduction Types of Healthcare Organization Information Systems Communication Systems Core Business Systems Order Entry Systems Patient Care Support Systems Department Collaboration and Exchange of Knowledge and Information Summary References

12 The Human–Technology Interface Judith A. Effken, Dee McGonigle, and Kathleen Mastrian Introduction The Human–Technology Interface The Human–Technology Interface Problem Improving the Human–Technology Interface A Framework for Evaluation Future of the Human–Technology Interface Summary References

13 Electronic Security Lisa Reeves Bertin, Dee McGonigle, and Kathleen Mastrian Introduction Securing Network Information Authentication of Users Threats to Security Security Tools Off-Site Use of Portable Devices Summary References

14 Nursing Informatics: Improving Workflow and Meaningful Use Denise Hammel-Jones, Dee McGonigle, and Kathleen Mastrian Introduction Workflow Analysis Purpose Workflow and Technology Workflow Analysis and Informatics Practice Informatics as a Change Agent Measuring the Results Future Directions Summary References

SECTION IV: NURSING INFORMATICS PRACTICE APPLICATIONS: CARE DELIVERY

15 The Electronic Health Record and Clinical Informatics Emily B. Barey, Kathleen Mastrian, and Dee McGonigle Introduction Setting the Stage Components of Electronic Health Records Advantages of Electronic Health Records Ownership of Electronic Health Records Flexibility and Expandability The Future Summary References

16 Informatics Tools to Promote Patient Safety and Clinical Outcomes Kathleen Mastrian and Dee McGonigle Introduction What Is a Culture of Safety? Strategies for Developing a Safety Culture Informatics Technologies for Patient Safety Role of the Nurse Informaticist Summary References

17 Supporting Consumer Information and Education Needs Kathleen Mastrian and Dee McGonigle Introduction Consumer Demand for Information Health Literacy and Health Initiatives Healthcare Organization Approaches to Education Promoting Health Literacy in School-Aged Children Supporting Use of the Internet for Health Education Future Directions Summary References

18 Using Informatics to Promote Community/Population Health Margaret Ross Kraft, Ida Androwich, Kathleen Mastrian, and Dee McGonigle Introduction Core Public Health Functions Community Health Risk Assessment: Tools for Acquiring Knowledge Processing Knowledge and Information to Support Epidemiology and Monitoring Disease Outbreaks Applying Knowledge to Health Disaster Planning and Preparation Informatics Tools to Support Communication and Dissemination Using Feedback to Improve Responses and Promote Readiness Summary References

19 Telenursing and Remote Access Telehealth Original contribution by Audrey Kinsella, Kathleen Albright, Sheldon Prial, and Schuyler F. Hoss; revised by Kathleen Mastrian and Dee McGonigle Introduction History of Telehealth Nursing Aspects of Telehealth Driving Forces for Telehealth Telehealth Care Telenursing Telehealth Patient Populations Tools of Home Telehealth Home Telehealth Software Home Telehealth Practice and Protocols Legal, Ethical, and Regulatory Issues A Day in the Life of a Home Telenurse The Patient’s Role in Telehealth Telehealth Research The Foundation of Knowledge Model and Home Telehealth Parting Thoughts for the Future and a View Toward What the Future Holds Summary References

SECTION V: EDUCATION APPLICATIONS OF NURSING INFORMATICS

20 Nursing Informatics and Nursing Education Heather E. McKinney, Sylvia DeSantis, Dee McGonigle, and Kathleen Mastrian Introduction: Nursing Education and the Foundation of Knowledge Model Knowledge Acquisition and Sharing Hardware and Software Considerations Delivery Modalities Technology Tools Internet Tools: Webcasts, Searching, Instant Messaging, Chats and Online Discussions, Electronic Mailing Lists, and Portals Promoting Active and Collaborative Learning

Knowledge Assessment Methods Knowledge Dissemination and Sharing The Future Exploring Information Fair Use and Copyright Restrictions Summary References

21 Simulation in Nursing Informatics Education Nickolaus Miehl Introduction Nursing Informatics Competencies in Nursing Education A Case for Simulation Incorporating EHRs into the Learning Environment Challenges and Opportunities What Does the Future Hold? Summary References

22 Games, Simulations, and Virtual Worlds for Educators Brett Bixler Introduction Case Scenario Educational Games Educational Simulations Virtual Worlds Choosing Among Educational Games, Simulations, and Virtual Worlds The Future of Games, Virtual Worlds, and Simulations Summary References

SECTION VI: NURSING INFORMATICS: RESEARCH APPLICATIONS

23 Research: Data Collection, Processing, and Analysis Heather E. McKinney, Sylvia DeSantis, Kathleen Mastrian, and Dee McGonigle Introduction: Nursing Research and the Foundation of Knowledge Model Knowledge Generation Through Nursing Research Acquiring Previously Gained Knowledge Through Internet and Library Holdings Fair Use of Information and Sharing Informatics Tools for Collecting Data and Storage of Information Tools for Processing Data and Data Analysis The Future Summary References

24 Data Mining as a Research Tool Dee McGonigle and Kathleen Mastrian Introduction: Big Data, Data Mining, and Knowledge Discovery KDD and Research Data Mining Concepts Data Mining Techniques Data Mining Models Benefits of KDD Ethics of Data Mining Summary References

25 Translational Research: Generating Evidence for Practice Jennifer Bredemeyer and Ida Androwich Introduction Clarification of Terms History of Evidence-Based Practice Evidence Bridging the Gap Between Research and Practice Barriers to and Facilitators of Evidence-Based Practice The Role of Informatics Developing EBP Guidelines Meta-Analysis and Generation of Knowledge

The Future Summary References

26 Bioinformatics, Biomedical Informatics, and Computational Biology Dee McGonigle and Kathleen Mastrian Introduction Bioinformatics, Biomedical Informatics, and Computational Biology Defined Why Are Bioinformatics and Biomedical Informatics So Important? What Does the Future Hold? Summary References

SECTION VII: IMAGINING THE FUTURE OF NURSING INFORMATICS

27 The Art of Caring in Technology-Laden Environments Kathleen Mastrian and Dee McGonigle Introduction Caring Theories Presence Strategies for Enhancing Caring Presence Reflective Practice Summary References

28 Emerging Technologies and the Generation of Knowledge Peter J. Murray, W. Scott Erdley, Dee McGonigle, and Kathleen Mastrian Introduction Looking Back from the Future Historical Overview Some Technologies of Today Some Views of What Will Affect the Future Some Emerging Technologies and Other Issues That Will Impact Nursing and Health Care 491 Summary References

29 Nursing Informatics and the Foundation of Knowledge Dee McGonigle and Kathleen Mastrian Introduction Foundation of Knowledge Revisited Knowledge Use in Practice Summary References

Abbreviations Glossary Index

Preface

The idea for this text originated with the development of nursing informatics (NI) classes, the publication of articles related to technology-based education, and the creation of the Online Journal of Nursing Informatics (OJNI), which Dee McGonigle cofounded. Like most nurse informaticists, we fell into the specialty; our love affair with technology and gadgets and our willingness to be the first to try new things helped to hook us into the specialty of informatics. The rapid evolution of technology and its transformation of the ways of nursing prompted us to try to capture the essence of NI in a text.

As we were developing the first edition, we realized that we could not possibly know all there is to know about informatics and the way in which it supports nursing practice, education, administration, and research. We also knew that our faculty roles constrained our opportunities for exposure to changes in this rapidly evolving field. Therefore, we developed a tentative outline and a working model of the theoretical framework for the text and invited participation from informatics experts and specialists around the world. We were pleased with the enthusiastic responses we received from some of those invited contributors and a few volunteers who heard about the text and asked to participate in their particular area of expertise.

In the second edition, we invited the original contributors to revise and update their chapters. Not everyone chose to participate in the second edition, so we revised several of the chapters using the original work as a springboard. The revisions to the text were guided by the contributors’ growing informatics expertise and the reviews provided by textbook adopters. In the revisions, we sought to do the following:

• Expand the audience focus to include nursing students from BS through DNP programs as well as nurses thrust into informatics roles in clinical agencies.

• Include, whenever possible, an attention-grabbing case scenario as an introduction or an illustrative case scenario demonstrating why the topic is important.

• Include important research findings related to the topic. Many chapters have research briefs presented in text boxes to encourage the reader to access current research.

• Focus on cutting-edge innovations, meaningful use, and patient safety as appropriate to each topic. • Include a paragraph describing what the future holds for each topic.

New chapters that were added to the second edition included those focusing on technology and patient safety, system development life cycle, workflow analysis, gaming, simulation, and bioinformatics.

In this, the third edition, we reviewed and updated all of the chapters, reordered some chapters for better content flow, eliminated duplicated content, split the education and research content into two sections, integrated social media content, and added two new chapters: Data Mining as a Research Tool and The Art of Caring in Technology-Laden Environments.

We believe that this text provides a comprehensive elucidation of this exciting field. Its theoretical underpinning is the Foundation of Knowledge model. This model is introduced in its entirety in the first chapter (Nursing Science and the Foundation of Knowledge), which discusses nursing science and its relationship to NI. We believe that humans are organic information systems that are constantly acquiring, processing, and generating information or knowledge in both their professional and personal lives. It is their high degree of knowledge that characterizes humans as extremely intelligent, organic machines. Individuals have the ability to manage knowledge— an ability that is learned and honed from birth. We make our way through life interacting with our environment and being inundated with information and knowledge. We experience our environment and learn by acquiring, processing, generating, and disseminating knowledge. As we interact in our environment, we acquire knowledge that we must process. This processing effort causes us to redefine and restructure our knowledge base and generate new knowledge. We then share (disseminate) this new knowledge and receive feedback from others. The dissemination and feedback initiate this cycle of knowledge over again, as we acquire, process, generate, and disseminate the knowledge gained from sharing and reexploring our own knowledge base. As others respond to our knowledge dissemination and we acquire new knowledge, we engage in rethinking and reflecting on our knowledge, processing, generating, and then disseminating anew.

The purpose of this text is to provide a set of practical and powerful tools to ensure that the reader gains an understanding of NI and moves from information through knowledge to wisdom. Defining the demands of nurses and providing tools to help them survive and succeed in the Knowledge Era remains a major challenge. Exposing nursing students and nurses to the principles and tools used in NI helps to prepare them to meet the challenge of practicing nursing in the Knowledge Era while striving to improve patient care at all levels.

The text provides a comprehensive framework that embraces knowledge so that readers can develop their knowledge repositories and the wisdom necessary to act on and apply that knowledge. The text is divided into seven sections.

• The Building Blocks of Nursing Informatics section covers the building blocks of NI: nursing science, information science, computer science, cognitive science, and the ethical management of information.

• The Perspectives on Nursing Informatics section provides readers with a look at various viewpoints on NI and NI practice as described by experts in the field.

• The Nursing Informatics Administrative Applications: Precare and Care Support section covers important functions of administrative applications of NI.

• The Nursing Informatics Practice Applications: Care Delivery section covers healthcare delivery applications including electronic health records (EHRs), clinical information systems, telehealth, patient safety, patient and community education,

and care management. • The Education Applications of Nursing Informatics section presents subject matter on how informatics supports nursing

education. • The Nursing Informatics: Research Applications section covers informatics tools to support nursing research, including data

mining and bioinformatics. • The Imagining the Future of Nursing Informatics section focuses on the future of NI, emphasizes the need to preserve caring

functions in technology-laden environments, and summarizes the relationship of informatics to the Foundation of Knowledge model and organizational knowledge management.

The introduction to each section explains the relationship between the content of that section and the Foundation of Knowledge model. This text places the material within the context of knowledge acquisition, processing, generation, and dissemination. It serves both nursing students (BS to DNP/PhD) and professionals who need to understand, use, and evaluate NI knowledge. As nursing professors, our major responsibility is to prepare the practitioners and leaders in the field. Because NI permeates the entire scope of nursing (practice, administration, education, and research), nursing education curricula must include NI. Our primary objective is to develop the most comprehensive and user-friendly NI text on the market to prepare nurses for current and future practice challenges. In particular, this text provides a solid groundwork from which to integrate NI into practice, education, administration, and research.

Goals of this text are as follows: • Impart core NI principles that should be familiar to every nurse and nursing student • Help the reader understand knowledge and how it is acquired, processed, generated, and disseminated • Explore the changing role of NI professionals • Demonstrate the value of the NI discipline as an attractive field of specialization

Meeting these goals will help nurses and nursing students understand and use fundamental NI principles so that they efficiently and effectively function as current and future nursing professionals. The overall vision, framework, and pedagogy of this text offer benefits to readers by highlighting established principles while drawing out new ones that continue to emerge as nursing and technology evolve.

Acknowledgments

We are deeply grateful to the contributors who provided this text with a richness and diversity of content that we could not have captured alone. Joan Humphrey provided social media content integrated throughout the text. We especially wish to acknowledge the superior work of Alicia Mastrian, graphic designer of the Foundation of Knowledge model, which serves as the theoretical framework on which this text is anchored. We could never have completed this project without the dedicated and patient efforts of the Jones & Bartlett Learning staff, especially Amanda Martin and Becky Myrick. Both fielded our questions and concerns in a very professional and respectful manner.

Dee acknowledges the undying love, support, patience, and continued encouragement of her best friend and husband, Craig, and her son, Craig, who has also made her so very proud. She sincerely thanks her cousins Camille, Glenn, Mary Jane, and Sonny, and her dear friends for their support and encouragement, especially Renee.

Kathy acknowledges the loving support of her family: husband Chip; children Ben and Alicia; sisters Carol and Sue; and parents Bob and Rosalie Garver. Kathy also acknowledges those friends who understand the importance of validation, especially Katie, Bobbie, Kathy, Anne, and Barbara.

Authors’ Note

This text provides an overview of nursing informatics from the perspective of diverse experts in the field, with a focus on nursing informatics and the Foundation of Knowledge model. We want our readers and students to focus on the relationship of knowledge to informatics and to embrace and maintain the caring functions of nursing—messages all too often lost in the romance with technology. We hope you enjoy the text!

Contributors

Ida Androwich, PhD, RN, BC, FAAN Loyola University Chicago School of Nursing Maywood, IL

Emily Barey, MSN, RN Director of Nursing Informatics Epic Systems Corporation Madison, WI

Lisa Reeves Bertin, BS, EMBA Pennsylvania State University Sharon, PA

Brett Bixler, PhD Pennsylvania State University University Park, PA

Jennifer Bredemeyer, RN Loyola University Chicago School of Nursing Skokie, IL

Steven Brewer, PhD Assistant Professor, Administration of Justice Pennsylvania State University Sharon, PA

Sylvia M. DeSantis, MA Pennsylvania State University University Park, PA

Eric R. Doerfler, PhD, NP Pennsylvania State University School of Nursing Middletown, PA

Judith Effken, PhD, RN, FACMI University of Arizona College of Nursing Tucson, AZ

William Scott Erdley, DNS, RN Niagara University Niagara University, NY

Nedra Farcus, MSN, RN Pennsylvania State University, Altoona Altoona, PA

Kathleen M. Gialanella, JD, RN, LLM Law Offices Westfield, NJ Associate Adjunct Professor Teachers College, Columbia University New York, NY Adjunct Professor

Seton Hall University, College of Nursing & School of Law South Orange & Newark, NJ

Denise Hammel-Jones, MSN, RN-BC, CLSSBB Greencastle Associates Consulting Malvern, PA

Nicholas Hardiker, PhD, RN Senior Research Fellow University of Salford School of Nursing & Midwifery Salford, UK

Glenn Johnson, MLS Pennsylvania State University University Park, PA

June Kaminski, MSN, RN Kwantlen University College Surrey, British Columbia, Canada

Julie Kenney, MSN, RNC-OB Clinical Analyst Advocate Health Care Oak Brook, IL

Margaret Ross Kraft, PhD, RN Loyola University Chicago School of Nursing Maywood, IL

Wendy L. Mahan, PhD, CRC, LPC Pennsylvania State University University Park, PA

Heather McKinney, PhD Pennsylvania State University University Park, PA

Nickolaus Miehl, MSN, RN Pennsylvania State University Erie, PA

Peter J. Murray, PhD, RN, FBCS Coachman’s Cottage Nocton, Lincoln, UK

Lynn M. Nagle, PhD, RN Assistant Professor University of Toronto Toronto, Ontario, Canada

Ramona Nelson, PhD, RN-BC, FAAN, ANEF Professor Emerita, Slippery Rock University President, Ramona Nelson Consulting Pittsburgh, PA

Nancy Staggers, PhD, RN, FAAN Professor, Informatics University of Maryland Baltimore, MD

Jeff Swain Instructional Designer Pennsylvania State University University Park, PA

Denise D. Tyler, MSN/MBA, RN-BC Implementation Specialist

Healthcare Provider, Consulting ACS, a Xerox Company Dearborn, MI

The Editors also acknowledge the work of the following first edition contributors (original contributions edited by McGonigle and Mastrian for second edition):

Kathleen Albright, BA, RN Strategic Account Manager at GE Healthcare Philadelphia, PA

Schuyler F. Hoss, BA Northwest Healthcare Management Vancouver, WA

Audrey Kinsella, MA, MS Information for Tomorrow Telehealth Planning Services Asheville, NC

Susan M. Paschke, MSN, RN The Cleveland Clinic Cleveland, OH

Sheldon Prial, RPH, BS Pharmacy Sheldon Prial Consultance Melbourne, FL

Jackie Ritzko Pennsylvania State University Hazelton, PA

Marianela Zytkowsi, MSN, RN The Cleveland Clinic Cleveland, OH

Section I

Building Blocks of Nursing Informatics

Chapter 1 Nursing Science and the Foundation of Knowledge Chapter 2 Introduction to Information, Information Science, and Information Systems Chapter 3 Computer Science and the Foundation of Knowledge Model Chapter 4 Introduction to Cognitive Science and Cognitive Informatics Chapter 5 Ethical Applications of Informatics

Nursing professionals are information-dependent knowledge workers. As health care continues to evolve in an increasingly competitive information marketplace, professionals—that is, the knowledge workers—must be well prepared to make significant contributions by harnessing appropriate and timely information. Nursing informatics (NI), a product of the scientific synthesis of information in nursing, encompasses concepts from computer science, cognitive science, information science, and nursing science. NI continues to evolve as more and more professionals access, use, and develop the information, computer, and cognitive sciences necessary to advance nursing science for the betterment of patients and the profession. Regardless of their future roles in the healthcare milieu, it is clear that nurses need to understand the ethical application of computer, information, and cognitive sciences to advance nursing science.

To implement NI, one must view it from the perspective of both the current healthcare delivery system and specific, individual organizational needs, while anticipating and creating future applications in both the healthcare system and the nursing profession. Nursing professionals should be expected to discover opportunities to use NI, participate in the design of solutions, and be challenged to identify, develop, evaluate, modify, and enhance applications to improve patient care. This text is designed to provide the reader with the information and knowledge needed to meet this expectation.

Section I presents an overview of the building blocks of NI: nursing, information, computer, and cognitive sciences. Also included in this section is a chapter on ethical applications of healthcare informatics. This section lays the foundation for the remainder of the book.

The Nursing Science and the Foundation of Knowledge chapter describes nursing science and introduces the Foundation of Knowledge model as the conceptual framework for the book. In this chapter, a clinical case scenario is used to illustrate the concepts central to nursing science. A definition of nursing science is also derived from the American Nurses Association’s definition of nursing. Nursing science is the ethical application of knowledge acquired through education, research, and practice to provide services and interventions to patients to maintain, enhance, or restore their health, and to acquire, process, generate, and disseminate nursing knowledge to advance the nursing profession. Information is a central concept and health care’s most valuable resource. Information science and systems, together with computers, are constantly changing the way healthcare organizations conduct their business. This will continue to evolve.

To prepare for these innovations, the reader must understand fundamental information and computer concepts, covered in the Introduction to Information, Information Science, and Information Systems and Computer Science and the Foundation of Knowledge Model chapters, respectively. Information science deals with the interchange (or flow) and scaffolding (or structure) of information and involves the application of information tools for solutions to patient care and business problems in health care. To be able to use and synthesize information effectively, an individual must be able to obtain, perceive, process, synthesize, comprehend, convey, and manage the information. Computer science deals with understanding the development, design, structure, and relationship of computer hardware and software. This science offers extremely valuable tools that, if used skillfully, can facilitate the acquisition and manipulation of data and information by nurses, who can then synthesize these resources into an ever-evolving knowledge and wisdom base. This not only facilitates professional development and the ability to apply evidence-based practice decisions within nursing care, but, if the results are disseminated and shared, can also advance the profession’s knowledge base. The development of knowledge tools, such as the automation of decision making and strides in artificial intelligence, has altered the understanding of knowledge and its representation. The ability to structure knowledge electronically facilitates the ability to share knowledge structures and enhance collective knowledge.

As discussed in the Introduction to Cognitive Science and Cognitive Informatics chapter, cognitive science deals with how the human mind functions. This science encompasses how people think, understand, remember, synthesize, and access stored information and knowledge. The nature of knowledge, including how it is developed, used, modified, and shared, provides the basis for continued learning and intellectual growth.

The Ethical Applications of Informatics chapter focuses on ethical issues associated with managing private information with technology and provides a framework for analyzing ethical issues and supporting ethical decision making.

The material within this book is placed within the context of the Foundation of Knowledge model (shown in Figure I-1 and periodically throughout the book, but more fully introduced and explained in the Nursing Science and the Foundation of Knowledge

chapter). The Foundation of Knowledge model is used throughout the text to illustrate how knowledge is used to meet the needs of healthcare delivery systems, organizations, patients, and nurses. It is through interaction with these building blocks—the theories, architecture, and tools—that one acquires the bits and pieces of data necessary, processes these into information, and generates and disseminates the resulting knowledge. Through this dynamic exchange, which includes feedback, individuals continue the interaction and use of these sciences to input or acquire, process, and output or disseminate generated knowledge. Humans experience their environment and learn by acquiring, processing, generating, and disseminating knowledge. When they then share (disseminate) this new knowledge and receive feedback on the knowledge they have shared, the feedback initiates the cycle of knowledge all over again. As individuals acquire, process, generate, and disseminate knowledge, they are motivated to share, rethink, and explore their own knowledge base. This complex process is captured in the Foundation of Knowledge model. Throughout the chapters in the Building Blocks of Nursing Informatics section, readers are challenged to think about how the model can help them to understand the ways in which they acquire, process, generate, disseminate, and then receive feedback on their new knowledge of the building blocks of NI.

Figure I-1 Foundation of Knowledge Model Source: Designed by Alicia Mastrian.

Chapter 1

Nursing Science and the Foundation of Knowledge Kathleen Mastrian and Dee McGonigle

OBJECTIVES

1. Define nursing science and its relationship to various nursing roles and nursing informatics. 2. Introduce the Foundation of Knowledge model as the organizing conceptual framework for the text. 3. Explain the relationships among knowledge acquisition, knowledge processing, knowledge generation, knowledge dissemination, and wisdom.

Key Terms

Borrowed theory Building blocks Clinical databases Clinical practice guidelines Conceptual framework Data Data mining Evidence Feedback Foundation of Knowledge model Information Knowledge Knowledge acquisition Knowledge dissemination Knowledge generation Knowledge processing Knowledge worker Nursing informatics Nursing science Nursing theory Relational database Transparent wisdom

Introduction Nursing informatics is defined as the combination of nursing science, information science, and computer science. This chapter focuses on nursing science as one of the building blocks of nursing informatics, although in this text the traditional definition of nursing informatics is extended to include cognitive science as one of the building blocks. The Foundation of Knowledge model is also introduced as the organizing conceptual framework of this text, and the model is tied to nursing science and the practice of nursing informatics. To lay the groundwork for this discussion, consider the following patient scenario:

Tom H. is a registered nurse who works in a very busy metropolitan hospital emergency room. He has just admitted a 79-year- old man whose wife brought him to the hospital because he is having trouble breathing. Tom immediately clips a pulse oximeter to the patient’s finger and performs a very quick assessment of the patient’s other vital signs. He discovers a rapid pulse rate and a decreased oxygen saturation level in addition to the rapid and labored breathing. Tom determines that the patient is not in immediate danger and that he does not require intubation. Tom focuses his initial attention on easing the patient’s labored breathing by elevating the head of the bed and initiating oxygen treatment; he then hooks the patient up to a heart monitor. Tom continues to assess the patient’s breathing status as he performs a head-to-toe assessment of the patient that leads to the nursing diagnoses and additional interventions necessary to provide comprehensive care to this patient.

Consider Tom’s actions and how and why he intervened as he did. Tom relied on the immediate data and information that he

acquired during his initial rapid assessment to deliver appropriate care to his patient. Tom also used technology (a pulse oximeter and a heart monitor) to assist with and support the delivery of care. What is not immediately apparent, and some would argue is transparent (done without conscious thought), is the fact that during the rapid assessment, Tom reached into his knowledge base of previous learning and experiences to direct his care, so that he could act with transparent wisdom. He used both nursing theory and borrowed theory to inform his practice. Tom certainly used nursing process theory, and he may have also used one of several other nursing theories, such as Rogers’s science of unitary human beings, Orem’s theory of self-care deficit, or Roy’s adaptation theory. In addition, Tom may have applied his knowledge from some of the basic sciences, such as anatomy, physiology, psychology, and chemistry, as he determined the patient’s immediate needs. Information from Maslow’s hierarchy of needs, Lazarus’s transaction model of stress and coping, and the health belief model may have also helped Tom practice professional nursing. He gathered data, and then analyzed and interpreted those data to form a conclusion—the essence of science. Tom has illustrated the practical aspects of nursing science.

The American Nurses Association (2003) defines nursing in this way: “Nursing is the protection, promotion, and optimization of health and abilities, prevention of illness and injury, alleviation of suffering through the diagnosis and treatment of human response, and advocacy in the care of individuals, families, communities, and populations” (p. 6). Thus the focus of nursing is on human responses to actual or potential health problems and advocacy for various clients. These human responses are varied and may change over time in a single case. Nurses must possess the technical skills to manage equipment and perform procedures, the interpersonal skills to interact appropriately with people, and the cognitive skills to observe, recognize, and collect data; analyze and interpret data; and reach a reasonable conclusion that forms the basis of a decision. At the heart of all of these skills lies the management of data and information. This definition of nursing science focuses on the ethical application of knowledge acquired through education, research, and practice to provide services and interventions to patients to maintain, enhance, or restore their health and to acquire, process, generate, and disseminate nursing knowledge to advance the nursing profession.

Nursing is an information-intensive profession. The steps of using information, applying knowledge to a problem, and acting with wisdom form the basis of nursing practice science. Information is composed of data that were processed using knowledge. For information to be valuable, it must be accessible, accurate, timely, complete, cost-effective, flexible, reliable, relevant, simple, verifiable, and secure. Knowledge is the awareness and understanding of a set of information and ways that information can be made useful to support a specific task or arrive at a decision. In the case scenario, Tom used accessible, accurate, timely, relevant, and verifiable data and information. He compared that data and information to his knowledge base and previous experiences to determine which data and information were relevant to the current case. By applying his previous knowledge to data, he converted those data into information, and information into new knowledge—that is, an understanding of which nursing interventions were appropriate in this case. Thus information is data made functional through the application of knowledge.

Humans acquire data and information in bits and pieces and then transform the information into knowledge. The information- processing functions of the brain are frequently compared to those of a computer, and vice versa (an idea discussed further in the Introduction to Cognitive Science and Cognitive Informatics chapter). Humans can be thought of as organic information systems that are constantly acquiring, processing, and generating information or knowledge in their professional and personal lives. They have an amazing ability to manage knowledge. This ability is learned and honed from birth as individuals make their way through life interacting with the environment and being inundated with data and information. Each person experiences the environment and learns by acquiring, processing, generating, and disseminating knowledge.

Tom, for example, acquired knowledge in his basic nursing education program and continues to build his foundation of knowledge by engaging in such activities as reading nursing research and theory articles, attending continuing education programs, consulting with expert colleagues, and using clinical databases and clinical practice guidelines. As he interacts in the environment, he acquires knowledge that must be processed. This processing effort causes him to redefine and restructure his knowledge base and generate new knowledge. Tom can then share (disseminate) this new knowledge with colleagues, and he may receive feedback on the knowledge that he shares. This dissemination and feedback builds the knowledge foundation anew as Tom acquires, processes, generates, and disseminates new knowledge as a result of his interactions. As others respond to his knowledge dissemination and he acquires yet more knowledge, he is engaged to rethink, reflect on, and reexplore his knowledge acquisition, leading to further processing, generating, and then disseminating knowledge. This ongoing process is captured in the Foundation of Knowledge model, which is used as an organizing framework for this text.

At its base, the model contains bits, bytes (computer terms for chunks of information), data, and information in a random representation (Figure 1-1). Growing out of the base are separate cones of light that expand as they reflect upward; these cones represent knowledge acquisition, knowledge generation, and knowledge dissemination. At the intersection of the cones and forming a new cone is knowledge processing. Encircling and cutting through the knowledge cones is feedback that acts on and may transform any or all aspects of knowledge represented by the cones. One should imagine the model as a dynamic figure in which the cones of light and the feedback rotate and interact rather than remain static. Knowledge acquisition, knowledge generation, knowledge dissemination, knowledge processing, and feedback are constantly evolving for nurse scientists. The transparent effect of the cones is deliberate and is intended to suggest that as knowledge grows and expands its use becomes more transparent—a person uses this knowledge during practice without even being consciously aware of which aspect of knowledge is being used at any given moment.

Experienced nurses, thinking back to their novice years, may recall feeling like their head was filled with bits of data and information that did not form any type of cohesive whole. As the model depicts, the processing of knowledge begins a bit later (imagine a timeline applied vertically) with early experiences on the bottom and expertise growing as the processing of knowledge ensues. Early on in nurses’ education, conscious attention is focused mainly on knowledge acquisition, and they depend on their instructors and others to process, generate, and disseminate knowledge. As nurses become more comfortable with the science of nursing, they begin to take over some of the other Foundation of Knowledge functions. However, to keep up with the explosion of information in nursing and health care, they must continue to rely on the knowledge generation of nursing theorists and researchers and the dissemination of their work. In this sense, nurses are committed to lifelong learning and the use of knowledge in the practice of nursing science.

Figure 1-1 Foundation of Knowledge Model Source: Designed by Alicia Mastrian.

The Foundation of Knowledge model permeates this text, reflecting the understanding that knowledge is a powerful tool and that nurses focus on information as a key building block of knowledge. The application of the model is described in each section of the text to help the reader understand and appreciate the foundation of knowledge in nursing science and see how it applies to nursing informatics. All of the various nursing roles (practice, administration, education, research, and informatics) involve the science of nursing. Nurses are knowledge workers, working with information and generating information and knowledge as a product. They are knowledge acquirers, providing convenient and efficient means of capturing and storing knowledge. They are knowledge users, meaning individuals or groups who benefit from valuable, viable knowledge. Nurses are knowledge engineers, designing, developing, implementing, and maintaining knowledge. They are knowledge managers, capturing and processing collective expertise and distributing it where it can create the largest benefit. Finally, they are knowledge developers and generators, changing and evolving knowledge based on the tasks at hand and the information available.

In the case scenario, at first glance one might label Tom as a knowledge worker, a knowledge acquirer, and a knowledge user. However, stopping here might sell Tom short in his practice of nursing science. Although he acquired and used knowledge to help him achieve his work, he also processed the data and information he collected to develop a nursing diagnosis and a plan of care. The knowledge stores Tom used to develop and glean knowledge from valuable information are generative (having the ability to originate and produce or generate) in nature. For example, Tom may have learned something new about his patient’s culture from the patient or his wife that he will file away in the knowledge repository of his mind to be used in another similar situation. As he compares this new cultural information to what he already knows, he may gain insight into the effect of culture on a patient’s response to illness. In this sense, Tom is a knowledge generator. If he shares this newly acquired knowledge with another practitioner, and as he records his observations and his conclusions, he is then disseminating knowledge. Tom also uses feedback from the various technologies he has applied to monitor his patient’s status. In addition, he may rely on feedback from laboratory reports or even other practitioners to help him rethink, revise, and apply the knowledge about this patient that he is generating.

To have ongoing value, knowledge must be viable. Knowledge viability refers to applications (most technology based) that offer easily accessible, accurate, and timely information obtained from a variety of resources and methods and presented in a manner so as to provide the necessary elements to generate new knowledge. In the case scenario, Tom may have felt the need to consult an electronic database or a clinical guidelines repository that he has downloaded on his personal digital assistant (PDA) or that reside in the emergency room’s networked computer system to assist him in the development of a comprehensive care plan for his patient. In this way, Tom uses technology and evidence to support and inform his practice. It is also possible in this scenario that an alert might appear in the patient’s electronic health record or the clinical information system (CIS) reminding Tom to ask about influenza and pneumonia vaccines. Clinical information technologies that support and inform nursing practice and nursing administration are an important part of nursing informatics and are covered in detail in the Nursing Informatics Administrative Applications: Precare and Care Support and Nursing Informatics Practice Applications: Care Delivery sections of this text. Technologies that support and inform nursing education and nursing research are covered in the Education Applications and Research Applications of Nursing Informatics sections respectively.

This text provides a framework that embraces knowledge so that readers can develop the wisdom necessary to apply what they have learned. Wisdom is the application of knowledge to an appropriate situation. In the practice of nursing science, one expects actions to be directed by wisdom. Wisdom uses knowledge and experience to heighten common sense and insight to exercise sound judgment in practical matters. It is developed through knowledge, experience, insight, and reflection. Wisdom is sometimes thought of as the highest form of common sense, resulting from accumulated knowledge or erudition (deep, thorough learning) or enlightenment (education that results in understanding and the dissemination of knowledge). It is the ability to apply valuable and viable knowledge, experience, understanding, and insight while being prudent and sensible. Knowledge and wisdom are not synonymous: Knowledge abounds with others’ thoughts and information, whereas wisdom is focused on one’s own mind and the synthesis of experience, insight, understanding, and knowledge. Wisdom has been called the foundation of the art of nursing.

Some nursing roles might be viewed as more focused on some aspects rather than other aspects of the foundation of knowledge. For example, some might argue that nurse educators are primarily knowledge disseminators and that nurse researchers are knowledge

generators. Although the more frequent output of their efforts can certainly be viewed in this way, it is important to realize that nurses use all of the aspects of the Foundation of Knowledge model regardless of their area of practice. For nurse educators to be effective, they must be in the habit of constantly building and rebuilding their foundation of knowledge about nursing science. In addition, as they develop and implement curricular innovations, they must evaluate the effectiveness of those changes. In some cases, they use formal research techniques to achieve this goal and, therefore, generate knowledge about the best and most effective teaching strategies. Similarly, nurse researchers must acquire and process new knowledge as they design and conduct their research studies. All nurses have the opportunity to be involved in the formal dissemination of knowledge via their participation in professional conferences, either as presenters or as attendees. In addition, some nurses disseminate knowledge by formal publication of their ideas. In the cases of conference presentation and publication, nurses may receive feedback that stimulates rethinking about the knowledge they have generated and disseminated, in turn prompting them to acquire and process data and information anew.

All nurses, regardless of their practice arena, must use informatics and technology to inform and support that practice. The case scenario discussed Tom’s use of various monitoring devices that provide feedback on the physiologic status of the patient. It was also suggested that Tom might consult a clinical database or nursing practice guidelines residing on a PDA or a clinical agency network as he develops an appropriate plan of action for his nursing interventions. Perhaps the CIS in the agency supports the collection of data about patients in a relational database, providing an opportunity for data mining by nursing administrators or nurse researchers. In this way, administrators and researchers can glean information about best practices and determine which improvements are necessary to deliver the best and most effective nursing care (Swan, Lang, & McGinley, 2004).

The future of nursing science and nursing informatics is closely associated with nursing education and nursing research. Skiba (2007) suggests that techno-savvy and well-informed faculty who can demonstrate the appropriate use of technologies to enhance the delivery of nursing care are needed. Along those lines, Greenfield (2007) conducted research among nursing students to determine the effectiveness of PDA technology applied to medication administration. Her study makes a good case for incorporating such technology into nursing curricula. Girard (2007) discussed cutting-edge operating room technologies, such as nanosurgery using nanorobots, smart fabrics that aid in patient assessment during surgery, biopharmacy techniques for the safe and effective delivery of anesthesia, and virtual reality training. She makes an extremely provocative point about nursing education: “Educators will need to expand their knowledge and teach for the future and not the past. They must take heed that the old tried-and-true nursing education methods and curriculum that has lasted 100 years will have to change, and that change will be mandated for all areas of nursing” (p. 353). Bassendowski (2007) specifically addresses the potential for the generation of knowledge in educational endeavors as faculty apply new technologies to teaching and the focus shifts away from individual to group instruction that promotes sharing and processing of knowledge.

Several key national groups are promoting the inclusion of informatics content in nursing education programs. These initiatives include a proposal by the National League for Nursing (NLN, 2008); recommendations in the Quality and Safety Education for Nurses (Cronenwett et al., 2007) report; the Technology Informatics Guiding Education Reform (TIGER) Initiative (2007); and a plan by the American Association of Colleges of Nursing (AACN, 2008).

The NLN’s (2008) position statement, Preparing the Next Generation of Nurses to Practice in a Technology-Rich Environment: An Informatics Agenda, challenges nurse educators to prepare informatics-competent nurses who can practice safely in a technology- rich healthcare environment.

In the Quality and Safety Education for Nurses (2007) report, Cronenwett and colleagues identified several core competencies for nursing education. One competency specifically addressed nursing informatics: “Use information and technology to communicate, manage knowledge, mitigate error, and support decision-making” (p. 129). Another addressed the appropriate use of data and information in nursing practice to promote quality improvement: “Use data to monitor the outcomes and processes and use improvement methods to design and test changes to continuously improve the quality and safety of health care systems” (p. 127).

The TIGER (2007) initiative identifies a key purpose: “to create a vision for the future of nursing that bridges the quality chasm with information technology, enabling nurses to use informatics in practice and education to provide safer, higher-quality patient care” (p. 4). The pillars of the TIGER vision include the following:

Management and Leadership: Revolutionary leadership that drives, empowers, and executes the transformation of health care. Education: Collaborative learning communities that maximize the possibilities of technology toward knowledge development

and dissemination, driving rapid deployment and implementation of best practices. Communication and Collaboration: Standardized, person-centered, technology-enabled processes to facilitate teamwork and

relationships across the continuum of care. Informatics Design: Evidence-based, interoperable intelligence systems that support education and practice to foster quality

care and safety. Information Technology: Smart, people-centered, affordable technologies that are universal, useable, useful, and standards

based. Policy: Consistent, incentives-based initiatives (organizational and governmental) that support advocacy and coalition-building,

achieving and resourcing an ethical culture of safety. Culture: A respectful, open system that leverages technology and informatics across multiple disciplines in an environment

where all stakeholders trust each other to work together toward the goal of high quality and safety (p. 4).

The Essentials of Baccalaureate Education for Professional Nursing Practice (AACN, 2008, pp. 18–19) includes the following technology-related outcomes for baccalaureate nursing graduates:

1. Demonstrate skills in using patient care technologies, information systems, and communication devices that support safe nursing practice.

2. Use telecommunication technologies to assist in effective communication in a variety of healthcare settings. 3. Apply safeguards and decision-making support tools embedded in patient care technologies and information systems to support

a safe practice environment for both patients and healthcare workers. 4. Understand the use of CIS to document interventions related to achieving nurse-sensitive outcomes. 5. Use standardized terminology in a care environment that reflects nursing’s unique contribution to patient outcomes. 6. Evaluate data from all relevant sources, including technology, to inform the delivery of care. 7. Recognize the role of information technology in improving patient care outcomes and creating a safe care environment. 8. Uphold ethical standards related to data security, regulatory requirements, confidentiality, and clients’ right to privacy. 9. Apply patient care technologies as appropriate to address the needs of a diverse patient population.

10. Advocate for the use of new patient care technologies for safe, quality care. 11. Recognize that redesign of workflow and care processes should precede implementation of care technology to facilitate

nursing practice. 12. Participate in the evaluation of information systems in practice settings through policy and procedure development.

The report suggests the following sample content for achieving these student outcomes (AACN, 2008, pp. 19–20):

Use of patient care technologies (e.g., monitors, pumps, computer-assisted devices) Use of technology and information systems for clinical decision making Computer skills that may include basic software, spreadsheet, and healthcare databases Information management for patient safety Regulatory requirements through electronic data-monitoring systems Ethical and legal issues related to the use of information technology, including copyright, privacy, and confidentiality issues Retrieval information systems, including access, evaluation of data, and application of relevant data to patient care Online literature searches Technological resources for evidence-based practice Web-based learning and online literature searches for self and patient use Technology and information systems safeguards (e.g., patient monitoring, equipment, patient identification systems, drug alerts

and IV systems, and bar coding) Interstate practice regulations (e.g., licensure, telehealth) Technology for virtual care delivery and monitoring Principles related to nursing workload measurement and resources and information systems Information literacy Electronic health record and physician order entry Decision support tools Role of the nurse informaticist in the context of health informatics and information systems

The Informatics and Healthcare Technologies Essentials of Master’s Education in Nursing includes the following elements:

Essential V: Informatics and Healthcare Technologies

Rationale Informatics and healthcare technologies encompass five broad areas:

Use of patient care and other technologies to deliver and enhance care Communication technologies to integrate and coordinate care Data management to analyze and improve outcomes of care Health information management for evidence-based care and health education Facilitation and use of electronic health records to improve patient care (AACN, 2011, pp. 17–18).

Quality and Safety Education for Nurses As nursing science evolves, it is critical that patient care improves. Sometimes, unfortunately, patient care is less than adequate and is unsafe. Therefore, quality and safety have become paramount. The Quality and Safety Education for Nurses (QSEN) Institute project seeks to prepare future nurses who will have the knowledge, skills, and attitudes (KSAs) necessary to continuously improve the quality and safety of the healthcare systems within which they work.

Prelicensure informatics KSAs include the following (QSEN Institute, n.d.b):

INFORMATICS Knowledge Skills Attitudes

Explain why information and technology skills are essential for safe patient care

Seek education about how information is managed in care settings before providing care

Apply technology and information management tools to support safe

Appreciate the necessity for all health professionals to seek lifelong, continuous learning of information technology skills

processes of care

Identify essential information that must be available in a common database to support patient care

Contrast benefits and limitations of different communication technologies and their impact on safety and quality

Navigate the electronic health record

Document and plan patient care in an electronic health record

Employ communication technologies to coordinate care for patients

Value technologies that support clinical decision making, error prevention, and care coordination

Protect the confidentiality of protected health information in electronic health records

Describe examples of how technology and information management are related to the quality and safety of patient care

Recognize the time, effort, and skill required for computers, databases, and other technologies to become reliable and effective tools for patient care

Respond appropriately to clinical decision-making supports and alerts

Use information management tools to monitor the outcomes of care processes

Value nurses’ involvement in design, selection, implementation, and evaluation of information technologies to support patient care

Use high-quality electronic sources of health-care information

Definition: Use information and technology to communicate, manage knowledge, mitigate error, and support decision making.

Reprinted from Nursing Outlook, 55(3), Cronenwett, L., Sherwood, G., Barnsteiner J., Disch, J., Johnson, J., Mitchell, P., Sullivan, D., Warren, J., Quality and safety education for nurses, pages 122–131. copyright 2007, with permission from Elsevier.

Graduate-level informatics KSAs include the following (QSEN Institute, n.d.a):

INFORMATICS Knowledge Skills Attitudes

Contrast benefits and limitations of common information technology strategies used in the delivery of patient care

Evaluate the strengths and weaknesses of information systems used in patient care

Participate in the selection, design, implementation, and evaluation of information systems

Communicate the integral role of information technology in nurses’ work

Model behaviors that support the implementation and appropriate use of electronic health records

Assist team members to adopt information technology by piloting and evaluating proposed technologies

Value the use of information and communication technologies in patient care

Formulate essential information that must be available in a common database to support patient care in the practice specialty

Evaluate benefits and limitations of different communication technologies and their impact on safety and quality

Promote access to patient care information for all professionals who provide care to patients

Serve as a resource for how to document nursing care at basic and advanced levels

Develop safeguards for protected health information

Champion communication technologies that support clinical decision making, error prevention, care coordination, and protection of patient privacy

Appreciate the need for consensus and collaboration in developing systems to manage information for patient care

Value the confidentiality and security of all patient records

Describe and critique taxonomic and terminology systems used in national efforts to enhance interoperability of information systems and knowledge management systems

Access and evaluate high-quality electronic sources of healthcare information

Participate in the design of clinical decision-making supports and alerts

Search, retrieve, and manage data to make decisions using information and knowledge management systems

Anticipate unintended consequences of new technology

Value the importance of standardized terminologies in conducting searches for patient information

Appreciate the contribution of technological alert systems

Appreciate the time, effort, and skill required for computers, databases, and other technologies to become reliable and effective tools for patient care

Definition: Use information and technology to communicate, manage knowledge, mitigate error, and support decision making.

Reprinted from Nursing Outlook, 57(6), Cronenwett, L., Sherwood, G., Pohl, J., Barnsteiner, J., Moore, S., Sullivan, D., Ward, D., Warren, J.,

Quality and safety education for advanced nursing practice, pages 338–348, copyright 2009, with permission from Elsevier.

This text is designed to include the necessary content to prepare nurses for practice in the ever-changing and technology-laden healthcare environments.

Goossen (2000) believes that the focus of nursing informatics research should be on the structuring and processing of patient information and the ways that these endeavors inform nursing decision making in clinical practice. The increased use of technology to enhance nursing practice, nursing education, and nursing research will open new avenues for acquiring, processing, generating, and disseminating knowledge.

In the future, nursing research will make significant contributions to the development of nursing science. Technologies and translational research will abound, and clinical practices will be evidence based, thereby improving patient outcomes and decreasing safety concerns. Schools of nursing will embrace nursing science as they strive to meet the needs of changing student populations and the increasing complexity of healthcare environments.

Summary Nursing science influences all areas of nursing practice. This chapter provided an overview of nursing science and considered how nursing science relates to typical nursing practice roles, nursing education, and nursing research. The Foundation of Knowledge model was introduced as the organizing conceptual framework for this text. Finally, the relationship of nursing science to nursing informatics was discussed. In subsequent chapters the reader will learn more about how nursing informatics supports nurses in their many and varied roles. In an ideal world, nurses would embrace nursing science as knowledge users, knowledge managers, knowledge developers, knowledge engineers, and knowledge workers.

THOUGHT-PROVOKING QUESTIONS

1. Imagine you are in a social situation and someone asks you, “What does a nurse do?” Think about how you will capture and convey the richness that is nursing science in your answer.

2. Choose a clinical scenario from your recent experience and analyze it using the Foundation of Knowledge model. How did you acquire knowledge? How did you process knowledge? How did you generate knowledge? How did you disseminate knowledge? How did you use feedback, and what was the effect of the feedback on the foundation of your knowledge?

References American Association of Colleges of Nursing (AACN). (2008, October 20). The essentials of baccalaureate education for professional nursing practice.

http://www.aacn.nche.edu/education-resources/baccessentials08.pdf American Association of Colleges of Nursing (AACN). (2011, March 21). The essentials of master’s education in nursing.

http://www.aacn.nche.edu/education-resources/MastersEssentials11.pdf American Nurses Association. (2003). Nursing’s social policy statement (2nd ed.). Silver Spring, MD: Author. Bassendowski, S. (2007). NursingQuest: Supporting an analysis of nursing issues. Journal of Nursing Education, 46(2), 92–95. Retrieved from

Education Module database [document ID: 1210832211]. Cronenwett, L., Sherwood, G., Barnsteiner J., Disch, J., Johnson, J., Mitchell, P., …Warren, J. (2007). Quality and safety education for nurses. Nursing

Outlook, 55(3), 122–131. Girard, N. (2007). Science fiction comes to the OR. Association of Operating Room Nurses. AORN Journal, 86(3), 351–353. Retrieved from Health Module database [document ID: 1333149261]. Goossen, W. (2000). Nursing informatics research. Nurse Researcher, 8(2), 42. Retrieved from ProQuest Nursing & Allied Health Source database

[document ID: 67258628]. Greenfield, S. (2007). Medication error reduction and the use of PDA technology. Journal of Nursing Education, 46(3), 127–131. Retrieved from

Education Module database [document ID: 1227347171]. National League for Nursing (NLN). (2008). Preparing the next generation of nurses to practice in a technology rich environment: An informatics

agenda [Position statement]. http://www.nln.org/aboutnln/PositionStatements/informatics_052808.pdf QSEN Institute. (n.d.a). Graduate KSAs. http://qsen.org/competencies/graduate-ksas/ QSEN Institute. (n.d.b). Pre-licensure KSAs. http://qsen.org/competencies/Pre-licensure-ksas/Skiba, D. (2007). Faculty 2.0: Flipping the novice to expert

continuum. Nursing Education Per- spectives, 28(6), 342–344. Retrieved from ProQuest Nursing & Allied Health Source database [document ID: 1401240241]. Swan, B., Lang, N., & McGinley, A. (2004). Access to quality health care: Links between evidence, nursing language, and informatics. Nursing

Economics, 22(6), 325–332. Retrieved from Health Module database [document ID: 768191851]. Technology Informatics Guiding Education Reform. (2007). Evidence and informatics transforming nursing: 3-year action steps toward a 10-year vision.

http://www.tigersummit.com/uploads/TIGERInitiative_Report2007_Color.pdf

Chapter 2

Introduction to Information, Information Science, and Information Systems Dee McGonigle and Kathleen Mastrian

OBJECTIVES

1. Reflect on the progression from data to information to knowledge. 2. Describe the term information. 3. Assess how information is acquired. 4. Explore the characteristics of quality information. 5. Describe an information system. 6. Explore data acquisition or input and processing or retrieval, analysis, and synthesis of data. 7. Assess output or reports, documents, summaries alerts, and outcomes. 8. Describe information dissemination and feedback. 9. Define information science. 10. Assess how information is processed. 11. Explore how knowledge is generated in information science.

Key Terms

Acquisition Alert Analysis Chief information officer Chief technical officer Chief technology officer Cloud computing Cognitive science Communication science Computer-based information system Computer science Consolidated Health Informatics Data Dissemination Document Electronic health record Federal Health Information Exchange Feedback Health information exchange Health Level Seven Indiana Health Information Exchange Information Information science Information system Information technology Input Interface Internet2 Knowledge Knowledge worker Library science Massachusetts Health Data Consortium National Health Information Infrastructure National Health Information Network New England Health EDI Network

Next-Generation Internet Outcome Output Processing Rapid Syndromic Validation Project Report Social sciences Stakeholder Summaries Synthesis Telecommunications

Introduction This chapter explores information, information systems (IS), and information science. The key word here, of course, is information. Healthcare professionals are knowledge workers, and they deal with information on a daily basis. Many concerns and issues arise with healthcare information, such as ownership, access, disclosure, exchange, security, privacy, disposal, and dissemination. With the gauntlet of developing electronic health records having been laid down, publicand private-sector stakeholders have been collaborating on a wide-ranging variety of healthcare information solutions. These initiatives include Health Level Seven (HL7), Consolidated Health Informatics’ (CHI’s) eGov initiative, the National Health Information Infrastructure (NHII), the National Health Information Network (NHIN), Next-Generation Internet (NGI), Internet2, and iHealth record. There are also health information exchange (HIE) systems, such as Connecting for Health, the eHealth initiative, the Federal Health Information Exchange (FHIE), the Indiana Health Information Exchange (IHIE), the Massachusetts Health Data Consortium (MHDC), the New England Health EDI Network (NEHEN), the State of New Mexico Rapid Syndromic Validation Project (RSVP), the Southeast Michigan e-Prescribing Initiative, and the Tennessee Volunteer eHealth Initiative (Goldstein, Groen, Ponkshe, & Wine, 2007). The most recent federal government initiative, the HITECH Act, has set 2014 as the deadline for implementing electronic health records (see the Legislative Aspects of Nursing Informatics: HITECH and HIPAA chapter).

It is quite evident from the previous brief listing that there is a need to remedy healthcare information technology concerns, challenges, and issues faced today. One of the main issues deals with how healthcare information is managed to make it meaningful. It is important to understand how people obtain, manipulate, use, share, and dispose of information. This chapter deals with the information piece of this complex puzzle.

Information Suppose someone states the number 99.5. What does that mean? It could be a radio station or a score on a test. Now suppose someone says that Ms. Howsunny’s temperature is 99.5°F—what does that convey? It is then known that 99.5 is a person’s temperature. The data (99.5) were processed to the information that 99.5° is a specific person’s temperature. Data are raw facts. Information is processed data that has meaning. Healthcare professionals constantly process data and information to provide the best care possible for their patients.

Many types of data exist, such as alphabetic, numeric, audio, image, and video data. Alphabetic data refer to letters, numeric data refer to numbers, and alphanumeric data include both letters and numbers. This includes all text and the numeric outputs of digital monitors. Some of the alphanumeric data encountered by healthcare professionals are in the form of patients’ names, identification numbers, or medical record numbers. Audio data refer to sounds, noises, or tones—for example, monitor alerts or alarms, taped or recorded messages, and other sounds. Image data include graphics and pictures, such as graphic monitor displays or recorded electrocardiograms, radiographs, magnetic resonance imaging

(MRI) outputs, and computed tomography (CT) scans. Video data refer to animations, moving pictures, or moving graphics. Using these data, one may review the ultrasound of a pregnant patient, examine a patient’s echocardiogram, watch an animated video for professional development, or learn how to operate a new technology tool, such as a pump or monitoring system.

The integrity and quality of the data, rather than the form, are what matter. Integrity refers to whole, complete, correct, and consistent data. Data integrity can be compromised through human error; viruses, worms, or other computer bugs; hardware failures or crashes; transmission errors; or hackers entering the system. Information technologies help to decrease these errors by putting into place safeguards, such as backing up files on a routine basis, error detection for transmissions, and user interfaces that help people enter the data correctly. High-quality data are relevant and accurately represent their corresponding concepts. Data are dirty when a database contains errors, such as duplicate, incomplete, or outdated records. One author (D.M.) found 50 cases of tongue cancer in a database she examined for data quality. When the records were tracked down and analyzed, and the dirty data were removed, only one case of tongue cancer remained. In this situation, the data for the same person had been entered erroneously 49 times. The major problem was with the patient’s identification number and name: The number was changed or his name was misspelled repeatedly. If researchers had just taken the number of cases in that defined population as 50, they would have concluded that tongue cancer was an epidemic, resulting in flawed information that is not meaningful. As this example demonstrates, it is imperative that data be clean if the goal is quality information. The data that are processed into information must be of high quality and integrity to create meaning to inform assessments and decision making.

To be valuable and meaningful, information must be of good quality. Its value relates directly to how the information informs decision making. Characteristics of valuable, quality information include accessibility, security, timeliness, accuracy, relevancy, completeness, flexibility, reliability, objectivity, utility, transparency, verifiability, and reproducibility.

Accessibility is a must; the right user must be able to obtain the right information at the right time and in the right format to meet his or her needs. Getting meaningful information to the right user at the right time is as vital as generating the information in the first

place. The right user refers to an authorized user who has the right to obtain the data and information he or she is seeking. Security is a major challenge because unauthorized users must be blocked while the right user is provided with open, easy access (see the Electronic Security chapter).

Timely information means that the information is available when it is needed for the right purpose and at the right time. Knowing who won the lottery last week does not help one to know if the person won it today. Accurate information means that there are no errors in the data and information. Relevant information is a subjective descriptor, in that the user must have information that is relevant or applicable to his or her needs. If a healthcare provider is trying to decide whether a patient needs insulin and only the patient’s CT scan information is available, this information is not relevant for that current need. However, if one needed information about the CT scan, then the information is relevant.

Complete information contains all of the necessary essential data. If the healthcare provider needs to contact the only relative listed for the patient and his or her contact information is listed but the approval for that person to be a contact is missing, this information is considered incomplete. Flexible information means that the information can be used for a variety of purposes. Information concerning the inventory of supplies on a nursing unit, for example, can be used by nurses who need to know if an item is available for use for a patient. The nurse manager accesses this information to help decide which supplies need to be ordered, to determine which items are used most frequently, and to do an economic assessment of any waste.

Reliable information comes from reliable or clean data and authoritative and credible sources. Objective information is as close to the truth as one can get; it is not subjective or biased, but rather is factual and impartial. If someone states something, it must be determined whether that person is reliable and whether what he or she is stating is objective or tainted by his or her own perspective.

Utility refers to the ability to provide the right information at the right time to the right person for the right purpose. Transparency allows users to apply their intellect to accomplish their tasks while the tools housing the information disappear. Verifiable information means that one can check to verify or prove that the information is correct. Reproducibility refers to the ability to produce the same information again.

Information is acquired either by actively looking for it or by having it conveyed by the environment. All of the senses (vision, hearing, touch, smell, and taste) are used to gather input from the surrounding world, and as technologies mature, more and more input will be obtained through the senses. Currently, people receive information from computers (output), through vision, hearing, or touch (input); and the response (output) to the computer (input) is the interface with technology. Gesture recognition is increasing, and interfaces that incorporate it will change the way people become informed. Many people access the Internet on a daily basis seeking information or imparting information. Individuals are constantly becoming informed, discovering, or learning; becoming reinformed, rediscovering, or relearning; and purging what has been acquired. The information acquired through these processes is added to the knowledge base. Knowledge is the awareness and understanding of a set of information and ways that information can be made useful to support a specific task or arrive at a decision. This knowledge building is an ongoing process engaged in while a person is conscious and going about his or her normal daily activities.

Information Science Information science has evolved over the last 50 some years as a field of scientific inquiry and professional practice. It can be thought of as the science of information, studying the application and usage of information and knowledge in organizations and the interface or interaction between people, organizations, and information systems (IS). This extensive, interdisciplinary science integrates features from cognitive science, communication science, computer science, library science, and social sciences. Information science is primarily concerned with the input, processing, output, and feedback of data and information through technology integration with a focus on comprehending the perspective of the stakeholders involved and then applying information technology as needed. It is systemically based, dealing with the big picture rather than individual pieces of technology.

Information science can also be related to determinism. Specifically, it is a response to technologic determinism—the belief that technology develops by its own laws, that it realizes its own potential, limited only by the material resources available, and must therefore be regarded as an autonomous system controlling and ultimately permeating all other subsystems of society (Web Dictionary of Cybernetics and Systems, 2007, para. 1).

This approach sets the tone for the study of information as it applies to itself, the people, the technology, and the varied sciences that are contextually related depending on the needs of the setting or organization; what is important is the interface between the stakeholders and their systems, and the ways they generate, use, and locate information. According to Cornell University (2010), “Information Science brings together faculty, students and researchers who share an interest in combining computer science with the social sciences of how people and society interact with information” (para. 1). Information science is an interdisciplinary, people- oriented field that explores and enhances the interchange of information to transform society, communication science, computer science, cognitive science, library science, and the social sciences. Society is dominated by the need for information, and knowledge and information science focuses on systems and individual users by fostering user-centered approaches that enhance society’s information capabilities, effectively and efficiently linking people, information, and technology. This impacts the configuration and mix of organizations and influences the nature of work—namely, how knowledge workers interact with and produce meaningful information and knowledge.

Information Processing Claude E. Shannon, who is considered the father of information theory (Horgan, 1990), thought of information processing as the conversion of latent information into manifest information. Latent information is that which is not yet realized or apparent, whereas manifest information is obvious or clearly apparent. According to O’Connor and Robertson (2005), “Shannon believed that information was no different than any other quantity and therefore could be manipulated by a machine” (para. 13).

Information science enables the processing of information. This processing links people and technology. Humans are organic ISs,

constantly acquiring, processing, and generating information or knowledge in their professional and personal lives. This high degree of knowledge, in fact, characterizes humans as extremely intelligent organic machines. The premise of this text revolves around this concept, and the text is organized on the basis of the Foundation of Knowledge model: knowledge acquisition, knowledge processing, knowledge generation, and knowledge dissemination.

Information is data that are processed using knowledge. For information to be valuable or meaningful, it must be accessible, accurate, timely, complete, cost-effective, flexible, reliable, relevant, simple, verifiable, and secure. Knowledge is the awareness and understanding of an information set and ways that information can be made useful to support a specific task or arrive at a decision. As an example, if an architect were going to design a building, part of the knowledge necessary for developing a new building is understanding how the building will be used, what size of building is needed compared to the available building space, and how many people will have or need access to this building. Therefore, the work of choosing or rejecting facts based on their significance or relevance to a particular task, such as designing a building, is also based on a type of knowledge used in the process of converting data into information. Information can then be considered data made functional through the application of knowledge. The knowledge used to develop and glean knowledge from valuable information is generative (having the ability to originate and produce or generate) in nature. Knowledge must also be viable. Knowledge viability refers to applications that offer easily accessible, accurate, and timely information obtained from a variety of resources and methods and presented in a manner so as to provide the necessary elements to generate knowledge.

Information science and computational tools are extremely important in enabling the processing of data, information, and knowledge in health care. In this environment, the hardware, software, networking, algorithms, and human organic ISs work together to create meaningful information and generate knowledge. The links between information processing and scientific discovery are paramount. However, without the ability to generate practical results that can be disseminated, the processing of data, information, and knowledge is for naught. It is the ability of machines (inorganic ISs) to support and facilitate the functioning of people (human organic ISs) that refines, enhances, and evolves nursing practice by generating knowledge. This knowledge represents five rights: the right information, accessible by the right people in the right settings, applied the right way at the right time.

An important and ongoing process is the struggle to integrate new knowledge and old knowledge so as to enhance wisdom. Wisdom is the ability to act appropriately; it assumes actions directed by one’s own wisdom. Wisdom uses knowledge and experience to heighten common sense, and insight to exercise sound judgment in practical matters. It is developed through knowledge, experience, insight, and reflection. Wisdom is sometimes thought of as the highest form of common sense, resulting from accumulated knowledge or erudition (deep, thorough learning) or enlightenment (education that results in understanding and the dissemination of knowledge). It is the ability to apply valuable and viable knowledge, experience, understanding, and insight while being prudent and sensible. Knowledge and wisdom are not synonymous, because knowledge abounds with others’ thoughts and information, whereas wisdom is focused on one’s own mind and the synthesis of one’s own experience, insight, understanding, and knowledge.

If clinicians are inundated with data without the ability to process it, the situation results in too much data and too little wisdom. Consequently, it is crucial that clinicians have viable ISs at their fingertips to facilitate the acquisition, sharing, and use of knowledge while maturing wisdom; this process leads to empowerment.

Information Science and the Foundation of Knowledge Information science is a multidisciplinary science that encompasses aspects of computer science, cognitive science, social science, communication science, and library science to deal with obtaining, gathering, organizing, manipulating, managing, storing, retrieving, recapturing, disposing of, distributing, and broadcasting information. Information science studies everything that deals with information and can be defined as the study of ISs. This science originated as a subdiscipline of computer science, as practitioners sought to understand and rationalize the management of technology within organizations. It has since matured into a major field of management and is now an important area of research in management studies. Moreover, information science has expanded its scope to examine the human–computer interaction, interfacing, and interaction of people, ISs, and corporations. It is taught at all major universities and business schools worldwide.

Modern-day organizations have become intensely aware of the fact that information and knowledge are potent resources that must be cultivated and honed to meet their needs. Thus information science or the study of ISs—that is, the application and usage of knowledge—focuses on why and how technology can be put to best use to serve the information flow within an organization.

Information science impacts information interfaces, influencing how people interact with information and subsequently develop and use knowledge. The information a person acquires is added to his or her knowledge base. Knowledge is the awareness and understanding of an information set and ways that information can be made useful to support a specific task or arrive at a decision.

Healthcare organizations are affected by and rely on the evolution of information science to enhance the recording and processing of routine and intimate information while facilitating human-to-human and human-to-systems communications, delivery of healthcare products, dissemination of information, and enhancement of the organization’s business transactions. Unfortunately, the benefits and enhancements of information science technologies have also brought to light new risks, such as glitches and loss of information and hackers who can steal identities and information. Solid leadership, guidance, and vision are vital to the maintenance of cost-effective business performance and cuttingedge, safe information technologies for the organization. This field studies all facets of the building and use of information. The emergence of information science and its impact on information have also influenced how people acquire and use knowledge.

Information science has already had a tremendous impact on society and will undoubtedly expand its sphere of influence further as it continues to evolve and innovate human activities at all levels. What visionaries only dreamed of is now possible and part of reality. The future has yet to fully unfold in this important arena.

Introduction to Information Systems

Consider the following scenario: You have just been hired by a large healthcare facility. You enter the personnel office and are told that you must learn a new language to work on the unit where you have been assigned. This language is used just on this unit. If you had been assigned to a different unit, you would have to learn another language that is specific to that unit, and so on. Because of the differences in various units’ languages, interdepartmental sharing and information exchange (known as interoperability) are severely hindered.

This scenario might seem far-fetched, but it is actually how workers once operated in health care—in silos. There was a system for the laboratory, one for finance, one for clinical departments, and so on. As healthcare organizations have come to appreciate the importance of communication, tracking, and research, however, they have developed integrated information systems that can handle the needs of the entire organization.

Information and information technology have become major resources for all types of organizations, and health care is no exception (see Box 2-1). Information technologies help to shape a healthcare organization, in conjunction with personnel, money, materials, and equipment. Many healthcare facilities have hired chief information officers (CIOs) or chief technical officers (CTOs), also known as chief technology officers. The CIO is involved with the information technology infrastructure, and this role is sometimes expanded to include the position of chief knowledge officer. The CTO is focused on organizationally based scientific and technical issues and is responsible for technological research and development as part of the organization’s products and services. The CTO and CIO must be visionary leaders for the organization, because so much of the business of health care relies on solid infrastructures that generate potent and timely information and knowledge. The CTO and CIO are sometimes interchangeable positions, but in some organizations the CTO reports to the CIO. These positions will become critical roles as companies continue to shift from being product oriented to knowledge oriented, and as they begin emphasizing the production process itself rather than the product. In health care, ISs must be able to handle the volume of data and information necessary to generate the needed information and knowledge for best practices, because the goal is to provide the highest quality of patient care.

Information Systems ISs can be manually based, but for the purposes of this text, the term refers to computer-based information systems (CBISs). According to Jessup and Valacich (2008), computer-based ISs “are combinations of hardware, software and telecommunications networks that people build and use to collect, create, and distribute useful data, typically in organizational settings” (p. 10). Along the same lines, ISs are also defined as “a set of interrelated components that collect, manipulate, store and disseminate data and information and provide a feedback mechanism to meet an objective” (Stair & Reynolds, 2008, p. 4). ISs are designed for specific purposes within organizations. They are only as functional as the decision-making capabilities, problem-solving skills, and programming potency built in and the quality of the data and information input into them (see the Systems Development Life Cycle: Nursing Informatics and Organizational Decision Making chapter). The capability of the IS to disseminate, provide feedback, and adjust the data and information based on these dynamic processes is what sets them apart. The IS should be a user-friendly entity that provides the right information at the right time and in the right place.

BOX 2-1 EXAMPLES OF INFORMATION SYSTEMS

Information System How It Is Used

Clinical Information System (CIS)

Comprehensive and integrative system that manages the administrative, financial, and clinical aspects of a clinical facility; a CIS should help to link financial and clinical outcomes. An example is the electronic health record (EHR).

Decision Support System (DSS)

Organizes and analyzes information to help decision makers formulate decisions when they are unsure of their decision’s possible outcomes. After gathering relevant and useful information, develops “what if” models to analyze the options or choices and alternatives.

Executive Support System Collects, organizes, analyzes, and summarizes vital information to help executives or senior management with strategic decision making. Provides a quick view of all strategic business activities.

Geographic Information System (GIS) Collects, manipulates, analyzes, and generates information related to geographic locations or the surface of the earth; provides output in the form of virtual models, maps, or lists.

Management Information Systems (MIS) Provides summaries of internal sources of information, such as information from the transaction processing system, and develops a series of routine reports for decision making.

Office Systems Facilitates communication and enhances the productivity of users needing toprocess data and information.

Transaction Processing System (TPS) Processes and records routine business transactions, such as billing systems that create and send invoices to customers, and payroll systems that generate employees’ pay stubs and wage checks and calculate tax payments.

Hospital Information System (HIS) Manages the administrative, financial, and clinical aspects of a hospitalenterprise. It should help to link financial and clinical outcomes.

An IS acquires data or inputs; processes data through the retrieval, analysis, or synthesis of those data; disseminates or outputs information in the form of reports, documents, summaries, alerts, prompts, or outcomes; and provides for responses or feedback. Input or data acquisition is the activity of collecting and acquiring raw data. Input devices include combinations of hardware, software, and telecommunications and include keyboards, light pens, touch screens, mice or other pointing devices, automatic scanners, and machines that can read magnetic ink characters or lettering. To watch a pay-per-view movie, for example, the viewer must first input the chosen movie, verify the purchase, and have a payment method approved by the vendor. The IS must acquire this information before the viewer can receive the movie.

Processing—the retrieval, analysis, or synthesis of data—refers to the alteration and transformation of the data into helpful or useful information and outputs. The processing of data can range from storing it for future use, to comparing the data, making calculations, or applying formulas, to taking selective actions. Processing devices consist of combinations of hardware, software, and telecommunications and include processing chips where the central processing unit (CPU) and main memory are housed. Some of these chips are quite ingenious. According to Schupak (2005), the bunny chip could save the pharmaceutical industry money while sparing “millions of furry creatures, with a chip that mimics a living organism” (para. 1). The HµREL Corporation has developed environments or biologic ISs that reside on chips and actually mimic the functioning of the human body. Researchers can use these environments to test for both the harmful and beneficial effects of drugs, including those that are considered experimental and that could be harmful if used in human and animal testing. Such chips also allow researchers to monitor a drug’s toxicity in the liver and other organs.

One patented HµREL microfluidic “biochip” comprises an arrangement of separate but fluidically interconnected “organ” or “tissue” compartments. Each compartment contains a culture of living cells drawn from, or engineered to mimic the primary functions of the respective organ or tissue of a living animal. Microfluidic channels permit a culture medium that serves as a “blood surrogate” to recirculate just as in a living system, driven by a microfluidic pump. The geometry and fluidics of the device are fashioned to simulate the values of certain related physiologic parameters found in the living creature. Drug candidates or other substrates of interest are added to the culture medium and allowed to recirculate through the device. The effects of drug compounds and their metabolites on the cells within each respective organ compartment are then detected by measuring or monitoring key physiologic events. The cell types used may be derived from either standard cell culture lines or primary tissues (HµREL Corporation, 2010, para. 2–3). As new technologies such as the HµREL chips continue to evolve, more and more robust ISs that can handle a variety of biological and clinical applications will be seen.

Returning to the movie rental example, the IS must verify the data entered by the viewer and then process the request by following the steps necessary to provide access to the movie that was ordered. This processing must be instantaneous in today’s world, where everyone wants everything now. After the data are processed, they are stored. In this case, the rental must also be processed so the vendor receives payment for the movie, whether electronically, via a credit card or checking account withdrawal, or by generating a bill for payment.

Output or dissemination produces helpful or useful information that can be in the form of reports, documents, summaries, alerts, or outcomes. Reports are designed to inform and are generally tailored to the context of a given situation or user or user group. Reports may include charts, figures, tables, graphics, pictures, hyperlinks, references, or other documentation necessary to meet the needs of the user. Documents represent information that can be printed, saved, e-mailed or otherwise shared, or displayed. Summaries are condensed versions of the original designed to highlight the major points. Alerts are warnings, feedback, or additional information necessary to assist the user in interacting with the system. Outcomes are the expected results of input and processing. Output devices are combinations of hardware, software, and telecommunications and include sound and speech synthesis outputs, printers, and monitors.

Continuing with the movie rental example, the IS must be able to provide the consumer with the movie ordered when it is wanted and somehow notify the purchaser that he or she has, indeed, purchased the movie and is granted access. The IS must also be able to generate payment either electronically or by generating a bill, while storing the transactional record for future use.

Feedback or responses are reactions to the inputting, processing, and outputs. In ISs, feedback refers to information from the system that is used to make modifications in the input, processing actions, or outputs. In the movie rental example, what if the consumer accidentally entered the same movie order three times, but really wanted to order the movie only once? The IS would determine that more than one movie order is out of range for the same movie order at the same time and provide feedback. Such feedback is used to verify and correct the input. If undetected, the viewer’s error would result in an erroneous bill and decreased customer satisfaction while creating more work for the vendor, which would have to engage in additional transactions with the customer to resolve this problem. The Nursing Informatics Practice Applications: Care Delivery section of this text provides detailed descriptions of clinical ISs that operate on these same principles to support healthcare delivery.

Summary Information systems deal with the development, use, and management of an organization’s information technology (IT) infrastructure. An IS acquires data or inputs; processes data through the retrieval, analysis, or synthesis of those data; disseminates or outputs in the form of reports, documents, summaries, alerts, or outcomes; and provides for responses or feedback. Quality decision-making and problem-solving skills are vital to the development of effective, valuable ISs. Today’s organizations now recognize that their most precious asset is their information, as represented by their employees, experience, competence or know-how, and innovative or novel approaches, all of which are dependent on a robust information network that encompasses the information technology infrastructure.

In an ideal world, all ISs would be fluid in their ability to adapt to any and all users’ needs. They would be Internet oriented and global, where resources are available to everyone. Think of cloud computing—it is just the beginning point from which ISs will expand and grow in their ability to provide meaningful information to their users. As technologies advance, so will the skills and capabilities to comprehend and realize what ISs can become.

It is important to continue to develop and refine functional, robust, visionary ISs that meet the current meaningful information needs while evolving systems that are even better prepared to handle future information and knowledge needs of the healthcare

industry.

THOUGHT-PROVOKING QUESTIONS

1. How do you acquire information? Choose 2 hours out of your busy day and try to notice all of the information that you receive from your environment. Keep diaries indicating where the information came from and how you knew it was information and not data.

2. Reflect on an IS with which you are familiar, such as the automatic banking machine. How does this IS function? What are the advantages of using this system (i.e., why not use a bank teller instead)? What are the disadvantages? Are there enhancements that you would add to this system?

3. In health care, think about a typical day of practice and describe the setting. How many times does the nurse interact with ISs? What are the ISs that we interact with, and how do we access them? Are they at the bedside, hand-held, or station based? How do their location and ease of access impact nursing care?

4. Briefly describe an organization and discuss how our need for information and knowledge impacts the configuration and interaction of that organization with other organizations. Also discuss how the need for information and knowledge influences the nature of work or how knowledge workers interact with and produce information and knowledge in this organization.

5. If you could meet only four of the rights discussed in this chapter, which one would you omit and why? Also, provide your rationale for each right you chose to meet.

References Cornell University. (2010). Information science. http://www.infosci.cornell.edu/ Goldstein, D., Groen, P., Ponkshe, S., & Wine, M. (2007). Medical informatics 20/20. Sudbury, MA: Jones and Bartlett. Horgan, J. (1990). Claude E. Shannon: Unicyclist, juggler and father of information theory. Retrieved March 2008 from

http://www.ecs.umass.edu/ece/hill/ece221.dir/shannon.html HµREL Corporation. (2010). Human-relevant: HµREL. Technology overview. http://www.hurelcorp.com/overview.php Jessup, L., & Valacich, J. (2008). Information systems today (3rd ed.). Upper Saddle River, NJ: Pearson Prentice Hall. O’Connor, J., & Robertson, E. (2005). Claude Elwood Shannon. http://www.thocp.net/biographies/shannon_claude.htm Schupak, A. (2005). Technology: The bunny chip. http://members.forbes.com/forbes/2005/0815/053.html Stair, R., & Reynolds, G. (2008). Principles of information systems (8th ed.). Boston, MA: Thomson Course Technology. Web Dictionary of Cybernetics and Systems. (2007). Technological determinism. http://pespmc1.vub.ac.be/ASC/TECHNO_DETER.html

Chapter 3

Computer Science and the Foundation of Knowledge Model June Kaminski

OBJECTIVES

1. Describe the essential components of computer systems, including both hardware and software. 2. Recognize the rapid evolution of computer systems and the benefit of keeping up-to-date with current trends and developments. 3. Analyze how computer systems function as tools for managing information and generating knowledge. 4. Define the concept of human–technology interfaces. 5. Articulate how computers can support collaboration, networking, and information exchange.

Key Terms

Acquisition Applications Arithmetic logic unit (ALU) Basic input/output system (BIOS) Binary system Bit Bus Byte Cache memory Central processing unit (CPU) Communication software Compact disk read-only memory (CD-ROM) Compact disk-recordable (CD-R) Compact disk-rewritable (CD-RW) Compatibility Computer Computer science Conferencing software Creativity software Databases Degradation Desktop Digital video disk (DVD) Digital video disk-recordable (DVD-R) Digital video disk-rewritable (DVD-RW) Dissemination Dynamic random access memory (DRAM) E-mail E-mail client Electronically erasable programmable read-only memory (EEPROM) Exabyte (EB) Execute Extensibility FireWire Firmware Flash memory Gigabyte Gigahertz Graphical user interface Graphics card Hard disk Hard drive Hardware Information

Information Age Instant message (IM) Integrated drive electronics (IDE) Internet browser Keyboard Knowledge Laptop Main memory Mainframes Megabyte (MB) Megahertz (MHz) Memory Microprocessor Microsoft Surface Modem Monitor Motherboard Mouse MPEG-1 Audio Layer-3 (MP3) Networks Nonsynchronous Office suite Open source Operating system (OS) Palm computers Parallel port Peripheral component interconnection (PCI) Personal computer (PC) Personal digital assistant (PDA) Plug and play Port Portability Portable operating system interface for UNIX (POSIX) Power supply Presentation Processing Productivity software Professional development Programmable read-only memory (PROM) Publishing QWERTY Random-access memory (RAM) Read-only memory (ROM) Security Serial port Small Computer System Interface (SCSI) Software Sound card Spreadsheet Supercomputers Synchronous Synchronous dynamic random-access memory (SDRAM) Technology Terabyte (TB) Throughput Touch screen Universal serial bus (USB) User friendly User interface Video adapter card Virtual memory Wearable technology Wisdom Word processing World Wide Web (WWW) Yottabyte (YB) Zettabyte (ZB)

Introduction In this chapter, the discipline of computer science is introduced through a focus on computers and the hardware and software that make up these evolving systems. Computer science offers extremely valuable tools that, if used skillfully, can facilitate the acquisition and manipulation of data and information by nurses, who can then synthesize these into an evolving knowledge and wisdom base. This process can facilitate professional development and the ability to apply evidence-based practice decisions within nursing care, and if the results are disseminated and shared, can also advance the professional knowledge base.

This chapter begins with a look at common computer hardware, followed by a brief overview of operating, productivity, creativity, and communication software. It concludes with a glimpse at how computer systems help to shape knowledge and collaboration and an introduction to human–technology interface dynamics.

The Computer as a Tool for Managing Information and Generating Knowledge Throughout history, various milestones have signaled discoveries, inventions, or philosophic shifts that spurred a surge in knowledge and understanding within the human race. The advent of the computer is one such milestone, which has sparked an intellectual metamorphosis whose boundaries have yet to be fully understood. Computer technology has ushered in what has been called the Information Age, an age when data, information, and knowledge are both accessible and able to be manipulated by more people than ever before in history. How can a mere machine lead to such a revolutionary state of knowledge potential? To begin to answer this question, it is best to examine the basic structure and components of computer systems.

Essentially, a computer is an electronic information-processing machine that serves as a tool with which to manipulate data and information. The easiest way to begin to understand computers is to realize they are input–output systems. These unique machines accept data input via a variety of devices, process data through logical and arithmetic rendering, store the data in memory components, and output data and information to the user.

Since the advent of the first electronic computer in the mid-1940s, computers have evolved to become essential tools in every walk of life, including the profession of nursing. The complexity of computers has increased dramatically over the years, and will continue to do so. “Computing has changed the world more than any other invention of the past hundred years, and has come to pervade nearly all human endeavors. Yet, we are just at the beginning of the computing revolution; today’s computing offers just a glimpse of the potential impact of computers” (Evans, 2010, p. 3). Major computer manufacturers and researchers, such as Intel, have identified the need to design computers to mask this growing complexity. The sophistication of computers is evolving at amazing speed, yet ease of use or user-friendly aspects are also increasing accordingly. This is achieved by honing hardware and software capabilities until they work seamlessly together to ensure user-friendly, intuitive tools for users of all levels of expertise. Box 3-1 provides information about computing surfaces, an evolving technology.

According to Intel Corporation’s technology research team, the goal is “technology that just works.” “To conceal complexity, Intel Research is looking at a number of solutions by:

Relating user mental models with complex systems and technology to improve the use and adaptation of systems across devices and contexts.

Enabling devices to explore their environment to discover other devices and capabilities, and then form integrated ‘teams’ that self-organize for higher functionality and performance.

Better control of failure modes, graceful degradation, and self-healing across ensembles of devices. Zero-knowledge applications and interoperation.” (Intel Corporation, 2008, para. 2)

BOX 3-1 MICROSOFT SURFACE TENSION? iTABLE Dee McGonigle Do not get too attached to your mouse and keyboard, because they will be outdated soon if Microsoft and PQ Labs have their way. Microsoft has introduced the Microsoft Surface and PQ Labs is building custom iTables, according to Kumparak (2009). Have you ever thought of digital information you can touch and grab? Microsoft and PQ Labs are leading us into the next generation of computing, known as surface or table computing.

Surface or table computing consists of a multitouch, multiuser interface that allows one to “grab” digital information and then collaborate, share, and store that information, without using a mouse or keyboard—just the hands and fingers, and such devices as a digital camera and personal digital assistant (PDA). This interface generally rests on top of a table and is so advanced that it can actually sense objects, touch, and gestures from many users (Microsoft, 2008).

Imagine entering a restaurant and interacting with the menu through the surface of the table where you sit. Once you have completed your order, you can begin computing by using the capabilities built into the surface or using your own device, such as a PDA. You can set the PDA on the surface and download images, graphics, and text to the surface. You can even communicate with others using full audio and video while waiting for your order. When you have finished eating, you simply set your credit card on the surface and it is automatically charged; you pick up your credit card and leave. This is certainly a different kind of eating experience—but one that will become commonplace for the next generation of users.

You might be wondering when this new age of computing will be touched by typical users. In fact, it is already used in Las Vegas, as well as in selected casinos, banks, restaurants, and hotels throughout the United States and Canada.

You should seek to explore this new interface, which will forever change how we interact and compute. Think of the ramifications for health care …

REFERENCES Kumparak, G. (2009). Look out, Microsoft Surface: The iTable might just trump you in every way. http://www.crunchgear.com/2009/01/10/look- out-microsoft-surface-the-itable-mightjust-trump-you-in-every-way/

Microsoft. (2008). Microsoft Surface: General questions. Retrieved May 2010 from http://www.microsoft.com/SURFACE/about_faqs/faqs.aspx

One example of this type of complexity masked in simplicity is the evolution of “plug and play” computer add-ons, where a peripheral, such as an iPod or game console, can be simply plugged into a serial or other port and instantly used.

Computers are universal machines, because they are general-purpose, symbol-manipulating devices that can perform any task represented in specific programs. For instance, they can be used to draw an image, calculate statistics, write an essay, or record nursing care data. In a nutshell, computers can be used for data and information storage, retrieval, analysis, generation, and transformation.

Most computers are based on scientist John Von Neumann’s model of a processor–memory–input–output architecture. In this

model, the logic unit and control unit are parts of the processor, the memory is the storage region, and the input and output segments are provided by the various computer devices, such as the keyboard, mouse, monitor, and printer. Recent developments have provided alternative configurations to the Von Neumann model—for example, the parallel computing model, where multiple processors are set up to work together. Nevertheless, today’s computer systems share the same basic configurations and components inherent in the earliest computers.

Components Hardware Computer hardware refers to the actual physical body of the computer and its components. Several key components in the average computer work together to shape a complex yet highly usable machine that serves as a tool for knowledge management, communication, and creativity.

Protection: The Casing

The most noticeable component of any computer is the outer case. Desktop personal computers have either a desktop case, which lies flat, horizontally on a desk, often with the computer monitor positioned on top of it; or a tower case, which stands vertically, and usually sits beside the monitor or on a lower shelf or the floor. Most cases come equipped with a case fan, which is extremely critical for keeping the computer components cool when in use. Laptop computers combine the casing in a flat rectangular casing that is attached to the hinged or foldable monitor. Palm computers and personal digital assistants also have a protective outer plastic and metal case with an embedded liquid crystal display screen.

Central Processing Unit

Sometimes conceptualized as the “brain” of the computer, the central processing unit (CPU) is the computer component that actually executes, calculates, and processes the binary computer code (which consists of various configurations of 0s and 1s), instigated by the operating system (OS) and other applications on the computer. The CPU serves as the command center that directs the actions of all other computer components, and it manages both incoming and outgoing data that are processed across components. Common CPUs include the Pentium, K6, PowerPC, and Sparc models.

The CPU contains specific mechanical units, including registers, arithmetic logic units, a floating point unit, control circuitry, and cache memory. Together, these inner components form the computer’s central processor. Registers consist of data-storing circuits whose contents are processed by the adjacent arithmetic and logic units or the floating point unit. Cache memory is extremely quick memory that holds whatever data and code are being used at any one time. The CPU uses the cache to store inprocess data so that it can be quickly retrieved as needed. The CPU is protected by a heat sink, a copper or aluminum metal block that cools the processor (often with the help of a fan) to prevent overheating.

In the past, the speed and power of a CPU were measured in units of megahertz and was written as a value in MHz (e.g., 400 MHz, meaning the microprocessor ran at 400 MHz, executing 400 million cycles per second). Today, it is more common to see the speed measured in gigahertz (1 GHz is equal to 1,000 MHz); thus a CPU that operates at 4 GHz is 1,000 times faster than an older one that operates at 4 MHz. The more cycles a processor can complete per second, the faster computer programs can run.

In recent years, processor manufacturers, such as Intel, have moved to multicore microprocessors, which are chips that combine two or more processors. In fact, multiple microprocessors have become a standard in both personal and professional-level computers. “Minicomputers, which were traditionally made from off-the-shelf logic or from gate arrays, have been replaced by servers made using microprocessors. Mainframes have been almost replaced with multiprocessors consisting of small numbers of off-the-shelf microprocessors. Even high-end supercomputers are being built with collections of microprocessors” (Hennessy & Patterson, 2006, p. 3).

Motherboard

The motherboard has been called the “central nervous system” of the computer. It is a key foundational component because all other components are connected to it in some way (either directly via local sockets, attached directly to it, or connected via cables). This includes universal serial bus (USB) controllers, Ethernet network controllers, integrated graphics controllers, and so forth. The essential structures of the motherboard include the major chipset, Super Input/Output chip, BIOS read-only memory (ROM), bus communications pathways, and a variety of sockets that allow components to plug into the board. The chipset (often a pair of chips) determines the computer’s CPU type and memory. It also houses the north bridge and south bridge controllers that allow the buses to transfer data from one to another.

Power Supply

The power supply is a critical component of any computer, because it provides the essential electrical energy needed to allow a computer to operate. The power supply unit converts the 240-V AC main power (provided via the power cable from the wall socket into which the computer is plugged) into low-voltage DC power. Computers depend on a reliable, steady supply of DC power to function properly. The more devices and programs used on a computer, the larger the power supply should be to avoid damage and malfunctioning. Power supplies normally range from 160 to 700 W, with an average of 300 to 400 W. Most contemporary power supply units come equipped with at least one fan to cool the unit under heavy use. The power supply is controlled by pressing the on and off switch, as well as the reset switch (which restarts the system) of a computer.

Laptop and other portable computing machines, such as electronic readers and tablet computers, are equipped with a rechargeable

battery power supply and the standard plug-in variety.

Hard Disk

This component is so named because of the rigid hard disks that reside in it, which are mounted to a spindle that is spun by a motor when in use. Drive heads (most computers have two or more heads) produce a magnetic field through their transducers that magnetizes the disk surface as a voltage is applied to the disk. The hard disk acts as a permanent data storage area that holds the gigabytes or even terabytes worth of data, information, documents, and programs saved on the computer, even when the computer is shut off. Disk drives are not infallible, however, so backing up important data is imperative.

The computer writes binary data to the hard drive by magnetizing small areas of its surface. Each drive head is connected to an actuator that moves along the disk to hover over any point on the disk surface as it spins. The parts of the hard disk are encased in a sealed unit. The hard drive is managed by a disk controller, which is a circuit board that controls the motor and actuator arm assembly. The hard drive produces the voltage waveform that contacts the heads to write and read data, and handles communications with the motherboard. It is usually located within the computer’s hard outer casing. Some people also attach a second hard drive externally, to increase available memory or to back up data.

Main Memory or Random-Access Memory

Random-access memory (RAM) is considered to be volatile memory because it is a temporary storage system that allows the processor to access program codes and data while working on a task. The contents of RAM are lost once the system is rebooted, shut off, or loses power.

The memory is actually situated on small chip boards, which sport rows of pins along the bottom edge and are plugged into the motherboard of the computer. These memory chips contain complex arrays of tiny memory circuits that can be either set by the CPU during write operations (puts them into storage) or read by the CPU during data retrieval. The circuits store the data in binary form as either a low (on) voltage stage, expressed as a 0, or a high (off) voltage stage, expressed as a 1. All of the work being done on a computer resides in RAM until it is saved onto the hard drive or other storage drive. Computers generally come with 2 GB of RAM or more, and some offer more RAM via graphics cards and other expansion cards.

A certain portion of the RAM, called the main memory, serves the hard disk and facilitates interactions between the hard disk and central processor. Main memory is provided by dynamic random access memory (DRAM) and is attached to the processor using specific addresses and data buses.

Synchronous dynamic random-access memory (SDRAM) (also known as static dynamic RAM) is “much faster than conventional (nonsynchronous) memory because it can synchronize itself with a microprocessor’s bus” (Null & Lobor, 2006, p. 8).

Read-Only Memory

Read-only memory (ROM) is essential permanent or semipermanent nonvolatile memory that stores saved data and is critical in the working of the computer’s OS and other activities. ROM is primarily stored in the motherboard, but it may also be available through the graphics card, other expansion cards, and peripherals. In recent years, rewritable ROM chips that may include other forms of ROM, such as programmable read-only memory (PROM), erasable ROM, electronically erasable programmable read-only memory (EEPROM), and a flash memory (a variation of electronically erasable programmable ROM) have become available.

Basic Input/Output System

The basic input/output system (BIOS) is a specific type of ROM used by the computer when it first boots up, to establish basic communication between the processor, motherboard, and other components. Often called boot firmware, it controls the computer from the time the machine is switched on until the primary OS (e.g., Windows, OS X, or Linux) takes over. The firmware initializes the hardware and boots (loads and executes) the primary OS.

Virtual Memory

Virtual memory is a special type of memory is stored on the hard disk to provide temporary data storage so data can be swapped in and out of the RAM as needed. This capability is particularly handy when working with large data-intensive programs, such as games and multimedia.

Integrated Drive Electronics Controller

The integrated drive electronics (IDE) controller component is the primary interface for the hard drive, compact disk read-only memory (CD-ROM), or digital video disk (DVD) drive, and the floppy disk drive.

Peripheral Component Interconnection Bus

This component is important for connecting additional plug-in components to the computer. It uses a series of slots on the motherboard to allow peripheral component interconnection (PCI) card plug-in.

Small Computer System Interface

The Small Computer System Interface (SCSI) component provides the means to attach additional devices, such as scanners and

extra hard drives, to the computer.

DVD/CD Drive

The CD-ROM drive reads and records data to portable CDs, using a laser diode to emit an infrared light beam that reflects onto a track on the CD using a mirror positioned by a motor. The light reflected on the disk is directed by a system of lenses to a photodetector that converts the light pulses into an electrical signal; this signal is then decoded by the drive electronics to the motherboard. Both compact disk-recordable (CD-R) and compact disk-rewritable (CD-RW) drives are common. The same principle applies to digital video disk-recordable (DVD-R) and digital video disk-rewritable (DVD-RW) drives. A DVD drive can do everything a CD drive can do, plus it can play the content of disks and, if it is a recordable unit, can record data on blank DVDs.

Flash or USB Drive

This portable memory device uses electronically erasable programmable ROM to provide fast permanent memory.

Modem

A modem is a component that can be situated either externally (external modem) or internally (internal modem) relative to the computer and enables Internet connectivity via a cable connection through network adaptors situated within the computer apparatus.

Connection Ports

All computers have connection ports made to fit different types of plug-in devices. These ports include a monitor cable port, keyboard and mouse ports, a network cable port, microphone/speaker/auxiliary input ports, USB ports, and printer ports (SCSI or parallel). These ports allow data to move to and from the computer via peripheral or storage devices. Specific ports include the following:

Parallel: connects to a printer Serial: connects to an external modem USB: connects to a myriad of plug-in devices, such as portable flash drives, digital cameras, MPEG-1 Audio Layer-3 (MP3)

players, graphics tablets, and light pens, using a plug-and-play connection (the ability to add devices automatically) FireWire (IEEE 1394): often used to connect digital-video devices to the computer Ethernet: connects networking apparatus, such as Internet and modem cables

Graphics Card

Most computers come equipped with a graphics accelerator card slotted in the microprocessor of a computer to process image data and output those data to the monitor. These in situ graphic cards provide satisfactory graphics quality for two-dimensional art and general text and numerical data. However, if a user intends to create or view three-dimensional images or is an active game user, one or more graphics enhancement cards are often installed.

Video Adapter Cards

Video adapter cards provide video memory, a video processor, and a digital-to-analog converter that works with the CPU to output higher quality video images to the monitor.

Sound Card

The sound card converts digital data into an analog signal that is then output to the computer’s speakers or headphones. The reverse is also accomplished by inputting a signal from a microphone or other audio recording equipment, which then converts the analog signal to a digital signal.

Bit

A bit is the smallest possible chunk of data memory used in computer processing and is depicted as either a 1 or a 0. Bits make up the binary system of the computer.

Byte

A byte is a chunk of memory that consists of 8 bits; it is considered to be the best way to indicate computer memory or storage capacity. In modern computers, bytes are described in units of megabytes (MB); gigabytes (GB), where 1 GB equals 1,000 MB; or terabytes (TB), where 1 TB equals 1 trillion bytes or 1,000 GB. Box 3-2 discusses storage capacities.

Software

Software comprises the application programs developed to facilitate various user functions, such as writing, artwork, organizing meetings, surfing the Internet, communicating with others, and so forth. For the purposes of this overview, the various types of software have been divided into four categories: (1) OS software, (2) productivity software, (3) creativity software, and (4)

communication software. User friendliness is a critical condition for effective software adoption. “End user performance is likely to be facilitated by user

friendliness of software packages” (Mahmood, 2003, p. 71). The easier and more intuitive a software package seems to be to a user influences that user’s perception of how clear the package is to understand and to use. The rapid evolution of hardware mentioned previously has been equally matched by the phenomenal development in software over the past three or four decades.

Commercial Software

Several large commercial software companies, such as Apple, Microsoft, IBM, and Adobe, dominate the market for software, and have done so since the advent of the personal computer. Licensed software has evolved over time; hence, most products have a long version history. Many software packages, such as office suites, are expensive to purchase; in turn, there is a “digital divide” as far as access and affordability go across societal spheres, especially when viewed from a global perspective.

BOX 3-2 STORAGE CAPACITIES Dee McGonigle and Kathleen Mastrian Storage and memory capacities are evolving. In the past few decades, there have been great leaps in data storage. It all begins with the bit, the basic unit of data storage, composed of 0s and 1s, also known as binary digits (bit). A byte is generally considered to be equal to 8 bits. The files on a computer are stored as binary files. The software that is used translates these binary files into words, numbers, pictures, images, or video. Using this binary code in the binary numbering system, measurement is counted by factors of 2, such as 1, 2, 4, 8, 16, 32, 64, and 128. These multiples of the binary system in computer usage are also prefixed based on the metric system. Therefore, a kilobyte (KB) is actually 2 to the 10th power (210) or 1,024 bytes, but is typically considered to be 1,000 bytes. This is why one sees 1,024 or multiples of that number instead of an even 1,000 mentioned at times in relation to kilobytes.

In the early 1980s, kilobytes were the norm as far as computer capacity went, and 128 KB machines were launched for personal use. Subsequent decades, however, have seen advanced computing power and storage capacity. As capabilities soared, so did the ability to save and store what was used and created. Megabytes (MB) emerged as a common unit of measure; a megabyte is 1,048,576 bytes but is considered to be roughly equivalent to 1 million bytes. The next leap in computer capacity was one that some people could not even imagine: gigabytes (GB). A gigabyte is 1,073,741,824 bytes but is generally rounded to 1 billion bytes. Some computing experts are very concerned that valuable bytes are lost when these measurements are rounded, whereas hard drive manufacturers use the decimal system so their capacity is expressed as an even 1 billion bytes per gigabyte.

The next advancements in computer capacity are moving into the range of terabytes (TB), petabytes (PB), exabytes (EB), zettabytes (ZB), and yottabytes (YB). These terms storage capacity are defined as follows:

TB 1,000 GB PB 1,000,000 GB EB 1,000 PB ZB 1,000 EB YB 1,000 ZB

To put all of this in perspective, Williams (n.d., para. 5) writes about the data powers of 10: 2 kilobytes: A typewritten page

2 megabytes: A high-resolution photograph 10 megabytes: A minute of high-fidelity sound or a digital chest X-ray 50 megabytes: A digital mammogram 1 gigabyte: A symphony in high-fidelity sound or a movie at TV quality 1 terabyte: All the X-ray films in a large technologically advanced hospital 2 petabytes: The contents of all U.S. academic research libraries 5 exabytes: All words ever spoken by human beings

We have not even addressed ZB and YB. Stay tuned …

REFERENCE Williams, R. (n.d.). Data powers of ten. http://ict.stmargaretsacademy.org.uk/computing/hardware/dataquan/d_p_ten2.html

Open Source Software

The open source movement began several years ago, but recently has become a powerful movement that is changing the software production and consumer market. In addition to commercially available software, a growing number of open source software packages are being developed in all four of the categories addressed in this chapter. The open source movement was begun by developers who wished to offer their creations to others for the good of the community and encouraged them to do the same. Users who modify or contribute to the evolution of open source software are obligated to share their new code, but essentially the software is free to all. Open Office and KOffice are both examples of open source productivity software.

OS Software

The OS is the most important software on any computer. It is the very first program to load on computer start-up and is fundamental for the operation of all other software and the computer hardware. Examples of commonly used operating systems include the Microsoft Windows family, Linux, Mac OS X, and UNIX. The OS manages both the hardware and the software and provides a reliable, consistent interface for the software applications to work with the computer’s hardware. An OS must be both powerful and flexible to adapt to the myriad of types of software available, which are made by a variety of development companies. New versions of

the major OSs are equipped to deal with multiple users and handle multitasking with ease. For instance, a user can work on a word processing document while listening for an “e-mail received” signal, have a Web browser window open to look for references on the Internet as needed, listen to music in the CD drive, and download a file—all at the same time.

OS tasks can be described in terms of six basic processes:

Memory management Device management Processor management Storage management Application interface User interface (usually a graphical user interface [GUI])

OSs should be convenient to use, easy to learn, reliable, safe, and fast. They should also be easy to design, implement, and maintain and should be flexible, reliable, error free, and efficient. For example, Silbershatz, Baer Galvin, and Gagne (2004) described how the Windows OS has been designed in keeping with the following goals established by Microsoft:

Portability: The OS can be moved from one hardware architecture to another with few changes needed. Security: The OS incorporates hardware protection for virtual memory and software protection mechanisms for OS resources. Portable operating system interface for UNIX (POSIX) compliance: Applications designed to follow the POSIX (IEEE

1003.1) standard can be compiled to run on Windows without changing the source code. Multiprocessor support: The OS is designed for symmetrical multiprocessing. Extensibility: This capability is provided by using a layered architecture with a protected executive layer for basic system

services, several server subsystems that operate in user mode, and a modular structure that allows additional environmental subsystems to be added without affecting the executive layer.

International support: The Windows OS supports different locales via the national language support application programming interface (API).

Compatibility with MS-DOS and MS-Windows applications.

Productivity Software

Productivity software, such as office suites, is the type of software most commonly used both in the workplace and on personal computers. Several software companies produce these multiple-program software, which usually bundles together word processing, spreadsheets, databases, presentation, Web development, and e-mail programs.

The intent of office suites is generally to provide all of the basic programs that office or knowledge workers need to do their work. The bundled programs within the suite are organized to be compatible with one another, are designed to look similar to one another for ease of use, and provide a powerful array of tools for data manipulation, information gathering, and knowledge generation. Some office suites add other programs, such as database creation software, mathematical editors, drawing, and desktop publishing programs. Table 3-1 summarizes the programs included in five of the most popular office suites: Microsoft Office, Open Office, KOffice, Corel WordPerfect Suite, and Apple iWork (for Macintosh computers). Of these five, Open Office (for Windows, Linux, Solaris, Mac OS X, FreeBSD, and HP-UX OSs) and KOffice (for Linux environments but also being developed for Windows and Mac OS X platforms) are open source, free software.

Creative Software

Creative software includes programs that allow users to draw, paint, render, record music and sound, and incorporate digital video and other multimedia in professional aesthetic ways to share and convey information and knowledge (Table 3-2).

Communication Software

Networking and communication software enable users to dialogue, share, and network with other users via the exchange of e-mail or instant messages, by accessing the World Wide Web, or by engaging in virtual meetings using conferencing software (Table 3-3).

TABLE 3-1 OFFICE SUITE SOFTWARE FEATURES AND EXAMPLES

OFFICE SUITE SOFTWARE Program Application Examples

Word processing Composition, editing, formatting,and producing text documents

Microsoft Word, Open Office Writer, KOffice KWord, Corel WordPerfect or Corel Write, Apple Pages

Spreadsheets Grid-based documents in ledger format; organizes numbers and text; calculates statistical formulae

Microsoft Excel, Open Office Calc, KOffice Kspread, Corel Quattro Pro, Apple Numbers

Presentations

Slideshow software, usually used for business or classroom presentations using text, images,

Microsoft Power Point, Open Office Impress, KOffice KPresenter, Corel Show, Apple Keynote

graphs, media

Databases Database creation for text andnumbers

Microsoft Access (in elite packages), Open Office Base, KOffice Kexi, Corel Calculate, Corel Paradox

E-mail Integrated e-mail program to sendand receive electronic mail Microsoft Outlook, Corel WordPerfect Mail, Mozilla Thunderbird

Drawing Graphics and diagram drawing Open Office Draw, Corel Presentation Graphics,KOffice Kivio, Karbon, Krita

Math formulas Inserts math equations in wordprocessing and presentation work Open Office Math, KOffice KFormula

Desktop publishing Page layouts and publication-readydocuments Microsoft Publisher (in elite packages), Apple Pages

TABLE 3-2 CREATIVE SOFTWARE FEATURES AND EXAMPLES

CREATIVE SOFTWARE Program and Application Software Examples

Raster graphics programs

Draw, paint, render, manipulate and edit images, fonts, and photographs to create pixel-based (dot points) digital art and graphics.

Adobe Photoshop and Fireworks, Ulead PhotoImpact, Corel Draw, Painter, and Paint Shop Pro, GIMP (open source), KOffice’s Krita (open source)

Vector graphics programs

Mathematically rendered, geometric modeling is applied through shapes, curves, lines, points and manipulated for shape, color, size. Ideal for printing and three-dimensional (3D) modeling

Adobe Flash, Freehand, and Illustrator, CorelDraw and Designer, Open Office Draw (open source), Mirosoft Visio, Xara Xtreme, KOffice Karbon14 (open source)

Desktop publishing programs

Page layout and publishing preparation for printed and web documents, such as magazines, journals, books, newsletters, brochures

Adobe InDesign, Corel PageMaker, Microsoft Publisher, Scribus (open source), QuarkXPress, Apple Pages (note that many of the graphics programs can also be used for DTP)

Web design programs

Create, edit, update webpages using specific codes, such as XML, CSS, HTML, and JAVA

Adobe Dreamweaver, Coffee Cup, Microsoft FrontPage, Nvu (open source), W3C’s Amaya (open source)

Multimedia programs

Combines text, audio, images, animation, and video into interactive content for electronic presentation.

Adobe Flash, Microsoft Movie Maker, Apple QuickTime and FinalCut Studio, Corel VideoStudio, Ulead VideoStudio, Real Studio, CamStudio (open source), Audacity (open source)

TABLE 3-3 COMMUNICATION SOFTWARE FEATURES AND EXAMPLES

COMMUNICATI ON SOFTWARE E-mail client Resident programs

Allows user to read, edit, forward, and send email messages to other users via an Internet connection. The software can be resident on the computer or accessed via the World Wide Web

Microsoft Outlook and Outlook Express, Eudora, Pegasus, Mozilla Thunderbird, Lotus Notes Web-based programs Gmail, Yahoo Mail, Hotmail

Internet browsers

Enables user to access, browse, download, upload, and interact with text, audio, video, and other Web-based documents

Mozilla Firefox, Microsoft Internet Explorer, Google Chrome, Apple Safari, Opera, Microbrowser (for mobile access)

Instant messaging (IM)

Real-time text messaging between users, can attach images, videos, and other documents via personal computer, cell phone, hand-held devices Conferencing

MSN Instant Messenger, Microsoft Live Messenger, Yahoo Messenger, Apple iChat

Enables user to communicate in a virtual meeting room setting to share work, discussions, planning, using an intranet or Internet environment; can exhibit files, video, screen shots of content

Adobe Acrobat Connect, Microsoft Live Meeting or Meeting Space, GotoMeeting, Meeting Bridge, Free Conference, RainDance, WebEx

Acquisition of Data and Information: Input Components

Input devices include the keyboard; mouse; joysticks (typically used for playing computer games); game controllers or pads; Web cameras (webcams); stylus (often used with tablets or personal digital assistants); image scanners for copying a digital image of a document or picture; or other plug-and-play input devices, such as digital cameras, digital video recorders (camcorders), MP3 players, electronic musical instruments, and physiologic monitors (Figure 3-1). These devices are the origin or medium used to input text, visual, audio, or multimedia data into the computer system for viewing, listening, manipulating, creating, or editing. The two primary input devices on a computer are the keyboard and mouse.

Figure 3-1 Computer System

Keyboard

Computer keyboards are very similar to the typewriter keyboards of earlier days and usually serve as the prime input device that enables the user to type words, numbers, and commands into the computer’s programs. Standard computer keyboards have 101 keys and are organized to facilitate Latin-based languages using a QWERTY layout (so named because these letters appear on the first six keys in the first row of letters).

Certain keys are used as command keys, particularly the control (CTRL), alternate (Alt), delete (Del), and shift keys, which can all be used to activate useful commands. The escape (ESC) key allows the user instantly to exit a process or program. The F keys, numbered F1 through F12, are function keys. They are used in different ways by particular programs. If a program instructs users to press the “F8” key, they would do so by pressing F8. The print screen (PrtSc) key sends a graphical picture or screen shot of a computer screen to the clipboard. This copied screen shot can then be pasted in any graphic program that can work with bitmap files.

Mouse

The mouse is the second most commonly used input device. It is manipulated by the user’s hand to point, click, and move objects around on the computer screen. A mouse can come in a number of different configurations, including a standard mechanical trackball serial mouse, bus mouse, PS/2 mouse, USB connected mouse, optical lens mouse, cordless mouse, and optomechanical mouse.

Processing of Data and Information: Throughput/Processing Components

All of the hardware discussed earlier in this chapter is involved in the throughput or processing of input data and in the preparation of output data and information. Specific software is used, depending on the application and data involved. One key hardware component, the computer monitor, is a unique example of a visible throughput component—it is the part of the computer that users focus on the most when they are working on a computer. Input data can be visualized and accessed by manipulating the mouse and keyboard input devices, but it is the monitor that receives the user’s attention. The monitor is critical for the efficient rendering during this part of the cycle, because it facilitates user access and control of the data and information.

Monitor

The monitor is the visual display that serves as the landscape for all interactions between user and machine. It typically resembles a television screen, and comes in various sizes (usually ranging from 15 to 21 inches) and configurations. Monitors are either based on cathode ray tubes (the conventional monitor with a large section behind the screen) or are thinner, flat-screen liquid crystal display devices. Some computer monitors also have a touch screen that can serve as an input device when the user touches specific areas of the screen.

Monitors vary in their refresh rate (usually measured in megahertz) and dot pitch. Both of these characteristics are important for user comfort. The faster the refresh rate, the cleaner and clearer the image on the screen, because the monitor refreshes the screen contents more frequently. For instance, a monitor with a 100 MHz refresh rate refreshes the screen contents 100 times per second. Similarly, the larger the dot pitch factor, the smaller the dots that make up the screen image, which provides a more detailed display on the monitor and also facilitates clarity and ease of viewing.

If equipped with a touch screen, a monitor can also serve as an input device when activated by a stylus or finger pressure. Some users might also consider the monitor to be an output device, because access to input and stored documents is often performed via the

screen (e.g., reading a document that is stored on the computer or viewable from the Internet).

Dissemination: Output Components

Output devices carry data in a usable form through exit devices in or attached to a computer. Common forms of output include printed documents, audio or video files, physiologic summaries, scan results, and saved files on portable disk drives, such as a CD, DVD, flash drive, or external hard drive. Output devices literally put data and information at the user’s fingertips, which can then be used to develop knowledge and even wisdom. The most commonly used output devices include printers, speakers, and portable disk drives.

Printer

Printers are external components that can be attached to a computer using a printer cord that is secured into the computer’s printer port. Printers enable users to print a hard paper copy of documents that are housed on the computer.

The most common printer types are the inkjet and laser printers. Inkjet printers are more economical to use and offer quite good quality; they apply ink to paper using a jet-spray mechanism. Laser printers produce publisher-ready quality printing if combined with good-quality paper but cost more in terms of printing supplies. Both types of printers can print in black and white or in color.

Speakers

All computers have some sort of speaker setup, usually small speakers embedded in the monitor, in the case, or, if a laptop, close to the keyboard. Often, external speakers are added to a computer system using speaker connectors; these devices provide enhanced sound and a more enjoyable listening experience.

What Is the Relationship of Computer Science to Knowledge? Scholars and researchers are just beginning to understand the effects that computer systems, architecture, applications, and processes have on the potential for knowledge acquisition and development. Users who have access to contemporary computers equipped with full Internet access have resources at their fingertips that were only dreamed of before the 21st century. Entire library collections are accessible, with many documents available in full printable form. Users are also able to contribute to the development of knowledge through the use of productivity, creativity, and communication software. In addition, using the World Wide Web interface, users are able to disseminate knowledge on a grand scale with other users.

This deluge of information available via computers must be mastered and organized by the user if knowledge is to emerge. Discernment and the ability to critique and filter this information must also be present to facilitate the further development of wisdom.

The development of an understanding of computer science principles as they apply to technology used in nursing can facilitate optimal usage of the technology for knowledge development in the profession. The maxim that “knowledge is power” and that the skillful use of computers lies at the heart of this power is a presumption:

The computer-literate nurse will have knowledge, and as a result, power and influence. Society has accepted computers as standard elements, and as such, computers will continue to shape nurses’ psychological, social, economic, and political existence in innumerable ways. Nursing, in order to interface with other spheres of society, must be computer literate. In short, society has accepted computer technology as a means to enhance life; so must nursing. (Richards, 2001, p. 9)

Once nurses become comfortable with the various technologies, they can shape them, refine them, and apply them in new and different ways, just as they have always adapted earlier equipment and technologies.

How Does the Computer Support Collaboration and Information Exchange? Computers can be linked to other computers through networking software and hardware to promote communication, information exchange, work sharing, and collaboration. Such networks can be local or organizationally based, with computers joined together into a local area network; or organized on a wider area scope (e.g., a city or district) using a metropolitan area network; or encompassing computers at an even greater distance (e.g., a whole country or continent, or the Internet itself) using a wide area network configuration (Sarkar, 2006). Network interface cards are used to connect a computer and its modem to a network.

Networks within health care can manifest in several different configurations, including client-focused networks, such as in telenursing, e-health, and client support networks; work-related networks, including virtual work and virtual social networks; and learning and research networks, as in communities of practice. These trends are still in their infancy in most nursing work environments (and most nurses’ personal lives), but they are predicted to grow dramatically in the future:

As the Net generation grows in influence, the trend will be toward networks, not hierarchies; toward open collaboration rather than authority; toward consensus rather than arbitrary edict. The communication support provided by networks and information systems will also alter patterns of social interaction within a healthcare organization. This technology provides a medium for greater accessibility to shared information and support for rich interpersonal exchange and collaboration across departmental boundaries. (Richards, 2001, p. 10)

Virtual social networks are another form of professional network that have expanded phenomenally since the advent of the Internet and other computer software and hardware:

Electronic media do more than just expand access to vast bodies of information. They also serve as a convenient vehicle for building virtual social networks for creating shared knowledge through collaborative learning and problem solving. Cross pollination of ideas through worldwide connectivity can boost creativity synergistically in the co-construction of knowledge. (Bandura, 2002, p. 4)

Nursing-related virtual social networks provide a cyberspace for nurses to make contacts, share information and ideas, and build a sense of community.

Social communication software is used to provide a dynamic virtual environment, and often virtual social networks provide communicative capabilities through posting tools, such as blogs, forums, and wikis; e-mail for sharing ideas on a smaller scale; collaborative areas for interaction, creating, and building digital artifacts or planning projects; navigation tools for moving through the virtual network landscape; and profiles to provide a space for each member to disclose personal information with others. Nurses who have to engage in shift work often find that virtual social networks can provide a sense of connection with other professionals that is available around the clock. Because time is often a factor in any social interchange, virtual communication often offers an alternative for practicing nurses, who can access information and engage in interchanges at any time of day. With active participation, the interchanges and shared information and ideas of the network can culminate in valuable social and cultural capital, available to all members of that network. Often, nursing virtual social networks are created for the purpose of exchanging ideas on practice issues and best practices; to become more knowledgeable about new trends, research, and innovations in health care; or to participate in advocacy, activist, and educational initiatives.

Through the use of portable disk devices, such as flash drives, CDs, and DVDs, people can share information, documents, and communications by exchanging files. Since the advent of the Internet in the mid-1980s, the World Wide Web has evolved to become a viable and user-friendly way for people to collaborate and exchange information, projects, and other knowledge-based files, such as websites, e-mail, social networking applications, and web conferencing logs. Box 3-3 provides information on Web 2.0, the latest iteration of the World Wide Web.

BOX 3-3 WEB 2.0 TOOLS Dee McGonigle, Kathleen Mastrian, and Wendy Mahan Web 2.0—the name given to the new World Wide Web tools—enables users to collaborate, network socially, and disseminate knowledge with other users on a scale that was once not even comprehensible. These programs promote data and information exchange, feedback, and knowledge development and dissemination.

To facilitate a selective review of the Web 2.0 tools available, they have been categorized into three areas here: (1) tools for creating and sharing information, (2) tools for collaborating, and (3) tools for communicating. Examples of tools for creating and sharing information include blogs, podcasts, Flickr, YouTube, Hellodeo, Jing, Screencast-o-matic, Facebook, MySpace, and MakeBeliefsComix. Examples of tools for collaborating with others include Google Docs, Zoho, wikis, Del.icio.us, and Gliffy. Finally, some tools for communicating with others include Adobe Connect, Vyew, Skype, Twitter, and instant messaging.

The application of the creating and sharing information tools has led to an explosion of social networking on the Web. YouTube has promoted the “broadcast yourself” proliferation. Anyone can launch a video onto YouTube that is shared with others over the Web. Similarly, Flickr allows users to upload and tag personal photos to share either privately or publicly. Facebook and MySpace both promote socializing on the Web. Facebook is a social utility and MySpace is a place for friends, according to the descriptions found on these websites. Other tools let users create and share recorded messages, diagrams, screen captures, and even custom comic strips.

Collaborating over the Web has become easier. Indeed, it is a way of life for many people. Google Docs and Zoho allow users to create online and share and collaborate in real time. Wikis are server-based software programs that enable users to generate and edit webpage content using any browser. Del.icio.us is a social bookmarking manager that uses tags to identify or describe the bookmarks that can be shared with others.

Communicating with others includes audio and video conferencing in real time. Adobe Connect is a comprehensive Web communications solution. Although a fee-based service, it does provide a free trial. Users should read all of the documentation on Adobe’s site before downloading, installing, and using this software. Vyew is free, always-on collaboration plus live Web conferencing. Skype allows users to make calls in audio only or with video. Users can download Skype for free but depending on the type of calls made, fees or charges could be assessed. Individuals should read through all of the information before downloading, installing, and using this software. Twitter allows participants to answer the question “What are you doing?” with messages containing 140 or fewer characters. Although Twitter can be used to keep the friends in a person’s network updated on daily activities, it can also be used for other purposes, such as asking questions or expressing thoughts. In addition, Twitter can be accessed by cell phones, so users can stay in touch on the go.

Along with all of the advantages and intellectual harvesting capabilities from the use of these tools come serious security issues. Wagner (2007) warns the user to “bear in mind before you jump in that you’re giving information to a third-party company to store” (para. 5). He also states that “you should talk to your company’s legal and compliance offices to be sure you’re obeying the law and regulations with regard to managing company’s information” (para. 5). One suggestion that Wagner offers is that if you do not want to involve a third party, “Wikis provide a good alternative for organizations looking to maintain control of their own software. Organizations can install wiki software on their own, internal servers” (para. 6).

This new wave of Web-based tools facilitates the ability to interact, exchange, collaborate, communicate, and share in ways that have only begun to be realized. As the tools and their innovative uses continue to expand, users need to stay vigilant to handle the associated security challenges. These Web 2.0 tools are providing a new cyber-playground that is limited only by users’ own imaginations and intelligence. We encourage you to explore these tools. Refer to this text’s companion website (http://go.jblearning.com/mcgonigle) for more information.

REFERENCE Wagner, M. (2007). Nine easy Web-based collaborative tools. http://www.forbes.com/2007/02/26/google-microsoft-bluetie-enttech- cx_mw_0226smallbizresource.html

What Is the Human–Technology Interface? In the context of using a computer system, the human–technology interface is facilitated by the input and output devices discussed previously in this chapter. Specifically, the keyboard, mouse, monitor, laser pen, joystick, stylus, or game pads and controls, and other

USB or plug-and-play devices, such as MP3 players, digital cameras, digital camcorders, musical instruments, and hand-held smaller computers, such as personal digital assistants, are all viable devices for interfacing with a computer.

The GUI associated with the OS of a computer provides the on-screen environment for direct interaction between the user and the computer. The typical GUI provided by Windows or Mac OS X utilizes a user-friendly desktop metaphor interface that is made up of the input and output devices and icons that represent files, programs, actions, and processes. These interface icons can be activated by clicking the mouse buttons to perform various actions, such as provide information, execute functions, open and manipulate folders (directories), select options, and so forth.

Although these aspects of a computer system may be taken for granted, they are critical in facilitating a sense of comfort and competency in users of the system. This environment is particularly critical in nursing, when computers are used in the context of nursing care. One question that arises is, Do nurses control these information technology tools, or do the tools shape the activities, decisions, and attention of the nurses as users of technology? Both possibilities can be answered in the affirmative to some extent, but the former is the safest situation for nursing care. (See The Art of Caring in Technology-Laden Environments for an expanded discussion of this issue). If the nurse user needs to focus on the software or hardware because of difficult-to-use programs, confusing GUI schema, or sheer complexity in the programming, the nurse’s provision of client care will suffer. It is critical that any software and hardware used in the nursing milieu be expertly designed to facilitate nursing care in a user-friendly, intuitive way. This is one reason that informatics experts, called nurse informaticians, are being placed in positions of authority where they can facilitate the adoption of computer systems within nursing care environments. It is essential that the activities of the staff nurses are reflected well within the software that is used in the care setting. If nurses are knowledgeable about computers and related technologies, they will be able to provide meaningful data and information about how computer systems best work within their particular care areas.

In an ideal world, nurses would be able to use and interact with computer technologies effectively to enhance patient care. They would understand computer science and know how to harness its capabilities to benefit the profession and ultimately their patients.

Looking to the Future The coming trends toward wearable technology, smaller and faster hand-held and portable computer systems, and high-quality voice- activated inventions will further facilitate the use of computers in nursing practice and professional development. The field of computer science will continue to contribute to the evolving art and science of nursing informatics. New trends promise to bring wide- sweeping and (it is hoped) positive changes to the practice of nursing. Computers and other technologies have the potential to support a more client-oriented healthcare system in which clients truly become active participants in their own healthcare planning and decisions. Mobile health technology, telenursing, sophisticated electronic health records, and next-generation technology are predicted to contribute to high-quality nursing care and consultation within healthcare settings, including patients’ homes and communities.

In the future, computers will become more powerful yet more compact, which will contribute to the development of several technologic initiatives that are currently still in their infancy. Some of these initiatives are described here. These predicted innovations are only some of the many computer and technologic applications being developed. As nurses gain proficiency in capitalizing on the creative, time-saving, and interactive capabilities emerging from information technology research, the field of nursing informatics will grow in similar proportions.

Voice-Activated Communicators

Voice-activated communicators are already being developed by a variety of companies, including Vocera Communications. These new technologies will permit nurses and other healthcare professionals to use wireless, hands-free devices to communicate with one another and to record data. This technology promises to become a user-friendly and cost-effective way to increase clinical productivity.

Game and Simulation Technology

Game and simulation technology promises to offer realistic, innovative ways to teach nursing content in general, including nursing informatics concepts and skills. The same technology that powers video games can be used to create dynamic educational interfaces to help student nurses learn about pathophysiology, care guidelines, medication usage, and a host of other topics. Such applications can also be very valuable for client education and health promotion materials. The “serious games” industry is just beginning to develop. Video game producers are now looking beyond mere entertainment to address public and private policy, management, and leadership issues and topics, including those related to health care. For example, the Games for Health Project, initiated by the Robert Wood Johnson Foundation (2006), is working on developing best practices to support innovation in healthcare training, messaging, and illness management.

Virtual Reality

Virtual reality is another technological breakthrough that will become common in nursing education and professional development in the future. Virtual reality is a three-dimensional, computer-generated “world” where a person (with the right equipment) can move about and interact as if he or she were actually in the visualized location. The person’s senses are immersed in this virtual reality world using special gadgetry, such as head-mounted displays, data gloves, joysticks, and other hand tools. The equipment and special technology provides a sense of presence that is lacking in multimedia and other complex programs.

Mobile Devices

Mobile devices will be used more by nurses both at the point of care and in planning, documenting, interacting with the healthcare team, and research.

There are strong indicators that nursing is ready to move quickly to adopt this new technology and utilize it to its full potential at the point-of-care. We anticipate the rate of adoption for mobile information systems within nursing to be rapid, and it will ultimately equal and perhaps exceed that of physicians. Mobile Nursing Informatics will be at the core of nursing in the 21st century. Ready access to data and analytical tools will fundamentally change the way practitioners of the health sciences conduct research, and approach and solve problems. (Suszka-Hildebrandt, 2000, p. 3)

Summary The field of computer science is one of the fastest-growing disciplines. Astonishing innovations in computer hardware, software, and architecture have occurred over the past few decades, and there are no indications that this trend will come to a halt anytime soon. Computers have increased in speed, accuracy, and efficiency, yet now cost less and have reduced physical size compared to their forebears. These trends are predicted to continue. Current computer hardware and software serve as vital and valuable tools for both nurses and clients to engage in on-screen and online activities that provide rich access to data and information. Productivity, creativity, and communication software tools also enable nurses to work with computers to further foster knowledge acquisition and development. Wide access to vast stores of information and knowledge shared by others facilitates the emergence of wisdom in users, which can then be applied to nursing in meaningful and creative ways. It is imperative that nurses become discerning, yet skillful users of computer technology to apply the principles of nursing informatics to practice, and to contribute to the profession’s ever-growing body of knowledge.

Working Wisdom Since the beginning of the profession, nurses have applied their ingenuity, resourcefulness, and professional awareness of what works to adapt technology and objects to support nursing care, usually with the intention of promoting efficiency but also in support of client comfort and healing. This resourcefulness could also be applied effectively to the adaptation of information technology within the care environment, to ensure that the technology truly does serve clients and nurses and the rest of the interdisciplinary team.

Consider this question: “How can you develop competency in using the various computer hardware and software not only to promote efficient nursing care and to develop yourself professionally, but also to further the development of the profession’s body of knowledge?”

Application Scenario Dan P. is a first-year student in graduate studies in nursing. In the past, he has learned to use his family’s personal computer to surf the World Wide Web, exchange e-mail with friends, and play some computer games. Now, however, Dan realizes that the computer is a vital tool for his academic success. He has saved up enough money to purchase a laptop computer. He has decided on a Pentium CPU system with 500 GB of storage and 4 GB of RAM. Dan also wishes to choose appropriate software for his system. He is on a limited budget but wants to make the most of his investment.

1. Which of the four categories of software discussed in this chapter would benefit Dan the most in his studies (OS, productivity, creativity, or communication)? Dan definitely needs an OS—this is critical. He would also directly benefit from productivity software and at least connective e-mail and web browser software from the communication group so he can access the Internet for research, to collaborate with peers, and to communicate with his teachers.

2. How could Dan afford to install software from all four groups on his new laptop? If Dan accessed some open source software (e.g., Open Office for his productivity software), he could save money to put toward creativity software.

Internet and Software Resources BBC Absolute Beginner’s Guide to Using Your Computer: A WebWise Guide. http://www.bbc.co.uk/webwise/abbeg/abbeg.shtml BBC’s Computer Tutor: The BBC’s Guide to Using a Computer. http://www.bbc.co.uk/webwise/topics/your-computer/

THOUGHT-PROVOKING QUESTIONS

1. How can knowledge of computer hardware and software help nurses to participate in information technology adoption decisions in the practice area?

2. How can new computer software help nurses engage in professional development, collaboration, and knowledge dissemination activities at their own pace and leisure?

References Bandura, A. (2002). Growing primacy of human agency in adaptation and change in the electronic era. European Psychologist, 7(1), 2–16. Evans, D. (2010). Introduction to computing: Explorations in language, logic, and machines. University of Virginia. http://www.computingbook.org Hennessy, J., & Patterson, D. (2006). Computer architecture: A quantitative approach (4th ed.). San Francisco, CA: Morgan Kaufmann. Intel Corporation. (2008). Concealing complexity. Technology and research. http://techresearch.intel.com/articles/Exploratory/1430.htm

Mahmood, M. (2003). Advanced topics in end user computing. Hershey, PA: Idea Group Inc. Null, L., & Lobor, J. (2006). The essentials of computer organization and architecture (2nd ed.). Sudbury, MA: Jones and Bartlett. Richards, J. A. (2001). Nursing in a digital age. Nursing Economic$, 19(1), 6–12. Robert Wood Johnson Foundation. (2006). Games for health. http://gamesforhealth.org/about/ Sarkar, N. (2006). Tools for teaching computer networking and hardware concepts. Hershey, PA: Idea Group. Silbershatz, A., Baer Galvin, P., & Gagne, G. (2004). Operating system concepts (7th ed.). Hoboken, NJ: John Wiley & Sons. Suszka-Hildebrandt, S. (2000). Mobile information technology at the point-of-care. PDA Cortex. http://www.rnpalm.com/mitatpoc.htm

Chapter 4

Introduction to Cognitive Science and Cognitive Informatics Dee McGonigle and Kathleen Mastrian

OBJECTIVES

1. Describe cognitive science. 2. Assess how the human mind processes and generates information and knowledge. 3. Explore cognitive informatics. 4. Examine artificial intelligence and its relationship to cognitive science and computer science.

Key Terms

Artificial intelligence Brain Cognitive informatics Cognitive science Computer science Connectionism Decision making Empiricism Epistemology Intelligence Intuition Knowledge Logic Memory Mind Neuroscience Perception Problem solving Psychology Rationalism Reasoning Wisdom

Introduction Cognitive science is the fourth of four basic building blocks used to understand informatics. The Building Blocks of Nursing Informatics section began by examining nursing science, information science, and computer science and considering how each relates to and helps one understand the concept of informatics. This chapter explores the building blocks of cognitive science, cognitive informatics (CI), and artificial intelligence (AI).

Throughout the centuries, cognitive science has intrigued philosophers and educators alike. Beginning in Greece, the ancient philosophers sought to comprehend how the mind works and what the nature of knowledge is. This age-old quest to unravel the processes inherent in the working brain has been undertaken by some of the greatest minds in history. However, it was only about 50 years ago that computer operations and actions were linked to cognitive science, meaning theories of the mind, intellect, or brain. This association led to the expansion of cognitive science to examine the complete array of cognitive processes, from lower-level perceptions to higher-level critical thinking, logical analysis, and reasoning.

The focus of this chapter is the impact of cognitive science on nursing informatics (NI). This section provides the reader with an introduction and overview of cognitive science, the nature of knowledge, wisdom, and AI as they apply to the Foundation of Knowledge model and NI. The applications to NI include problem solving, decision support systems, usability issues, user-centered

interfaces and systems, and the development and use of terminologies.

Cognitive Science The interdisciplinary field of cognitive science studies the mind, intelligence, and behavior from an information processing perspective. According to Wikipedia (2013), “The term cognitive science was coined by Christopher Longuet-Higgins in his 1973 commentary on the Lighthill report, which concerned the then-current state of artificial intelligence research” (para. 36). The Cognitive Science Society and the Cognitive Science Journal date back to 1980 (Cognitive Science Society, 2005). Their interdisciplinary base arises from psychology, philosophy, neuroscience, computer science, linguistics, biology, and physics; covers memory, attention, perception, reasoning, language, mental ability, and computational models of cognitive processes; and explores the nature of the mind, knowledge representation, language, problem solving, decision making, and the social factors influencing the design and use of technology. Simply put, cognitive science is the study of the mind and how information is processed in the mind. As described in the Stanford Encyclopedia of Philosophy:

The central hypothesis of cognitive science is that thinking can best be understood in terms of representational structures in the mind and computational procedures that operate on those structures. While there is much disagreement about the nature of the representations and computations that constitute thinking, the central hypothesis is general enough to encompass the current range of thinking in cognitive science, including connectionist theories which model thinking using artificial neural networks. (2010, para. 9)

Connectionism is a component of cognitive science that uses computer modeling through artificial neural networks to explain human intellectual abilities. Neural networks can be thought of as interconnected simple processing devices or simplified models of the brain and nervous system that consist of a considerable number of elements or units (analogs of neurons) linked together in a pattern of connections (analogs of synapses). A neural network that models the entire nervous system would have three types of units: (1) input units (analogs of sensory neurons), which receive information to be processed; (2) hidden units (analogs to all of the other neurons, not sensory or motor), which work in between input and output units; and (3) output units (analogs of motor neurons), where the outcomes or results of the processing are found.

Connectionism is rooted in how computation occurs in the brain and nervous system or biologic neural networks. On their own, single neurons have minimal computational capacity. When interconnected with other neurons, however, they have immense computational power. The connectionism system or model learns by modifying the connections linking the neurons. Just as neurons form elaborate information processing networks, so artificial neural networks are unique computer programs that model or simulate their biologic analogs.

The mind is frequently compared to a computer, and experts in computer science strive to understand how the mind processes data and information. In contrast, experts in cognitive science model human thinking using artificial networks provided by computers—an endeavor sometimes referred to as AI. How does the mind process all of the inputs received? Which items and in which ways are things stored or placed into memory, accessed, augmented, changed, reconfigured, and restored? Cognitive science provides the scaffolding for the analysis and modeling of complicated, multifaceted human performance and has a tremendous effect on the issues impacting informatics.

The end user is the focus of this activity because the concern is with enhancing the performance in the workplace; in nursing, the end user could be the actual clinician in the clinical setting, and cognitive science can enhance the integration and implementation of the technologies being designed to facilitate this knowledge worker with the ultimate goal of improving patient care. Technologies change rapidly, and this evolution must be harnessed for the clinician at the bedside. To do this at all levels of nursing practice, one must understand the nature of knowledge, the information and knowledge needed, and the means by which the nurse processes this information and knowledge in the situational context.

Sources of Knowledge Just as philosophers have questioned the nature of knowledge, so they have also strived to determine how knowledge arises, because the origins of knowledge can help one understand its nature. How do people come to know what they know about themselves, others, and their world? There are many viewpoints on this issue, both scientific and nonscientific.

According to Holt (2006), “There are two competing traditions concerning the ultimate source of our knowledge: empiricism and rationalism” (para. 3). Empiricism is based on knowledge being derived from experiences or senses, whereas rationalism contends that “some of our knowledge is derived from reason alone and that reason plays an important role in the acquisition of all of our knowledge” (para. 5). Empiricists do not recognize innate knowledge, whereas rationalists believe that reason is more essential in the acquisition of knowledge than the senses.

Three sources of knowledge have been identified: (1) instinct, (2) reason, and (3) intuition. Instinct is when one reacts without reason, such as when a car is heading toward a pedestrian and he jumps out of the way without thinking. Instinct is found in both humans and animals, whereas reason and intuition are found only in humans. Reason “[c]ollects facts, generalizes, reasons out from cause to effect, from effect to cause, from premises to conclusions, from propositions to proofs” (Sivananda, 2004, para. 4). Intuition is a way of acquiring knowledge that cannot be obtained by inference, deduction, observation, reason, analysis, or experience. Intuition was described by Aristotle as “A leap of understanding, a grasping of a larger concept unreachable by other intellectual means, yet fundamentally an intellectual process” (Shallcross & Sisk, 1999, para. 4).

Some believe that knowledge is acquired through perception and logic. Perception is the process of acquiring knowledge about the environment or situation by obtaining, interpreting, selecting, and organizing sensory information from seeing, hearing, touching, tasting, and smelling. Logic is “[a] science that deals with the principles and criteria of validity of inference and demonstration: the science of the formal principles of reasoning” (Merriam-Webster Online Dictionary, 2007, para. 1). Acquiring knowledge through

logic requires reasoned action to make valid inferences. The sources of knowledge provide a variety of inputs, throughputs, and outputs through which knowledge is processed. No matter

how one believes knowledge is acquired, it is important to be able to explain or describe those beliefs, communicate those thoughts, enhance shared understanding, and discover the nature of knowledge.

Nature of Knowledge Epistemology is the study of the nature and origin of knowledge—that is, what it means to know. Everyone has a conception of what it means to know based on their own perceptions, education, and experiences; knowledge is a part of life that continues to grow with the person. Thus a definition of knowledge is somewhat difficult to agree on because it reflects the viewpoints, beliefs, and understandings of the person or group defining it. Some people believe that knowledge is part of a sequential learning process resembling a pyramid, with data on the bottom, rising to information, then knowledge, and finally wisdom. Others believe that knowledge emerges from interactions and experience with the environment, and still others think that it is religiously or culturally bound. Knowledge acquisition is thought to be an internal process derived through thinking and cognition or an external process from senses, observations, studies, and interactions. Descartes’s important premise “called ‘the way of ideas’ represents the attempt in epistemology to provide a foundation for our knowledge of the external world (as well as our knowledge of the past and of other minds) in the mental experiences of the individual” (Encyclopedia Britannica, 2007, para. 4).

For the purpose of this text, knowledge is defined as the awareness and understanding of a set of information and ways that information can be made useful to support a specific task or arrive at a decision. It abounds with others’ thoughts and information or consists of information that is synthesized so that relationships are identified and formalized.

How Knowledge and Wisdom Are Used in Decision Making The reason for collecting and building data, information, and knowledge is to be able to make informed, judicious, prudent, and intelligent decisions. When one considers the nature of knowledge and its applications, one must also examine the concept of wisdom. Wisdom has been defined in numerous ways:

Knowledge applied in a practical way or translated into actions The use of knowledge and experience to heighten common sense and insight to exercise sound judgment in practical matters The highest form of common sense resulting from accumulated knowledge or erudition (deep, thorough learning) or

enlightenment (education that results in understanding and the dissemination of knowledge) The ability to apply valuable and viable knowledge, experience, understanding, and insight while being prudent and sensible Focused on our own minds The synthesis of our experience, insight, understanding, and knowledge The appropriate use of knowledge to solve human problems

In essence, wisdom entails knowing when and how to apply knowledge. The decision-making process revolves around knowledge and wisdom. It is through efforts to understand the nature of knowledge and its evolution to wisdom that one can conceive of, build, and implement informatics tools that enhance and mimic the mind’s processes to facilitate decision making and job performance.

Cognitive Informatics Wang (2003) describes CI as an emerging transdisciplinary field of study that bridges the gap in understanding regarding how information is processed in the mind and in the computer. Computing and informatics theories can be applied to help elucidate the information processing of the brain, and cognitive and neurologic sciences can likewise be applied to build better and more efficient computer processing systems. Wang suggests that the common issue among the human knowledge sciences is the drive to develop an understanding of natural intelligence and human problem solving.

Pacific Northwest National Laboratory (PNNL), an organization operated on behalf of the U.S. Department of Energy; 2008, suggests the disciplines of neuroscience, linguistics, AI, and psychology constitute this field. It defines CI as “the multidisciplinary study of cognition and information sciences, which investigates human information processing mechanisms and processes and their engineering applications in computing” (para. 1). CI helps to bridge this gap by systematically exploring the mechanisms of the brain and mind and exploring specifically how information is acquired, represented, remembered, retrieved, generated, and communicated. This dawning of understanding can then be applied and modeled in AI situations resulting in more efficient computing applications.

Wang explains further:

Cognitive informatics attempts to solve problems in two connected areas in a bidirectional and multidisciplinary approach. In one direction, CI uses informatics and computing techniques to investigate cognitive science problems, such as memory, learning, and reasoning; in the other direction, CI uses cognitive theories to investigate the problems in informatics, computing, and software engineering. (p. 120)

CI and Nursing Practice According to Mastrian (2008), the recognition of the potential application of principles of cognitive science to NI is relatively new. The traditional and widely accepted definition of NI advanced by Graves and Corcoran (1989) is that NI is a combination of nursing science, computer science, and information science used to describe the processes nurses use to manage data, information, and

knowledge in nursing practice. Turley (1996) proposed the addition of cognitive science to this mix, as nurse scientists are seen to strive to capture and explain the influence of the human brain on data, information, and knowledge processing and to elucidate how these factors in turn affect nursing decision making. The need to include cognitive sciences is imperative as researchers attempt to model and support nursing decision making in complex computer programs.

In 2003, Wang proposed the term cognitive informatics to signify the branch of information and computer sciences that investigates and explains information processing in the human brain. The science of CI grew out of interest in AI, as computer scientists developed computer programs that mimic the information processing and knowledge generation functions of the human brain. CI bridges the gap between artificial and natural intelligence and enhances the understanding of how information is acquired, processed, stored, and retrieved so that these functions can be modeled in computer software.

What does this have to do with nursing? At its very core, nursing practice requires problem solving and decision making. Nurses help people manage their responses to illnesses and identify ways that they can maintain or restore their health. During the nursing process, nurses must first recognize that there is a problem to be solved, identify the nature of the problem, pull information from knowledge stores that is relevant to the problem, decide on a plan of action, implement the plan, and evaluate the effectiveness of the interventions. When a nurse has practiced the science of nursing for some time, he or she tends to do these processes automatically; it is instinctively known what needs to be done to intervene in the problem. What happens, however, if the nurse faces a situation or problem for which he or she has no experience on which to draw? The ever-increasing acuity and complexity of patient situations coupled with the explosion of information in health care has fueled the development of decision support software for nursing. This software models the human and natural decision-making processes of professionals in an artificial program. Such systems can help decision makers to consider the consequences of different courses of action before implementing the action. They also provide stores of information that the user may not be aware of and can use to choose the best course of action and ultimately make a better decision in unfamiliar circumstances.

Decision support programs continue to evolve as research in the fields of cognitive science, AI, and CI is continuously generated and then applied to the development of these systems. Nurses must embrace—not resist—these advances as support and enhancement of the practice of nursing science.

What Is AI? The field of AI deals with the conception, development, and implementation of informatics tools based on intelligent technologies. This field captures the complex processes of human thought and intelligence.

Herbert Simon believes that the field of AI could have two functions: “One is to use the power of computers to augment human thinking, just as we use motors to augment human or horse power. The other is to use a computer’s artificial intelligence to understand how humans think in a humanoid way” (Association for the Advancement of Artificial Intelligence [AAAI], 2007a, para. 1). According to the AAAI (2007b), AI is the “scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines” (para. 2).

John McCarthy, one of the men credited with founding the field of AI in the 1950s, stated that AI “is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable” (AAAI, 2007b, para. 4).

Lamont (2007) interviewed Ray Kurzweil, a visionary who defined AI as “the ability to perform a task that is normally performed by natural intelligence, particularly human natural intelligence. We have in fact artificial intelligence that can perform many tasks that used to require—and could only be done by—human intelligence” (para. 6). The intelligence factor is extremely important in AI and has been defined by McCarthy as “the computational part of the ability to achieve goals in the world. Varying kinds and degrees of intelligence occur in people, many animals, and some machines” (AAAI, 2007b, para. 4).

The challenge of this field rests in capturing, mimicking, and creating the complex processes of the mind in informatics tools, including software, hardware, and other machine technologies, with the goal that the tool be able to initiate and generate its own mechanical thought processing. The brain’s processing is highly intricate and complicated. This complexity is reflected in Cohn’s (2006) comment that “Artificial intelligence is 50 years old this summer, and while computers can beat the world’s best chess players, we still can’t get them to think like a 4-year-old” (para. 1). AI uses cognitive science and computer science to replicate and generate human intelligence. This field will continue to evolve and produce artificially intelligent tools to enhance nurses’ personal and professional lives.

Summary Cognitive science is the interdisciplinary field that studies the mind, intelligence, and behavior from an information processing perspective. CI is a field of study that bridges the gap in understanding regarding how information is processed in the mind and in the computer. Computing and informatics theories can be applied to help elucidate the information processing of the brain, and cognitive and neurologic sciences can likewise be applied to build better and more efficient computer processing systems.

AI is the field that deals with the conception, development, and implementation of informatics tools based on intelligent technologies. This field captures the complex processes of human thought and intelligence. AI uses cognitive science and computer science to replicate and generate human intelligence.

The sources of knowledge, nature of knowledge, and rapidly changing technologies must be harnessed by clinicians to enhance their bedside care. Therefore, we must understand the nature of knowledge, the information and knowledge needed, and the means by which nurses process this information and knowledge in their own situational context. The reason for collecting and building data, information, and knowledge is to be able to build wisdom—that is, the ability to apply valuable and viable knowledge, experience, understanding, and insight while being prudent and sensible. Wisdom is focused on our own minds, the synthesis of our experience,

insight, understanding, and knowledge. Nurses must use their wisdom and make informed, judicious, prudent, and intelligent decisions while providing care to patients, families, and communities. Cognitive science, CI, and AI will continue to evolve to help build knowledge and wisdom.

THOUGHT-PROVOKING QUESTIONS

1. How would you describe CI? Reflect on a plan of care that you have developed for a patient. How could CI be used to create tools to help with this important work?

2. Think of a clinical setting with which you are familiar and envision which AI tools might be applied in this setting. Are there any current tools in use? Which tools would enhance practice in this setting and why?

References Association for the Advancement of Artificial Intelligence (AAAI). (2007a). AI overview. http://www.aaai.org/AITopics/html/overview.html Association for the Advancement of Artificial Intelligence (AAAI). (2007b). Cognitive science.

http://www.aaai.org/aitopics/pmwiki/pmwiki.php/AITopics/CognitiveScience Cognitive Science Society. (2005). CSJ archive. http://www.cogsci.rpi.edu/CSJarchive/1980v04/index.html Cohn, D. (2006). AI reaches the golden years. http://www.wired.com/news/technology/0,71389–0.html Encyclopedia Britannica. (2007). Epistemology. http://www.britannica.com/eb/article-247960/epistemology Graves, J., & Corcoran, S. (1989). The study of nursing informatics. Image: Journal of Nursing Scholarship, 21(4), 227–230. Holt, T. (2006). Sources of knowledge. http://www.theoryofknowledge.info/sourcesofknowledge.html Lamont, I.(2007). The grill: Ray Kurzweil talks about “augmented reality” and the singularity. Retrieved October 2007 from

http://www.computerworld.com/action/article.do?command=viewArticleBasic&articleId=306176 Mastrian, K. (2008, February). Invited editorial: Cognitive informatics and nursing practice. Online Journal of Nursing Informatics, 12(1).

http://ojni.org/12_1/kathy.html Merriam-Webster Online Dictionary. (2007). Logic. http://www.merriam-webster.com/dictionary/logic Pacific Northwest National Laboratory, U.S. Department of Energy. (2008). Cognitive informatics. http://www.pnl.gov/coginformatics/ Shallcross, D. J., & Sisk, D. A. (1999). What is intuition? In T. Arnold (Ed.), Hyponoesis glossary: Intuition.

http://www.hyponoesis.org/Glossary/Definition/Intuition Sivananda, S. (2004). Four sources of knowledge. http://www.dlshq.org/messages/knowledge.htm Stanford Encyclopedia of Philosophy. (2010). Cognitive science. http://plato.stanford.edu/entries/cognitive-science/ Turley, J. (1996). Toward a model for nursing informatics. Image: Journal of Nursing Scholarship, 28(4), 309–313. Wang, Y. (2003). Cognitive informatics: A new transdisciplinary research field. Brain and Mind, 4(2), 115–127. Wikipedia. (2013). Cognitive science. http://en.wikipedia.org/wiki/Cognitive_science

Chapter 5

Ethical Applications of Informatics Kathleen Mastrian, Dee McGonigle, and Nedra Farcus

OBJECTIVES

1. Recognize ethical dilemmas in nursing informatics. 2. Examine ethical implications of nursing informatics. 3. Evaluate professional responsibilities for the ethical use of healthcare informatics technology. 4. Explore the ethical model for ethical decision making. 5. Analyze practical ways of applying the ethical model for ethical decision making to manage ethical dilemmas in nursing informatics.

Key Terms

Alternatives Antiprinciplism Application (app) Autonomy Beneficence Bioethics Bioinformatics Care ethics Casuist approach Confidentiality Consequences Courage Decision making Decision support Duty Ethical decision making Ethical dilemma Ethical, social, and legal implications Ethicist Ethics Eudaemonistic Fidelity Good Google Glass Harm Justice Liberty Moral dilemmas Moral rights Morals Negligence Nicomachean Nonmaleficence Principlism Privacy Rights Security Self-control Smartphones Social media Standard Truth Uncertainty

Values Veracity Virtue Virtue ethics Wisdom

Introduction Those who followed the actual events of Apollo 13, or who were entertained by the movie (Howard, 1995), watched the astronauts strive against all odds to bring their crippled spaceship back to Earth. The speed of their travel was incomprehensible to most viewers, and the task of bringing the spaceship back to Earth seemed nearly impossible. They were experiencing a crisis never imagined by the experts at NASA, and they made up their survival plan moment by moment. What brought them back to Earth safely? Surely, credit must be given to the technology and the spaceship’s ability to withstand the trauma it experienced. Most amazing, however, were the traditional nontechnological tools, skills, and supplies that were used in new and different ways to stabilize the spacecraft’s environment and keep the astronauts safe while traveling toward their uncertain future.

This sense of constancy in the midst of change serves to stabilize experience in many different life events and contributes to the survival of crisis and change. This rhythmic process is also vital to the healthcare system’s stability and survival in the presence of the rapidly changing events of the Knowledge Age. No one can dispute the fact that the Knowledge Age is changing health care in ways that will not be fully recognized and understood for years. The change is paradigmatic, and every expert who addresses this change reminds healthcare professionals of the need to go with the flow of rapid change or be left behind.

As with any paradigm shift, a new way of viewing the world brings with it some of the enduring values of the previous worldview. As health care journeys into the brave new world of digital communications, it brings some familiar tools and skills recognized in the form of values, such as privacy, confidentiality, autonomy, and nonmaleficence. Although these basic values remain unchanged, the standards for living out these values will take on new meaning as health professionals confront new and different moral dilemmas brought on by the adoption of technological tools for information management and knowledge development. Ethical decision-making frameworks will remain constant, but the context for examining these moral issues or ethical dilemmas will become increasingly complex.

This chapter highlights some familiar ethical concepts to consider on the challenging journey into the increasingly complex future of healthcare informatics. Ethics and bioethics are briefly defined, and the evolution of ethical approaches from the Hippocratic ethic era, to principlism, to the current antiprinciplism movement of ethical decision making is examined. New and challenging ethical dilemmas are surfacing in the venture into the unfolding era of healthcare informatics. Also presented in this chapter are findings from some of the more recent literature related to these issues. Readers are challenged to think constantly and carefully about ethics as they become involved in healthcare informatics and to stay abreast of new developments in ethical approaches.

Ethics Ethics is a process of systematically examining varying viewpoints related to moral questions of right and wrong. Ethicists have defined the term in a variety of ways, with each reflecting a basic theoretical philosophic perspective.

Beauchamp and Childress (1994) refer to ethics as a generic term for various ways of understanding and examining the moral life. Ethical approaches to this examination may be normative, presenting standards of right or good action; descriptive, reporting what people believe and how they act; or explorative, analyzing the concepts and methods of ethics.

Husted and Husted (1995) emphasize a practice-based ethics, stating “ethics examines the ways men and women can exercise their power in order to bring about human benefit—the ways in which one can act in order to bring about the conditions of happiness” (p. 3).

Velasquez, Andre, Shanks, and Myer (1987) posed the question, “What is ethics?”, and answered it with the following two-part response: “First, ethics refers to well-based standards of right and wrong that prescribe what humans ought to do, usually in terms of rights, obligations, benefits to society, fairness, or specific virtues” (para. 10), and “Secondly, ethics refers to the study and development of one’s ethical standards” (para. 11).

Regardless of the theoretical definition, common characteristics regarding ethics are its dialectical, goal-oriented approach to answering questions that have the potential for multiple acceptable answers.

Bioethics Bioethics is defined as the study and formulation of healthcare ethics. Bioethics takes on relevant ethical problems experienced by healthcare providers in the provision of care to individuals and groups. Husted and Husted (1995) state the fundamental background of bioethics that forms its essential nature is:

1. The nature and needs of humans as living, thinking beings 2. The purpose and function of the healthcare system in a human society 3. An increased cultural awareness of human beings’ essential moral status (p. 7)

Bioethics emerged in the 1970s as health care began to change its focus from a mechanistic approach of treating disease to a more holistic approach of treating people with illnesses. As technology advanced, recognition and acknowledgment of the rights and the needs of individuals and groups receiving this high-tech care also increased.

In today’s technologically savvy healthcare environment, patients are being prescribed applications (also known simply as apps)

for their smartphones instead of medications in some clinical practices. Patients’ smartphones are being used to interact with them in new ways and to monitor and assess their health in some cases. With apps and add-ons, for example, a provider can see the patient’s ECG immediately, or the patient can monitor his or her ECG and send it to the provider as necessary. A sensor attached to the patient’s mobile device could monitor blood glucose levels.

We are just beginning to realize the vast potential of these mobile devices—and the threats they sometimes pose. Google Glass, for example, can take photos and videos (Stern, 2013) without anyone knowing that this is occurring; in the healthcare environment, such a technological advancement can violate patients’ privacy and confidentiality. Add these evolving developments to healthcare providers’ engagement in social media use with their patients, and it becomes clear that personal and ethical dilemmas abound for nurses in the new über-connected world.

Ethical Issues and Social Media As connectivity has improved owing to emerging technologies, a rapid explosion in the phenomenon known as social media has occurred. Social media are defined as “a group of Internet-based applications that build on the ideological and technological foundations of Web 2.0 and that allow the creation and exchange of user-generated content” (Spector & Kappel, 2012, p. 1). Just as the electronic health record serves as a real-time event in recording patient–provider contact, so the use of social media represents an instantaneous form of communication. Healthcare providers—particularly nurses—can enhance the patient care delivery system, promote professional collegiality, and provide timely communication and education regarding health-related matters by using this forum (“White paper,” 2011, p. 1). In all cases, however, nurses must exercise judicious use of social media to protect patients’ rights. Nurses must understand their obligation to their chosen profession, particularly as it relates to personal behavior and the perceptions of their image as portrayed through social media. Above all, nurses must be mindful that once communication is written and posted on the Internet, there is no way to retract what was written; it is a permanent record that can be tracked, even if the post is deleted (Englund, Chappy, Jambunathan, & Gohdes, 2012, p. 242).

Social media platforms include such electronic communication outlets as Facebook, Twitter, LinkedIn, and YouTube. Other widely used means of instantaneous communications include wikis, blogs, tweeting, Skype, and the “hangout” on Google+. Even as recently as 5 years ago, some of these means of exchanging information were unknown (Spector & Kappel, 2012, p. 1).

Use of social networking has increased dramatically among all age groups, including a 78% increase in use among the 50- to 64- year-old age group, and a 42% increase in use among persons older than 65 years over a time frame of a little more than 3 years. Facebook reported in June 2012 that it had 955 million active monthly users. Twitter’s influence on health care is suggested by the fact that more than 100 million pieces of healthcare information have been tweeted, with as many as 140 million tweets being recorded in a day’s time (Prasad, 2013, p. 492). Moreover, people spend more than 700 billion minutes per month actively engaged with the Facebook site (Miller, 2011, p. 307).

The rapid growth of social media has found many healthcare professionals unprepared to face the new challenges or to exploit the opportunities that exist with these forums. The need to maintain confidentiality presents a major obstacle to the healthcare industry’s widespread adoption of such technology; thus social networking has not yet been fully embraced by many health professionals (Anderson, 2012, p. 1). Englund and colleagues (2012) note that undergraduate nursing students may face ambiguous and understudied professional and ethical implications when using social networking venues.

Another confounding factor is the increased use of mobile devices by health professionals as well as the public (Swartz, 2011, p. 345). The mobile device known as the smartphone has the capability to take still pictures as well as make live recordings; it has found its way into treatment rooms around the globe.

As a consequence of more stringent confidentiality laws and more widespread availability and use of social and mobile media, numerous ethical and legal dilemmas have been posed to nurses. What are not well defined are the expectations of healthcare providers regarding this technology. In some cases, nurses employed in the emergency department (ED) setting have been subjected to video and audio recordings by patients and families when they perform procedures and give care during the ED visit. Nurses would be wise to inquire—before an incident occurs—about the hospital policy regarding audio/video recording by patients and families, as well as the state laws governing two-party consent laws. Such laws require consent of all parties to any recording or eavesdropping activity (Lyons & Reinisch, 2013, p. 54).

Sometimes the enthusiasm for patient care and learning can lead to ethics violations. In one case, an inadvertent violation of privacy laws occurred when a nurse in a small town blogged about a child in her care whom she referred to as her “little handicapper.” The post also noted the child’s age and the fact that the child used a wheelchair. A complaint about this breach of confidentiality was reported to the Board of Nursing. A warning was issued to the nurse blogging this information, although a more stringent disciplinary action could have been taken (Spector & Kappel, 2012, p. 2).

In another case cited by Spector and Kappel (2012), a student nurse cared for a 3-year-old leukemia patient whom she wanted to remember after finishing her pediatric clinical experience. She took the child’s picture, and in the background of the photo the patient’s room number was clearly displayed. The child’s picture was posted on the student nurse’s Facebook page, along with her statement of how much she cared about this child and how proud she was to be a student nurse. Someone forwarded the picture to the nurse supervisor of the children’s hospital. Not only was the student expelled from the program, but the clinical site offer made by the children’s hospital to the nursing school was rescinded. In addition, the hospital faced citations for violations of the Health Insurance Portability and Accountability Act (HIPAA) owing to the student nurse’s transgression (p. 3).

A white paper published by the National Council of State Boards of Nursing (NCSBN; White paper, 2011) provides a thorough discussion of the issues associated with nurses’ use of social media.

Ethical Dilemmas and Morals Ethical dilemmas arise when moral issues raise questions that cannot be answered with a simple, clearly defined rule, fact, or

authoritative view. Morals refer to social convention about right and wrong human conduct that is so widely shared that it forms a stable (although usually incomplete) communal consensus (Beauchamp & Childress, 1994). Moral dilemmas arise with uncertainty, as is the case when some evidence a person is confronted with indicates an action is morally right and other evidence indicates that this action is morally wrong. Uncertainty is stressful and, in the face of inconclusive evidence on both sides of the dilemma, causes the person to question what he or she should do. Sometimes the individual concludes that based on his or her moral beliefs, he or she cannot act. Uncertainty also arises from unanticipated effects or unforeseeable behavioral responses to actions or the lack of action. Adding uncertainty to the situational factors and personal beliefs that must be considered creates a need for an ethical decision-making model to help one choose the best action.

Ethical Decision Making Ethical decision making refers to the process of making informed choices about ethical dilemmas based on a set of standards differentiating right from wrong. This type of decision making reflects an understanding of the principles and standards of ethical decision making, as well as the philosophic approaches to ethical decision making, and it requires a systematic framework for addressing the complex and often controversial moral questions.

Research Brief Using an online survey of 1,227 randomly selected respondents, Bodkin and Miaoulis (2007) sought to describe the characteristics of information seekers on e-health websites, the types of information they seek, and their perceptions of the quality and ethics of the websites. Of the respondents, 74% had sought health information on the Web, with women accounting for 55.8% of the health information seekers. A total of 50% of the seekers were between 35 and 54 years of age. Nearly two thirds of the users began their searches using a general search engine rather than a health-specific site, unless they were seeking information related to symptoms or diseases. Top reasons for seeking information were related to diseases or symptoms of medical conditions, medication information, health news, health insurance, locating a doctor, and Medicare or Medicaid information. The level of education of information seekers was related to the ratings of website quality, in that more educated seekers found health information websites more understandable, but were more likely to perceive bias in the website information. The researchers also found that the ethical codes for e-health websites seem to be increasing consumers’ trust in the safety and quality of information found on the Web, but that most consumers are not comfortable purchasing health products or services online.

Source: The full article appears in Bodkin, C., & Miaoulis, G. (2007). eHealth information quality and ethics issues: An exploratory study of consumer perceptions. International Journal of Pharmaceutical and Healthcare Marketing, 1(1), 27–42. Retrieved from ABI/INFORM Global (Document ID: 1515583081).

As the high-speed era of digital communications evolves, the rights and the needs of individuals and groups will be of the utmost concern to all healthcare professionals. The changing meaning of communication, for example, will bring with it new concerns among healthcare professionals about protecting patients’ rights of confidentiality, privacy, and autonomy. Systematic and flexible ethical decision-making abilities will be essential for all healthcare professionals.

Notably, the concept of nonmaleficence (“do no harm”) will be broadened to include those individuals and groups whom one may never see in person, but with whom one will enter into a professional relationship of trust and care. Mack (2000) has discussed the popularity of individuals seeking information online instead of directly from their healthcare providers and the effects this behavior has on patient–provider relationships. He is emphatic in his reminder that “organizations and individuals that provide health information on the Internet have obligations to be trustworthy, provide high-quality content, protect users’ privacy, and adhere to standards of best practices for online commerce and online professional services in healthcare” (p. 41).

Makus (2001) suggests that both autonomy and justice are enhanced with universal access to information, but that tensions may be created in patient–provider relationships as a result of this access to outside information. Healthcare workers need to realize that they are no longer the sole providers and gatekeepers of health-related information; ideally, they should embrace information empowerment and suggest websites to patients that contain reliable, accurate, and relevant information (Resnick, 2001).

It is clear that patients’ increasing use of the Internet for healthcare information may prompt entirely new types of ethical issues, such as who is responsible if a patient is harmed as a result of following online health advice. Derse and Miller (2008) discuss this issue extensively and conclude that a clear line separates information and practice. Practice occurs when there is direct or personal communication between the provider and the patient, when the advice is tailored to the patient’s specific health issue, and when there is a reasonable expectation that the patient will act in reliance on the information.

A summit sponsored by the Internet Healthcare Coalition (www.ihealthcoalition.org) in 2000 developed the E-Health Code of Ethics (eHealth code, n.d.), which includes eight standards for the ethical development of health-related Internet sites: (1) candor, (2) honesty, (3) quality, (4) informed consent, (5) privacy, (6) professionalism, (7) responsible partnering, and (8) accountability. For more information about each of these standards, access the full discussion of the E-Health Code of Ethics (http://www.ihealthcoalition.org/ehealth-code/).

It is important to realize that the standards for ethical development of health-related Internet sites are voluntary; there is no overseer perusing these sites and issuing safety alerts for users. Although some sites carry a specific symbol indicating that they have been reviewed and are trustworthy (HONcode and Trust-e), the healthcare provider cannot control which information patients access or how they perceive and act related to the health information they find online. The research brief on the previous page describes one study of consumer perceptions of health information on the Web.

Theoretical Approaches to Healthcare Ethics Theoretical approaches to healthcare ethics have evolved in response to societal changes. In a 30-year retrospective article for the Journal of the American Medical Association, Pellegrino (1993) traced the evolution of healthcare ethics from the Hippocratic ethic, to

principlism, to the current antiprinciplism movement. The Hippocratic tradition emerged from relatively homogenous societies where beliefs were similar and most societal members

shared common values. The emphasis was on duty, virtue, and gentlemanly conduct. Principlism arose as societies became more heterogeneous and members began experiencing a diversity of incompatible beliefs

and values; it emerged as a foundation for ethical decision making. Principles were expansive enough to be shared by all rational individuals, regardless of their background and individual beliefs. This approach continued into the 1900s and was popularized by two bioethicists, Beauchamp and Childress (1977, 1994), in the last quarter of the 20th century. Principles are considered broad guidelines that provide guidance or direction but leave substantial room for case-specific judgment. From principles, one can develop more detailed rules and policies.

Beauchamp and Childress (1994) proposed four guiding principles: (1) respect for autonomy, (2) nonmaleficence, (3) beneficence, and (4) justice.

Autonomy refers to the individual’s freedom from controlling interferences by others and from personal limitations that prevent meaningful choices, such as adequate understanding. Two conditions are essential for autonomy: liberty, meaning the independence from controlling influences, and the individual’s capacity for intentional action.

Nonmaleficence asserts an obligation not to inflict harm intentionally and forms the framework for the standard of due care to be met by any professional. Obligations of nonmaleficence are obligations of not inflicting harm and not imposing risks of harm. Negligence—a departure from the standard of due care toward others—includes intentionally imposing risks that are unreasonable and unintentionally but carelessly imposing risks.

Beneficence refers to actions performed that contribute to the welfare of others. Two principles underlie beneficence: Positive beneficence requires the provision of benefits, and utility requires that benefits and drawbacks be balanced. One must avoid negative beneficence, which occurs when constraints are placed on activities that, even though they might not be unjust, could in some situations cause detriment or harm to others.

Justice refers to fair, equitable, and appropriate treatment in light of what is due or owed to a person. Distributive justice refers to fair, equitable, and appropriate distribution in society determined by justified norms that structure the terms of social cooperation.

Beauchamp and Childress also suggest three types of rules for guiding actions: substantive, authority, and procedural. (Rules are more restrictive in scope than principles and are more specific in content.) Substantive rules are rules of truth telling, confidentiality, privacy, and fidelity, and those pertaining to the allocation and rationing of health care, omitting treatment, physician-assisted suicide, and informed consent. Authority rules indicate who may and should perform actions. Procedural rules establish procedures to be followed.

The principlism advocated by Beauchamp and Childress has since given way to the antiprinciplism movement, which emerged in the 21st century with the expansive technological changes and the tremendous rise in ethical dilemmas accompanying these changes. Opponents of principlism include those who claim that its principles do not represent a theoretical approach as well as those who claim that its principles are too far removed from the concrete particularities of everyday human existence; are too conceptual, intangible, or abstract; or disregard or do not take into account a person’s psychological factors, personality, life history, sexual orientation, or religious, ethnic, and cultural background. Different approaches to making ethical decisions are next briefly explored, providing the reader with an understanding of the varied methods professionals may use to arrive at an ethical decision.

The casuist approach to ethical decision making grew out of the call for more concrete methods of examining ethical dilemmas. Casuistry is a case-based ethical reasoning method that analyzes the facts of a case in a sound, logical, and ordered or structured manner. The facts are compared to decisions arising out of consensus in previous paradigmatic or model cases. One casuist proponent, Jonsen (1991), prefers particular and concrete paradigms and analogies over the universal and abstract theories of principlism.

The Husted bioethical decision-making model centers on the healthcare professional’s implicit agreement with the patient or client (Husted & Husted, 1995). It is based on six contemporary bioethical standards: (1) autonomy, (2) freedom, (3) veracity, (4) privacy, (5) beneficence, and (6) fidelity.

The virtue ethics approach emphasizes the virtuous character of individuals who make the choices. A virtue is any characteristic or disposition desired in others or oneself. It is derived from the Greek word aretai, meaning “excellence,” and refers to what one expects of oneself and others. Virtue ethicists emphasize the ideal situation and attempt to identify and define ideals. Virtue ethics dates back to Plato and Socrates. When asked “whether virtue can be taught or whether virtue can be acquired in some other way, Socrates answers that if virtue is knowledge, then it can be taught. Thus, Socrates assumes that whatever can be known can be taught” (Scott, 2002, para. 9). According to this view, the cause of any moral weakness is not a matter of character flaws but rather a matter of ignorance. In other words, a person acts immorally because the individual does not know what is really good for him or her. A person can, for example, be overpowered by immediate pleasures and forget to consider the longterm consequences. Plato emphasized that to lead a moral life and not succumb to immediate pleasures and gratification, one must have a moral vision. He identified four cardinal virtues: (1) wisdom, (2) courage, (3) self-control, and (4) justice.

Aristotle’s Nicomachean principles (Aristotle, 350 BC) also contribute to virtue ethics. According to this philosopher, virtues are connected to will and motive because the intention is what determines if one is or is not acting virtuously. Ethical considerations, according to his eudaemonistic principles, address the question, “What is it to be an excellent person?” For Aristotle, this ultimately means acting in a temperate manner according to a rational mean between extreme possibilities.

Virtue ethics has experienced a recent resurgence in popularity (Healthcare Ethics, 2007). Two of the most influential moral and medical authors, Pellegrino and Thomasma (1993), have maintained that virtue theory should be related to other theories within a comprehensive philosophy of the health professions. They argue that moral events are composed of four elements (the agent, the act, the circumstances, and the consequences), and state that a variety of theories must be interrelated to account for different facets of moral judgment.

Care ethics is responsiveness to the needs of others that dictates providing care, preventing harm, and maintaining relationships.

This viewpoint has been in existence for some time. Engster (n.d.) states that “Carol Gilligan’s In a Different Voice (1982) established care ethics as a major new perspective in contemporary moral and political discourse” (p. 2). The relationship between care and virtue is complex, however. Benjamin and Curtis (1992) base their framework on care ethics; they propose that “critical reflection and inquiry in ethics involves the complex interplay of a variety of human faculties, ranging from empathy and moral imagination on the one hand to analytic precision and careful reasoning on the other” (p. 12). Care ethicists are less stringently guided by rules, but rather focus on the needs of others and the individual’s responsibility to meet those needs. As opposed to the aforementioned theories that are centered on the individual’s rights, an ethic of care emphasizes the personal part of an interdependent relationship that affects how decisions are made. In this theory, the specific situation and context in which the person is embedded become a part of the decision- making process.

The consensus-based approach to bioethics was proposed by Martin (1999), who claims that American bioethics harbors a variety of ethical methods that emphasize different ethical factors, including principles, circumstances, character, interpersonal needs, and personal meaning. Each method reflects an important aspect of ethical experience, adds to the others, and enriches the ethical imagination. Thus working with these methods provides the challenge and the opportunity necessary for the perceptive and shrewd bioethicist to transform them into something new with value through the process of building ethical consensus. Diverse ethical insights can be integrated to support a particular bioethical decision, and that decision can be understood as a new, ethical whole.

Applying Ethics to Informatics With the Knowledge Age has come global closeness, meaning the ability to reach around the globe instantaneously through technology. Language barriers are being broken through technologically based translators that can enhance interaction and exchange of data and information. Informatics practitioners are bridging continents, and international panels, committees, and organizations are beginning to establish standards and rules for the implementation of informatics. This international perspective must be taken into consideration when informatics dilemmas are examined from an ethical standpoint; it promises to influence the development of ethical approaches that begin to accept that healthcare practitioners are working within international networks and must recognize, respect, and regard the diverse political, social, and human factors within informatics ethics.

The various ethical approaches can be used to help healthcare professionals make ethical decisions in all areas of practice. The focus of this text is on informatics. Informatics theory and practice have continued to grow at a rapid rate and are infiltrating every area of professional life. New applications and ways of performing skills are being developed daily. Therefore, education in informatics ethics is extremely important.

Typically, situations are analyzed using past experience and in collaboration with others. Each situation warrants its own deliberation and unique approach, because each individual patient seeking or receiving care has his or her own preferences, quality of life, and healthcare needs in a situational milieu framed by financial, provider, setting, institutional, and social context issues. Clinicians must take into consideration all of these factors when making ethical decisions.

The use of expert systems, decision support tools, evidence-based practice, and artificial intelligence in the care of patients creates challenges in terms of who should use these tools, how they are implemented, and how they are tempered with clinical judgment. All clinical situations are not the same, and even though the result of interacting with these systems and tools is enhanced information and knowledge, the clinician must weigh this information in light of each patient’s unique clinical circumstances, including that individual’s beliefs and wishes. Patients are demanding access to quality care and the information necessary to control their lives. Clinicians need to analyze and synthesize the parameters of each distinctive situation using a specific decision-making framework that helps them make the best decisions. Getting it right the first time has a tremendous impact on expected patient outcomes. The focus should remain on patient outcomes while the informatics tools available are ethically incorporated.

Facing ethical dilemmas on a daily basis and struggling with unique client situations may cause many clinicians to question their own actions and the actions of their colleagues and patients. One must realize that colleagues and patients may reach very different decisions, but that does not mean anyone is wrong. Instead, all parties reach their ethical decision based on their own review of the situational facts and understanding of ethics. As one deals with diversity among patients, colleagues, and administrators, one must constantly strive to use ethical imagination to reach ethically competent decisions.

Balancing the needs of society, his or her employer, and patients could cause the clinician to face ethical challenges on an everyday basis. Society expects judicious use of finite healthcare resources. Employers have their own policies, standards, and practices that can sometimes inhibit the practice of the clinician. Each patient is unique and has life experiences that affect his or her healthcare perspective, choices, motivation, and adherence. Combine all of these factors with the challenges posed by informatics, and it is clear that the evolving healthcare arena calls for an informatics-competent, politically active, consumer-oriented, business-savvy, ethical clinician to rule this ever-changing landscape known as health care.

The goal of any ethical system should be that a rational, justifiable decision is reached. Ethics is always there to help the practitioner decide what is right. Indeed, the measure of an adequate ethical system or theory or approach is, in part, its ability to be useful in novel contexts. A comprehensive, robust theory of ethics should be up to the task of addressing a broad variety of new applications and challenges at the intersection of informatics and health care.

The information concerning an ethical dilemma must be viewed in the context of the dilemma to be useful. Bioinformatics could gather, manipulate, classify, analyze, synthesize, retrieve, and maintain databases related to ethical cases, the effective reasoning applied to various ethical dilemmas, and the resulting ethical decisions. This input would certainly be potent—but the resolution of dilemmas cannot be achieved simply by examining relevant cases from a database. Instead, clinicians must assess each situational context and the patient’s specific situation and needs and make their ethical decisions based on all of the information they have at hand.

Ethics is exciting, and competent clinicians need to know about ethical dilemmas and solutions in their professions. Ethicists have often been thought of as experts in the arbitrary, ambiguous, and ungrounded judgments of other people. They know that they make the best decisions they can based on the situation and stakeholders at hand. Just as clinicians try to make the best healthcare decisions with and for their patients, ethically driven practitioners must do the same. Each healthcare provider must critically think through the

situation to arrive at the best decision. To make ethical decisions about informatics technologies and patients’ intimate healthcare data and information, the healthcare

provider must be competent in informatics. To the extent that information technology is reshaping healthcare practices or promises to improve patient care, healthcare professionals must be trained and competent in the use of these tools. This competency needs to be evaluated through instruments developed by professional groups or societies; such assessment will help with consistency and quality. For the healthcare professional to be an effective patient advocate, he or she must understand how information technology affects the patient and the subsequent delivery of care. Information science and its effects on health care are both interesting and important. It follows that information technology and its ethical, social, and legal implications should be incorporated into all levels of professional education.

The need for confidentiality was perhaps first articulated by Hippocrates; thus, if anything is different in today’s environment, it is simply the ways in which confidentiality can be violated. Perhaps the use of computers for clinical decision support and data mining in research will raise new ethical issues. Ethical dilemmas associated with the integration of informatics must be examined to provide an ethical framework that considers all of the stakeholders. Patients’ rights must be protected in the face of a healthcare provider’s duty to his or her employer and society at large when initiating care and assigning finite healthcare resources. An ethical framework is necessary to help guide healthcare providers in reference to the ethical treatment of electronic data and information during all stages of collection, storage, manipulation, and dissemination. These new approaches and means come with their own ethical dilemmas. Often they are dilemmas not yet faced owing to the cutting-edge nature of these technologies.

Just as processes and models are used to diagnose and treat patients in practice, so a model in the analysis and synthesis of ethical dilemmas or cases can also be applied. An ethical model for ethical decision making (Box 5-1) facilitates the ability to analyze the dilemma and synthesize the information into a plan of action (McGonigle, 2000). The model presented here is based on the letters in the word ethical. Each letter guides and prompts the healthcare provider to think critically (think and rethink) through the situation presented. The model is a tool because, in the final analysis, it allows the nurse objectively to ascertain the essence of the dilemma and develop a plan of action.

BOX 5-1 ETHICAL MODEL FOR ETHICAL DECISION MAKING Examine the ethical dilemma (conflicting values exist). Thoroughly comprehend the possible alternatives available. Hypothesize ethical arguments. Investigate, compare, and evaluate the arguments for each alternative. Choose the alternative you would recommend. Act on your chosen alternative. Look at the ethical dilemma and examine the outcomes while reflecting on the ethical decision.

APPLYING THE ETHICAL MODEL Examine the ethical dilemma: Use your problem-solving, decision-making, and critical-thinking skills. What is the dilemma you are analyzing? Collect as much information about the dilemma as you can, making sure to gather the relevant facts that

clearly identify the dilemma. You should be able to describe the dilemma you are analyzing in detail. Ascertain exactly what must be decided. Who should be involved in the decision-making process for this specific case? Who are the interested players or stakeholders? Reflect on the viewpoints of these key players and their value systems. What do you think each of these stakeholders would like you to decide as a plan of action for this dilemma? How can you generate the greatest good?

Thoroughly comprehend the possible alternatives available: Use your problem-solving, decision-making, and critical-thinking skills. Create a list of the possible alternatives. Be creative when developing your alternatives. Be open minded; there is more than one way to reach a

goal. Compel yourself to discern at least three alternatives. Clarify the alternatives available and predict the associated consequences—good and bad—of each potential alternative or intervention. For each alternative, ask the following questions:

Do any of the principles or rules, such as legal, professional, or organizational, automatically nullify this alternative? If this alternative is chosen, what do you predict as the best-case and worst-case scenarios? Do the best-case outcomes outweigh the worst-case outcomes? Could you live with the worst-case scenario? Will anyone be harmed? If so, how will they be harmed? Does the benefit obtained from this alternative overcome the risk of potential harm that it could cause to anyone?

Hypothesize ethical arguments: Use your problem-solving, decision-making, and critical-thinking skills. Determine which of the five approaches apply to this dilemma. Identify the moral principles that can be brought into play to support a conclusion as to what ought to be done ethically in this case or similar

cases. Ascertain whether the approaches generate converging or diverging conclusions about what ought to be done.

Investigate, compare, and evaluate the arguments for each alternative: Use your problem-solving, decision-making, and critical-thinking skills. Appraise the relevant facts and assumptions prudently.

Is there ambiguous information that must be evaluated? Are there any unjustifiable factual or illogical assumptions or debatable conceptual issues that must be explored?

Rate the ethical reasoning and arguments for each alternative in terms of their relative significance.

4 = extreme significance 3 = major significance 2 = significant 1 = minor significance

Compare and contrast the alternatives available with the values of the key players involved. Reflect on these alternatives:

Does each alternative consider all of the key players? Does each alternative take into account and reflect an interest in the concerns and welfare of all of the key players? Which alternative will produce the greatest good or the least amount of harm for the greatest number of people?

Refer to your professional codes of ethical conduct. Do they support your reasoning?

Choose the alternative you would recommend: Use your problem-solving, decision-making, and critical-thinking skills. Make a decision about the best alternative available.

Remember the Golden Rule: Does your decision treat others as you would want to be treated? Does your decision take into account and reflect an interest in the concerns and welfare of all of the key players? Does your decision maximize the benefit and minimize the risk for everyone involved?

Become your own critic; challenge your decision as you think others might. Use the ethical arguments you predict they would use and defend your decision.

Would you be secure enough in your ethical decision-making process to see it aired on national television or sent out globally over the Internet?

Are you secure enough with this ethical decision that you could have allowed your loved ones to observe your decision-making process, your decision, and its outcomes?

Act on your chosen alternative: Use your problem-solving, decision-making, and critical-thinking skills. Formulate an implementation plan delineating the execution of the decision.

This plan should be designed to maximize the benefits and minimize the risks. This plan must take into account all of the resources necessary for implementation, including personnel and money.

Implement the plan.

Look at the ethical dilemma and examine the outcomes while reflecting on your ethical decision: Use your problem-solving, decision-making, and critical-thinking skills. Monitor the implementation plan and its outcomes. It is extremely important to reflect on specific case decisions and evaluate their outcomes to

develop your ethical decision-making ability. If new information becomes available, the plan must be reevaluated. Monitor and revise the plan as necessary.

Source: The ethical model for ethical decision making was developed by Dr. Dee McGonigle and is the property of Educational Advancement Associates (EAA). The permission for its use in this text has been granted by Mr. Craig R. Goshow, Vice President, EAA.

Case Analysis Demonstration The following case study is intended to help readers think through how to apply the ethical model. Review the model and then read through the case. Try to apply the model to this case or follow along as the model is implemented. Readers are challenged to determine their decision in this case and then compare and contrast their response with the decision the authors reached. Several more case studies presented for practice in implementing the ethical model for ethical decision making are available on the companion website for this text (http://nursing.jbpub.com/informatics).

Allison is a charge nurse on a busy medical–surgical unit. She is expecting the clinical instructor from the local university at 2:00 pm to review and discuss potential patient assignments for the nursing students scheduled for the following day. Just as the university professor arrives, one of the patients on the unit develops a crisis requiring Allison’s attention. To expedite the student nurse assignments for the following day, Allison gives her electronic medical record access password to the instructor.

Examine the Ethical Dilemma

Allison made a commitment to meet with the university instructor to develop student assignments at 2:00 pm. The patient emergency that developed prevented Allison from living up to that commitment. Allison had an obligation to provide patient care during the emergency and a competing obligation to the professor. She solved the dilemma of competing obligations by providing her electronic medical record access password to the university professor.

By sharing her password, Allison most likely violated hospital policy related to the security of healthcare information. She may also have violated the American Nurses Association code of ethics, which states that nurses must judiciously protect information of a confidential nature. Because the university professor was also a nurse and had a legitimate interest in the protected healthcare information, there might not be a code of ethics violation.

Thoroughly Comprehend the Possible Alternatives Available

The possible alternatives available include the following: (1) Allison could have asked the professor to wait until the patient crisis was resolved; (2) Allison could have delegated another staff member to assist the university professor; or (3) Allison could have logged on to the system for the professor.

Hypothesize Ethical Arguments

The utilitarian approach applies to this situation. An ethical action is one that provides the greatest good for the greatest number; the underlying principles in this perspective are beneficence and nonmaleficence. The rights to be considered are as follows: right of the individual to choose for himself or herself (autonomy); right to truth (veracity); right of privacy (the ethical right to privacy avoids conflict and, like all rights, promotes harmony); right not to be injured; and right to what has been promised (fidelity).

Does the action respect the moral rights of everyone? The principles to consider are autonomy, veracity, and fidelity. As for the fairness or justice, how fair is an action? Does it treat everyone in the same way, or does it show favoritism and

discrimination? The principles to consider are justice and distributive justice. Thinking about the common good assumes one’s own good is inextricably linked to good of the community; community members

are bound by pursuit of common values and goals and ensure that the social policies, social systems, institutions, and environments on which one depends are beneficial to all. Examples of such outcomes are affordable health care, effective public safety, a just legal system, and an unpolluted environment. The principle of distributive justice is considered.

Virtue assumes that one should strive toward certain ideals that provide for the full development of humanity. Virtues are attitudes or character traits that enable one to be and to act in ways that develop the highest potential; examples include honesty, courage, compassion, generosity, fidelity, integrity, fairness, self-control, and prudence. Like habits, virtues become a characteristic of the person. The virtuous person is the ethical person. Ask yourself, what kind of person should I be? What will promote the development of character within myself and my community? The principles considered are fidelity, veracity, beneficence, nonmaleficence, justice, and distributive justice.

In this case, there is a clear violation of an institutional policy designed to protect the privacy and confidentiality of medical records. However, the professor had a legitimate interest in the information and a legitimate right to the information. Allison trusted that the professor would not use the system password to obtain information outside the scope of the legitimate interest. However, Allison cannot be sure that the professor would not access inappropriate information. Further, Allison is responsible for how her access to the electronic system is used. Balancing the rights of everyone—the professor’s right to the information, the patients’ rights to expect that their information is safeguarded, and the right of the patient in crisis to expect the best possible care—is important and is the crux of the dilemma. Does the patient care obligation outweigh the obligation to the professor? Yes, probably. Allison did the right thing by caring for the patient in crisis. By giving out her system access password, Allison also compromised the rights of the other patients on the unit to expect that their confidentiality and privacy would be safeguarded.

Virtue ethics suggests that individuals use power to bring about human benefit. One must consider the needs of others and the responsibility to meet those needs. Allison must simultaneously provide care, prevent harm, and maintain professional relationships.

Allison may want to effect a long-term change in hospital policy for the common good. It is reasonable to assume that this event was not an isolated incident and that the problem may recur in the future. Can the institutional policy be amended to provide professors with access to the medical records system? As suggested in the HIPAA administrative guidelines, the professor could receive the same staff training regarding appropriate and inappropriate use of access and sign the agreement to safeguard the records. If the institution has tracking software, the professor’s access could be monitored to watch for inappropriate use.

Identify the moral principles that can be brought into play to support a conclusion as to what ought to be done ethically in this case or similar cases. The International Council of Nurses (2006) code of ethics states that “The nurse holds in confidence personal information and uses judgment in sharing this information” (p. 4). The code also states, “The nurse uses judgment in relation to individual competence when accepting and delegating responsibilities” (p. 5). Both of these statements apply to the current situation.

Ascertain whether the approaches generate converging or diverging conclusions about what ought to be done. From the analysis, it is clear that the best immediate solution is to delegate assisting the professor with assignments to another nurse on the unit.

Investigate, Compare, and Evaluate the Arguments for Each Alternative

Review and think through the items listed in Table 5-1.

TABLE 5-1 DETAILED ANALYSIS OF ALTERNATIVE ACTIONS

Choose the Alternative You Would Recommend

The best immediate solution is to delegate another staff member to assist the professor. The best long-term solution is to change the hospital policy to include access for professors, as described previously.

Act on Your Chosen Alternative

Allison should delegate another staff member to assist the professor in making assignments.

Look at the Ethical Dilemma and Examine the Outcomes While Reflecting on the Ethical Decision

As already indicated in the alternative analyses, delegation may not be an ideal solution because the staff nurse who is assigned to assist the professor may not possess the same extensive information about all of the patients as the charge nurse. It is, however, the best immediate solution to the dilemma and is certainly safer than compromising the integrity of the hospital’s computer system. As noted previously, Allison may want to pursue a long-term solution to a potentially recurring problem by helping the professor gain legitimate access to the computer system with the professor’s own password. The system administrator would then have the ability to track who used the system and which types of information were accessed during use.

This case analysis demonstration provides the authors’ perspective on this case and the ethical decision made. If your decision did not match this perspective, what was the basis for the difference of opinion? If you worked through the model, you might have reached a different decision based on your individual background and perspective. This does not make the decision right or wrong. A decision should reflect the best decision one can make given review, reflection, and critical thinking about this specific situation.

Six additional cases are provided in the online learner’s manual for review. Apply the model to each case study, and discuss these cases with colleagues or classmates.

New Frontiers in Ethical Issues The expanding use of new information technologies in health care will bring about new and challenging ethical issues. Consider that patients and healthcare providers no longer have to be in the same place for a quality interaction. How, then, does one deal with licensing issues if the electronic consultation takes place across a state line? Derse and Miller (2008) describe a second-opinion medical consultation on the Internet where the information was provided to the referring physician and not to the patient, thus avoiding the licensing issue.

Consider also the ethical issues created by genomic databases or by sharing of information in a health information exchange to promote population health. Alpert (2008) asks, “Is it wise to put genomic sequence data into electronic medical records that are poorly protected, that cannot adhere well to Fair Information Practice Principles for privacy, and that can potentially be seen by tens of thousands of people/entities, when it is clear that we do not understand the functionality of the genome and likely will not for several years?” (p. 382).

Further, how does one really obtain informed consent for such data collection, when how the data will ultimately be used is not

known, but clearly that application will be important to health research uses that go beyond the immediate medical care of the patient? Angst (2009) asks whether the public good outweighs individual interests in such a case because the information contained in these databases is important to developing new understandings and creating new knowledge by matching data in aggregated pools: “Thus, science adds meaning and context to data, but to what extent do we agree to make the data available such that this discovery process can take place, and are the impacts of discovery great enough to justify the risks?” (p. 172). Further, if a voluntary system where patients can opt out of such data collection is adopted, then are healthcare disparities related to incomplete electronic health records created?

In an ideal world, healthcare professionals must not be affected by conflicting loyalties; nothing should interfere with judicious, ethical decision making. As the technologically charged waters of health care are navigated, one must hone a solid foundation of ethical decision making and practice it consistently.

Summary As science and technology advance, and policy makers and healthcare providers continue to shape healthcare practices including information management, it is paramount that ethical decisions are made. Healthcare professionals are typically honest, trustworthy, and ethical, and they understand that they are duty bound to focus on the needs and rights of their patients. At the same time, their day- to-day work is conducted in a world of changing healthcare landscapes populated by new technologies, diverse patients, varied healthcare settings, and changing policies set by their employers, insurance companies, and providers. Healthcare professionals need to juggle all of these balls simultaneously, a task that often results in far too many gray areas or ethical decision-making dilemmas with no clear correct course of action.

Patients rely on the ethical competence of their healthcare providers, believing that their situation is unique and will be respected and evaluated based on their own needs, abilities, and limitations. The healthcare professional cannot allow conflicting loyalties to interfere with judicious, ethical decision making. Just as in the opening example of the Apollo mission, it is uncertain where this technologically heightened information era will lead, but if a solid foundation of ethical decision making is relied upon, duties and rights will be judiciously and ethically fulfilled.

THOUGHT-PROVOKING QUESTIONS

1. Identify moral dilemmas in healthcare informatics that would best be approached with the use of an ethical decision-making framework, such as the use of smartphones to interact with patients as well as to monitor and assess patient health.

2. Discuss the evolving healthcare ethics traditions within their social and historical contexts. 3. Differentiate among the theoretical approaches to healthcare ethics as they relate to the theorists’ perspectives of individuals and their

relationships. 4. Select one of the healthcare ethics theories and support its use in examining ethical issues in healthcare informatics. 5. Select one of the healthcare ethics theories and argue against its use in examining ethical issues in healthcare informatics.

References Alpert, S. (2008). Privacy issues in clinical genomic medicine, or Marcus Welby, M.D., meets the $1000 genome. Cambridge Quarterly of Healthcare

Ethics, 17(4), 373–384. Retrieved from Health Module (Document ID: 1880623501). Anderson, K. (2012, September). Patient care and social media. Australian Nursing Journal, 22. http://go.galegroup.com.silk.library.umass.edu/ps/i.do?

id=GALE%CA301964363&v=2.1&u=mlin_w_umassamh&it=r&p+AONE&sw=w Angst, C. (2009). Protect my privacy or support the common-good? Ethical questions about electronic health information exchanges. Journal of

Business Ethics, 90(suppl), 169–178. Retrieved from ABI/INFORM Global (Document ID: 2051417481). Aristotle. (350 BC). Nichomachean ethics. Book I. (W. D. Ross, Trans.). Retrieved from http://www.constitution.org/ari/ethic_00.htm Beauchamp, T. L., & Childress, J. F. (1977). Principles of biomedical ethics. New York, NY: Oxford University Press. Beauchamp, T. L., & Childress, J. F. (1994). Principles of biomedical ethics (4th ed.). New York, NY: Oxford University Press. Benjamin, M., & Curtis, J. (1992). Ethics in nursing (3rd ed.). New York, NY: Oxford University Press. Derse, A., & Miller, T. (2008). Net effect: Professional and ethical challenges of medicine online. Cambridge Quarterly of Healthcare Ethics, 17(4),

453–464. Retrieved from Health Module (Document ID: 1540615461). eHealth code. (n.d.). http://www.ihealthcoalition.org/ehealth-code/ Englund, H., Chappy, S., Jambunathan, J., & Gohdes, E. (2012, November/December). Ethical reasoning and online social media. Nurse Educator, 37,

242–247. http://dx.doi.org/10.1097/NNE.0b013e31826f2c04 Engster, D. (n.d.). Can care ethics be institutionalized? Toward a caring natural law theory.

https://lmstest.manhattan.edu/pluginfile.php/39833/mod_resource/content/1/Engster%20Care%20and%20Political%20Theory.pdf Healthcare Ethics. (2007). Virtue ethics. http://www.ascensionhealth.org/ethics/public/issues/virtue.asp Howard, R. (Director). (1995). Apollo 13 [Motion picture]. Universal City, CA: MCA Universal Studios. Husted, G. L., & Husted, J. H. (1995). Ethical decision-making in nursing (2nd ed.). New York, NY: Mosby. International Council of Nurses (ICN). (2006). The ICN code of ethics for nurses. http://www.icn.ch/icncode.pdf Jonsen, A. R. (1991). Casuistry as methodology in clinical ethics. Theoretical Medicine, 12, 295–307. Lyons, R., & Reinisch, C. (2013). The legal and ethical implications of social media in the emergency department. Advanced Emergency Nursing

Journal, 35(1), 53–56. http://dx.doi.org/10.1097/TME.0b013e31827a4926 Mack, J. (2000). Patient empowerment, not economics, is driving e-health: Privacy and ethics issues need attention too! Frontiers of Health Services

Management, 17(1), 39–43; discussion 49–51. Retrieved from ABI/INFORM Global (Document ID: 59722384). Makus, R. (2001). Ethics and Internet healthcare: An ontological reflection. Cambridge Quarterly of Healthcare Ethics, 10(2), 127–136. Retrieved from

Health Module (Document ID: 1409693941). Martin, P. A. (1999). Bioethics and the whole: Pluralism, consensus, and the transmutation of bioethical methods into gold. Journal of Law, Medicine &

Ethics, 27(4), 316–327.

McGonigle, D. (2000). The ethical model for ethical decision making. Inside Case Management, 7(8), 1–5. Miller, L. A. (2011). Social media: Friend and foe. Journal of Perinatal and Neonatal Nursing, 307–309.

http://dx.doi.org/10.1097/JPN.0b013e31823506e9 Pellegrino, E. D. (1993). The metamorphosis of medical ethics: A thirty-year retrospective. Journal of the American Medical Association, 269, 1158–

1162. Pellegrino, E., & Thomasma, D. (1993). The virtues in medical practice. New York, NY: Oxford University Press. Prasad, B. (2013). Social media, health care, and social networking. Gastrointestinal Endoscopy, 77, 492–495. Resnik, D. (2001). Patient access to medical information in the computer age: Ethical concerns and issues. Cambridge Quarterly of Healthcare Ethics,

10(2), 147–154; discussion 154–156. Retrieved from Health Module (Document ID: 1409693961). Scott, A. (2002). Plato’s Meno. http://www.angelfire.com/md2/timewarp/plato.html Spector, N., & Kappel, D. M. (2012). Guidelines for using electronic and social media. Online Journal of Nursing, 17.

http://www.medscape.com/viewarticle/780050 Stern, J. (2013). Google Glass: What you can and can’t do with Google’s wearable computer. http://abcnews.go.com/Technology/google-glass-googles-

wearable-gadget/story?id=19091948 Swartz, M. K. (2011, November/December). The potential for social media. Journal of Pediatric Health Care, 25, 345. Velasquez, M., Andre, C., Shanks, T., & Myer, M. (for the Markkula Center for Applied Ethics). (1987). What is ethics?

http://www.scu.edu/SCU/Centers/Ethics/practicing/decision/whatisethics.shtml White paper: A nurse’s guide to the use of social media. (2011). www.ncsbn.org

Section II

Perspectives on Nursing Informatics

Chapter 6 Overview of Nursing Informatics Chapter 7 Informatics Roles and the Knowledge Work of Nursing Chapter 8 Information and Knowledge Needs of Nurses in the 21st Century Chapter 9 Legislative Aspects of Nursing Informatics: HITECH and HIPAA

Nursing informatics (NI) is the synthesis of nursing science, information science, computer science, and cognitive science for the purpose of managing and enhancing healthcare data, information, knowledge, and wisdom to improve patient care and the nursing profession. In the Building Blocks of Nursing Informatics section, the reader learned about the four sciences of NI, also referred to as the four building blocks, and the ethical application of these sciences to manage patient information. Nursing knowledge workers must be able to understand the evolving specialty of NI to harness and use the tools available for managing the vast amount of healthcare data and information. It is essential that NI capabilities be appreciated, promoted, expanded, and advanced to facilitate the work of the nurse, improve patient care, and enhance the nursing profession.

This section presents the perspectives of nursing experts on NI. The Overview of Nursing Informatics chapter begins this exploration. In the Informatics Roles and the Knowledge Work of Nursing chapter, the reader learns about NI roles, competencies, and skills. The Information and Knowledge Needs of Nurses in the 21st Century chapter considers the evolving NI needs of nurses and the development of standardized terminologies in NI. The Legislative Aspects of Nursing Informatics: HITECH and HIPAA chapter follows.

In the Overview of Nursing Informatics chapter, interrelationships among major NI concepts are discussed. As data are transformed into information and information into knowledge, increasing complexity and interrelationships ensue. The boundaries between concepts can become blurred, and feedback loops from one concept level to another evolve. Structured languages and human– computer interaction concepts, which are critical elements for NI, are noted in this chapter. Taxonomies and other current structured languages for nursing are listed. Human–computer interaction concepts are briefly defined and discussed because they are critical to the success of informatics solutions. Importantly, the construct of decision making is added to the traditional nursing metaparadigms: nurse, person, health, and environment. Decision making is not only at the crux of nursing practice in all settings and roles, but it is a fundamental concern of NI. The work of nursing is centered on the concepts of NI: data, information, knowledge, and wisdom. Information technology per se is not the focus; it is the information that the technology conveys that is central. Moreover, NI is no longer the domain of experts in the field. More interestingly, one does not need technology to perform informatics. The centerpiece of informatics is the manipulation of data, information, and knowledge, especially related to decision making in any aspect of nursing or in any setting. In a way, nurses are all already informatics nurses.

The Informatics Roles and the Knowledge Work of Nursing chapter discusses NI as a relatively new nursing specialty that combines the building block sciences covered earlier in the text. Combining these sciences results in nurses being able to care for their patients effectively and safely because the information that they need is readily available. Nurses have been actively involved in NI since computers were introduced into health care. With the advent of electronic health records, it became apparent that nursing needed to develop its own language for this evolving field. NI was instrumental in assisting in nursing language development. The healthcare industry employs the largest number of knowledge workers—a fact that has resulted in the realization that healthcare administrators must begin to change the way they look at their employees. Nurses and physicians are bright, highly skilled, and dedicated to giving the best patient care. Administrators who tap into this wealth of knowledge find that patient care becomes safer and more efficient.

NI is governed by standards established by the American Nurses Association and is a very diverse field, which results in many nurse informaticist specialists becoming focused on one segment of NI. Although NI is a recognized specialty area of practice, in the future all nurses will be expected to have some knowledge of the field. NI competencies have been developed to ensure that all entry- level nurses are ready to enter a field that is becoming more technologically advanced. The competencies may also be used to determine the educational needs of currently practicing nurses. Nurse informatics specialists no longer have to enter the field solely through on-the-job exposure, but can now obtain an advanced degree in NI at many well-established universities throughout the United States. NI has grown tremendously as a specialty since its inception and is predicted to continue growing.

The Information and Knowledge Needs of Nurses in the 21st Century chapter notes that the core concepts and competencies of informatics are particularly well suited to a model of interprofessional education. Ideally, when educational programs are emulating clinical settings, informatics knowledge should be integrated with the processes of interprofessional teams and decision making. Because simulation laboratories are becoming increasingly common fixtures in the delivery of health-related professional education, they provide a perfect opportunity to incorporate the electronic health records applications. The learning laboratory for nursing education will then more closely approximate the information technology–enabled clinical settings that are emerging in the real world. A presumption is often made that future graduates will be more computer literate than nurses currently in practice. Although this may be true, computer literacy or comfort does not equate to an understanding of the facilitative and transformative role of information

technology. It is essential that the future curricula of basic nursing programs embed the concepts of the role of information technology in supporting clinical care delivery. The need for standardizing nursing terminology is also discussed in this chapter as a way to improve the clinical support functions of the electronic health record.

Equally important in informatics practice is a thorough understanding of current legislation and regulations that shape 21st century practice. The Legislative Aspects of Nursing Informatics: HITECH and HIPAA chapter provides insights into HIPAA rules and an overview of the rules associated with technology implementation as defined by the HITECH Act. The information provided in this text reflects current rules that were in effect at the time of publication. The reader should follow the rules development and evolution of informatics legislation at the U.S. Department of Health and Human Services website (www.hhs.gov) to obtain the most current information related to health information management.

There is an emerging global focus on information technology to support clinical care and on the potential benefits for clinicians and patients. In the future, nurses will likely have sufficient computing power at their disposal to aggregate and transform additional multidimensional data and information sources (e.g., historical, multisensory, experiential, genetic) into a clinical information system to engage with individuals, families, and groups in ways not yet imagined. Every nurse’s practice will make contributions to new nursing knowledge in these dynamically interactive clinical information system environments. With the right tools to support the management of data, complex information processing, and ready access to knowledge, the core concepts and competencies associated with informatics will be embedded in the practice of every nurse, whether administrator, researcher, educator, or practitioner. Information technology is not a panacea, but it provides the profession with unprecedented capacity to generate and disseminate new knowledge more rapidly.

The material within this text is placed within the context of the Foundation of Knowledge model (Figure II-1) to meet the needs of healthcare delivery systems, organizations, patients, and nurses. Through involvement in NI and learning about this evolving specialty, one will be able to use the current theories, architecture, and tools, while beginning to challenge what is known. This questioning and search for what could be will provide the basis for the future landscape of nursing. By using the Foundation of Knowledge model as an organizing framework for this text, the authors have attempted to capture this process.

Figure II-1 Foundation of Knowledge Model Source: Designed by Alicia Mastrian.

In this section, the reader learns about NI. Those readers who are beginning their education will consciously focus on input and knowledge acquisition, trying to glean as much information and knowledge as possible. As these readers become more comfortable in their clinical setting and with nursing science, they will begin to take over some of the other knowledge functions. Experienced nurses, also known as “seasoned nurses,” question what is known and search for ways to enhance their knowledge and the knowledge of others. What is not available must be created. It is through these leaders, researchers, or clinicians that new knowledge is generated and disseminated and nursing science is advanced. Sometimes, however, to keep up with the explosion of information in nursing and health care, one must continue to rely on the knowledge generated and disseminated by others. In this sense, nurses are committed to lifelong learning and the use of knowledge in the practice of nursing science. How nurses interact within their environment and apply what is learned depends on their placement in the Foundation of Knowledge model.

Readers of this section are challenged to ask how they can (1) apply knowledge gained from the practice setting to benefit patients and enhance their practice, (2) help colleagues and patients understand and use current technology, and (3) use wisdom to help create the theories, tools, and knowledge of the future.

Chapter 6

overview of nursing informatics Ramona Nelson and Nancy Staggers

OBJECTIVES

1. Define nursing informatics and key terminology. 2. Explore nursing informatics metastructures, concepts, and tools. 3. Analyze the sciences underpinning nursing informatics and their relationship to nursing informatics practice. 4. Describe phenomena of nursing.

Key Terms

Data Decision support system Ergonomics Expert system Human–computer interaction Informatics nurse Informatics nurse specialist Informatics solution Information Knowledge Nanotechnology Nursing informatics Usability Wisdom

Introduction Nurses in all settings and areas of practice are considered knowledge workers. The foundations of nursing informatics (NI) conceptualize the process of knowledge generation in nursing. The conceptual framework underpinning the science and practice of NI centers on the concepts of data, information, knowledge, and wisdom. These salient concepts are described in this chapter.

The following quote was crafted by a panel of NI experts as they revised the metastructures portion of the American Nurses Association’s (ANA) Scope and Standards for Nursing Informatics Practice:

Nursing informatics (NI) is a specialty that integrates nursing science, computer science, and information science to manage and communicate data, information, knowledge and wisdom in nursing practice. NI supports consumers, patients, nurses, and other providers in their decision-making in all roles and settings. This support is accomplished through the use of information structures, information processes, and information technology.

The goal of NI is to improve the health of populations, communities, families, and individuals by optimizing information management and communication. These activities include the design and use of informatics solutions, and/or technology to support all areas of nursing, including, but not limited to, the direct provision of care, establishing effective administrative systems, designing useful decision support systems, managing and delivering educational experiences, enhancing life-long learning, and supporting nursing research. (ANA, 2008, p. 1) (Source: © 2008 American Nurses Association. Reprinted with permission. All Rights Reserved.)

Within the scope and standards document, the term “individuals refer[s] to patients, healthcare consumers and any other recipient of nursing care or informatics solutions. The term patient refers to consumers in both a wellness and illness model” (ANA, 2008, p. 1). The authors thank Paulette Fraser, MS, RN, BC, for her work as coleader of the metastructures section of the ANA’s 2007 revision of the Scope and Standards for Nursing Informatics Practice.

The following passage is reprinted with permission of the ANA. Boldface type has been applied to key terms, and figure and table

numbers have been changed to correspond to this chapter.

The conceptual framework for NI is based on work by Graves and Corcoran (1989), who provided the initial definition and description of data, information, and knowledge as these terms apply to the science and practice of NI. Nelson (Nelson, 2002; Nelson & Joos, 1989) and Joos (Joos, Nelson, & Smith, 2010) added the concept of wisdom and reconceptualized how these concepts interrelate. In addition, the discussion of the definition and goal of nursing presented in the Scope and Standards of Practice evolved from work by Staggers and Thompson (2002). NI is one example of a discipline-specific informatics practice within the broader category of health informatics. NI has become well established within nursing since its recognition as a specialty for registered nurses by the ANA in 1992. It focuses on the representation of nursing data, information, knowledge, and wisdom and the management and communication of nursing information within the broader context of health informatics. NI (1) provides a nursing perspective, (2) illuminates nursing values and beliefs, (3) denotes a practice base for nurses in NI, (4) produces unique knowledge, (5) distinguishes groups of practitioners, (6) focuses on the phenomena of interest for nursing, and (7) provides needed nursing language and word context to health informatics (Brennan, 2003).

Metastructures, Concepts, and Tools of NI To understand the foundations of NI, one must begin by exploring its metastructures, sciences, concepts, and tools. Metastructures are overarching concepts used in theory and science. Also of interest are the sciences underpinning NI, concepts and tools from information science and computer science, human–computer interaction (HCI) and ergonomics concepts, and the phenomena of nursing.

Metastructures: Data, Information, Knowledge, and Wisdom

In the mid-1980s, Blum (1986) introduced the concepts of data, information, and knowledge as a framework for understanding clinical information systems and their impact on health care. He did this by classifying the then-current clinical information systems by the three types of objects that these systems processed: data, information, and knowledge. He noted that the classification was artificial with no clear boundaries; however, increasing complexity between the concepts existed.

In 1989, Graves and Corcoran built on this work when they published their seminal work describing the study of NI using the concepts of data, information, and knowledge. The article contributed two broad principles to NI that are acknowledged here: a definition of NI that has been widely accepted in the field; and an information model that identified data, information, and knowledge as key components of NI practice. The Graves model is presented in Figure 6-1.

Graves and Corcoran (1989) drew from Blum (1986) to define the three concepts as follows: (1) data are discrete entities described objectively without interpretation; (2) information is data that are interpreted, organized, or structured; and (3) knowledge is information that is synthesized so that relationships are identified and formalized. Drawing on this work, Nelson (2002; Nelson & Joos, 1989) defined wisdom as the appropriate application of knowledge to the management and solution of human problems.

Data, which are processed to create information and then knowledge, may be obtained from individuals, families, communities, and populations and the environment in which they exist. Data, information, knowledge, and wisdom are of concern to nurses in all areas of practice. For example, data derived from direct care of an individual may then be compiled across persons and aggregated for decision making by nurses, nurse administrators, or other health professionals. Further aggregation may address communities and populations. Nurse educators may create case studies using these data, and nurse researchers may access aggregated data for systematic study.

Figure 6-1 Conceptual framework for the study of nursing knowledge Source: American Nurses Association (ANA). (2008). Nursing informatics: Scope & standards of practice. Springfield, MD: nursebooks.org.

As an example, an instance of vital signs for an individual’s heart rate, respiration, temperature, and blood pressure can be considered a set of data elements. A serial set of vital signs taken over time, placed into a context, and used for longitudinal comparisons is considered information. That is, a dropping blood pressure and increasing heart rate, respiratory rate, and fever in an elderly, catheterized person are recognized as being abnormal for this person. The recognition that the person may be septic and, therefore, may need certain nursing interventions reflects information synthesis (knowledge) based on nursing knowledge and experience.

Figure 6-2 builds on the work of Graves and Corcoran (1989) by adding the concept of wisdom and reconfiguring the interrelationships between and among the concepts. As data are transformed into information and information into knowledge, each level increases in complexity and requires greater application of human intellect. The x-axis in (Figure 6-2) represents interactions within and between the concepts as one moves from data to wisdom; the y-axis represents the increasing complexity of the concepts’

increasing interrelationships. Wisdom is knowing when and how to apply knowledge to deal with complex problems or specific human need (Nelson, 2002;

Nelson & Joos, 1989). Although knowledge focuses on what is known, wisdom focuses on the appropriate application of that knowledge. For example, a knowledge base may include several options for managing an anxious family, whereas wisdom would guide the decisions about which of these options are most appropriate within a specific family. As this example demonstrates, the scope of NI is based on the scope of nursing practice and nursing science with a concentration on data, information, knowledge, and wisdom. It is not limited by current technology. If the study of NI was limited to what the computer can process, the study of informatics could not fully appreciate or support the full scope and complexity of nursing practice. NI must consider how nurses impact technology and how technology impacts nursing. An understanding of this interaction makes it possible to understand how nurses create knowledge and how they make use of that knowledge in their practices.

Figure 6-2 The relationship of data, information, knowledge, and wisdom Source: Copyright Ramona Nelson. Used with the permission of Ramona Nelson, President Ramona Nelson Consulting at ramonanelson@verizon.net. All rights reserved.

The appropriate use of knowledge involves the integration of empirical, ethical, personal, and aesthetic knowledge in the process of implementing actions. The individual must apply a high level of empirical knowledge in understanding the current situation, apply a professional value system in considering possible actions, be able to predict the potential outcome of these actions with a high level of accuracy, and then have the willpower to carry out the selected action in the current environment. An example of applied wisdom demonstrating this integration in NI is the appropriate use of information management and technological tools to support effective nursing practice.

The addition of wisdom raises new and important research questions. This addition challenges the discipline to develop tools and processes for classifying, measuring, and coding wisdom as it relates to nursing, NI, and informatics education. These research avenues help clarify the relationships between wisdom and the intuitive thinking of expert nurses. Such research is invaluable in building information systems to support expert healthcare practitioners and to support the decision process of more novice nurses.

Two interrelated forces have encouraged expansion of the NI model to include wisdom. First, the initial work was limited to the types of objects processed by automated systems in the mid-1980s. However, NI is now concerned with the use of information technology to improve the access and quality of health care that is delivered to individuals, families, and communities. Addition of the concept of wisdom expands the focus of the model from the technology and the processing of objects to include interaction of the human with the technology and resultant outcomes.

The previous ANA Scope and Standards of Practice (2001) had considered the inclusion of the concept of wisdom as too controversial. However, with the recognition of this concept in the 2008 ANA Scope and Standards of Practice, nurses are now demonstrating the practical application of the concept to the practice of NI. For example, Schleyer and Beaudry (2009) described how the data-to-wisdom continuum applies to informatics in telephone triage nursing practice. Another example from acute care is a statement by Troy Seagondollar posted on the NI Listserv ni-wg on November 19, 2010, at 11:27 am:

Content removed due to copyright restrictions.

More formally, the philosophical underpinnings and validity of the concept of wisdom in the data–information–knowledge– wisdom framework are discussed in more detail by Matney, Brewster, Sward, Cloyes, and Staggers (2011). Essentially, two perspectives in philosophy, postpositivism and hermeneutics, together support the addition of wisdom in the framework for NI.

Sciences Underpinning NI

A significant contribution of Graves and Corcoran (1989) was a description and definition of NI that was widely accepted in the field in the 1990s. It stated that NI is a combination of nursing science, information science, and computer science to manage and process nursing data, information, and knowledge to facilitate the delivery of health care. The central notion was that the application of these three core sciences was what made NI unique and differentiated it from other informatics specialties.

In addition to these three core sciences, other sciences may be required to solve informatics issues. James Turley (1996) expanded the model of NI to include cognitive science. Certainly, the cognitive aspect of humans is a critical piece for the informatics nurse specialist (INS) and the informatics nurse (IN) to understand. However, other sciences may be equally as critical, depending on the issue at hand. For example, if the INS is dealing with a systems implementation in an institution, an understanding of organizational theory may be germane to successful implementation (Staggers & Thompson, 2002). As science evolves, it may be necessary to include other core sciences in future models.

Although the core sciences are foundational to the work of NI, the practice of the specialty is considered an applied science rather than a basic science. The combination of sciences creates a unique blend that is greater than the sum of its parts, a unique combination that creates the definitive specialty of NI. Further, informatics realizes its full potential within health care when it is grounded within a discipline; in this case, the discipline is nursing. Computer and information science applied in isolation have less impact than if applied within a disciplinary framework. Through application, the science of informatics can solve critical healthcare issues of concern to a particular discipline.

Structured Language as a Tool for NI

Many of the tools used by the IN and INS are based on metastructures and concepts that incorporate knowledge from nursing and other health and information sciences. Nursing knowledge is gained by the ability to extract data that specifically defines nursing phenomena. Many different languages and ways of organizing data, information, and knowledge exist based on different concepts.

The creation of nursing taxonomies and nomenclatures has occurred over the past years, allowing these iterations to occur. The ANA has formalized the recognition of these languages and vocabularies through a review process of the Committee on Nursing Practice Information Infrastructure (CNPII). Box 6-1 lists the ANA-approved nursing languages (as of August 2012) and provides a website for each approved language (http://www.nursingworld.org/MainMenuCategories/ThePracticeofProfessionalNursing/NursingStandards/Recognized-Nursing- Practice-Terminologies.aspx).

At a higher level of structure, several resources have been developed to facilitate interoperability among different systems of concepts and nomenclature. For instance, the Systemized Nomenclature of Medicine (SNOMED CT) is considered a universal healthcare terminology and messaging structure. In nursing, SNOMED enables terminology from one system to be mapped to concepts from another system, such as the North American Nursing Diagnosis Association (NANDA) terminology, Nursing Intervention Classification (NIC), and Nursing Outcome Classification (NOC). On a larger scale, the Unified Medical Language System of the National Library of Medicine (UMLS; http://www.nlm.nih.gov/research/umls) incorporates the work of more than 100 vocabularies, including SNOMED (http://www.nlm.nih.gov/research/umls/knowledge_sources/metathesaurus/). The INS must be aware of these tools and may be called on to understand the concepts of one or more languages, the relationships between related concepts, and integration into existing vocabularies for a given organization.

BOX 6-1 ANA-RECOGNIZED TERMINOLOGIES THAT SUPPORT NURSING PRACTICE (AUGUST 2012) 1. NANDA: Nursing Diagnoses, Definitions, and Classification, 1992 Website: www.nanda.org 2. Nursing Interventions Classification System (NIC) 1992 Website:

www.nursing.uiowa.edu/excellence/nursing_knowledge/clinical_effectiveness/index.htm (NIC/NOC can be obtained from the same source)

3. Clinical Care Classification (CCC), 1992 Formerly Home Health Care Classification (HHCC) Website: www.sabacare.com 4. Omaha System, 1992 Website: www.omahasystem.org 5. Nursing Outcomes Classification (NOC), 1997 Sue Moorehead, PhD, RN, Center Director

Website: www.nursing.uiowa.edu/excellence/nursing_knowledge/clinical_effectiveness/index.htm (NIC/NOC can be obtained from the same source)

6. Nursing Management Minimum Data Set (NMMDS), 1998 Website: http://www.nursing.umn.edu/icnp/usa-nmds 7. PeriOperative Nursing Data Set (PNDS) 1999

Website: www.aorn.org or http://www.aorn.org/workarea/DownloadAsset.aspx?id=21663 8. SNOMED CT, 1999

Website: www.ihtsdo.org/snomed-ct/ 9. Nursing Minimum Data Set (NMDS), 1999

Website: http://www.nursing.umn.edu/icnp/usa-nmds/ 10. International Classification for Nursing Practice (ICNP), 2000 Website: http://www.icn.ch/icnp.htm 11. ABC Codes, 2000

Website: www.abccodes.com Prepared from information located at http://www.nursingworld.org/MainMenuCategories/ThePracticeofProfessionalNursing/NursingStandards/Recognized-Nursing-Practice- Terminologies.aspx

12. Logical Observation Identifiers Names and Codes (LOINC), 2002 Website: http://loinc.org 13. Retired Data Sets

Patient Care Data Set (PCDS), 1998

The importance of languages and vocabularies cannot be overstated. The INS must seek a broader picture of the implications of his or her work and the uses and outcomes of languages and vocabularies for end users. For instance, nurses working in mapping a home care vocabulary with an intervention vocabulary must see beyond the technical aspect of the work. They must understand that there

may be a case manager for a multisystem health organization or a home care agency that is developing knowledge of nursing acuity and case mix based on the differing vocabularies that they have integrated. The INS must envision the differing functions that may be used with the data, information, and knowledge that have been created. See the Informatics Roles and the Knowledge Work of Nursing chapter for a more thorough discussion of terminologies.

Concepts and Tools from Information Science and Computer Science

Computer science focuses on the study of the theoretical foundations of computation processes and of practical techniques for their application in computer systems. Information science is an interdisciplinary science concerned with the collection, classification, manipulation, storage, retrieval, and dissemination of information.

Informatics tools and methods from computer and information sciences are considered fundamental elements of NI, including information technology, information structures, information management, and information communication.

Information technology includes computer hardware, software, communication, and network technologies, derived primarily from computer science. The other three elements are derived primarily from information science. Information structures organize data, information, and knowledge for processing by computers. Information management is an elemental process within informatics in which one is able to file, store, and manipulate data for various uses. Information communication processes enable systems to send data and to present information in a format that improves understanding. The use of information technology distinguishes informatics from more traditional methods of information management. Thus, NI incorporates the four previously mentioned additional elements from computer and information science. Underlying all of these are HCI concepts, discussed next.

HCI and Related Concepts

HCI, usability, and ergonomics concepts are of fundamental interest to the INS. Essentially, HCI deals with people, software applications, computer technology, and the ways they influence each other (Dix, Finlay, Abowd, & Beale, 2004). Elements of HCI are rooted in psychology, social psychology, and cognitive science. However, the design, development, implementation, and evaluation of applications derive from applied work in computer science, the specific discipline at hand (in this case, nursing), and information science. For example, an INS assesses an application before purchase to determine whether the application design complements the way nurses cognitively process medication orders.

A related concept is “usability,” which deals with specific issues of human performance during computer interactions for specific tasks within a particular context (Dix et al., 2004). Usability issues address the efficiency and effectiveness of an application. For example, an INS might study the ease of learning an application, the ease of using an application, or the speed of task completion and errors that occurred during application use when determining which system or application would be best used on a nursing unit.

The term ergonomics typically is used in the United States to describe the design and implementation of equipment, tools, and machines related to human safety, comfort, and convenience. Commonly, the term ergonomics refers to attributes of physical equipment or to principles of arrangement of equipment in the work environment. For instance, an INS may have a role in ensuring that good ergonomics principles are used in an intensive care unit to select and arrange various devices to support workflow for cross- disciplinary providers and patients’ families.

HCI, usability, and ergonomics are related concepts that are typically subsumed under the rubric of human factors, or how humans and technology relate to each other. The overall goal is better design for software, devices, and equipment to promote optimal task completion in various contexts or environments. Optimal task completion includes the concepts of efficiency and effectiveness, including considerations about the safety of the user. It is essential that the INS understand these concepts to be able to develop effective strategies to select, implement, and evaluate information structures and informatics solutions.

The importance of human factors in health care was elevated with the Institute of Medicine’s 2001 report. Before this, HCI and usability assessments and methods were being incorporated into health care at a glacial speed. In the past 5 years, the number of HCI and usability publications in health care has increased substantially. Vendors have installed usability laboratories and incorporated usability testing of their products into their systems life cycles. The Food and Drug Administration (FDA) has mandated usability testing as part of its approval process for any new devices (Medical Devices Today, 2007). Thus HCI and usability are critical concepts for INs and INSs to understand. Numerous usability methods and tools are available (e.g., heuristics [rules of thumb], naturalistic observation, and think-aloud protocols). Readers are referred to HCI references and The Human–Technology Interface chapter to learn more about these methods.

Phenomena of Nursing

The metaparadigm of nursing comprises four key concepts: (1) nurse, (2) person, (3) health, and (4) environment. Nursing actions are based on interrelationships between the concepts and are related to the values nurses hold relative to them. Nurses make decisions about interventions from their unique perspectives. Decision making is the process of choosing among alternatives. The decisions that nurses make can be characterized by both the quality of decisions and the impact of the actions resulting from those decisions. As knowledge workers, nurses make numerous decisions that affect the life and well-being of individuals, families, and communities. The process of decision making in nursing is guided by the concept of critical thinking. Critical thinking is the intellectually disciplined process of actively and skillfully using knowledge to conceptualize, apply, analyze, synthesize, or evaluate data and information as a guide to belief and action (Scriven & Paul, 1997).

Clinical wisdom is the ability of the nurse to add experience and intuition to a situation involving the care of a person (Benner, Hooper-Kyriadkidis, & Stannard, 1999). Wisdom is demonstrated in informatics by the ability of the INS to evaluate the documentation drawn from a health information system (HIS) and the ability to adapt or change the system settings or parameters to improve the workflow of the clinical nurse.

Nurses’ decision making is described as an array of decisions that include specific behaviors and cognitive processes surrounding a

cluster of issues. For example, nurses use data transformed into information to determine interventions for persons, families, and communities. Nurses make decisions about potential problems presented by an individual and about appropriate recommendations for addressing those problems. They also make decisions in collaboration with other healthcare professionals, such as physicians, pharmacists, or social workers. Decisions also may occur within specific environments, such as executive offices, classrooms, and research laboratories.

An information system collects and processes data and information. Decision support systems are computer applications designed to facilitate human decision-making processes. Decision support systems are typically rule based, using a specified knowledge base and a set of rules to analyze data and information and provide recommendations. Other decision support systems are based on knowledge models induced directly from data, regression, or classification models that predict characteristics or outcomes. Recommendations take the form of alerts (i.e., calling user attention to abnormal laboratory results or potential adverse drug events) or suggestions (e.g., appropriate medications, therapies, or other actions) (Haug, Gardner, & Evans, 1999).

An expert system is a type of decision support system that implements the knowledge of one or more human experts without human intervention. For example, an insulin pump that senses the patient’s blood glucose level and administers insulin based on those data is a form of expert system. Whereas control systems implement decisions without involvement of a user, decision support systems merely provide recommendations and rely on the wisdom of the user for appropriate application of these provided recommendations. Within informatics, there is always a tension between which decisions should be automated and which decisions require human intervention. The relationships among these concepts and information, decision support, and expert systems are depicted in Figure 6-3.

An INS must be able to navigate the complexity of the relationships between the following elements and understand how they facilitate decision making:

Data, information, knowledge, and wisdom Nursing science, information science, computer science, and other sciences of interest to the issue at hand (e.g., cognitive

science) Nurse, person, health, and environment Information structures, information technology, managing and communicating information

Figure 6-3 Levels and types of automated systems Source: Modified from Englebardt, S. & Nelson, R. (2002). Health care informatics: An Interdisciplinary approach. St. Louis, MO: Mosby. Used with the permission of Ramona Nelson, President Ramona Nelson Consulting at ramonanelson@verizon.net. All rights reserved.

The Future of NI The future of NI will impact and be impacted by several driving trends. These include (1) changes in society, such as the aging population and the increased use of participatory and mobile technology; (2) changes in healthcare delivery, including the changing and expanding role of nursing within healthcare delivery; and (3) changes in technology, such as nanotechnology, which promises to redefine the composition of nearly every human-made material and drastically alter biomedical applications (Alivisatos, 2001).

All of these changes will drive increased saturation of informatics concepts and solutions into mainstream nursing and healthcare practices. As informatics solutions become as common a tool as the stethoscope, every nurse, to be safe and effective, will need to incorporate informatics concepts into all aspects of practice. One example is the increased recognition of the concept of wisdom as a key concept for NI. Its more detailed definition and measurement will be a part of the future practice of all nursing.

Summary This chapter outlined the foundations of NI. The following definition for NI is offered: NI is a specialty that integrates nursing science, computer science, and information science to manage and communicate data, information, knowledge, and wisdom in nursing practice. As part of this chapter’s consideration of NI, interrelationships among major NI concepts were discussed. As data are transformed into information and information into knowledge, and knowledge is applied through wisdom to ensure appropriate, effective, and compassionate nursing care, increasing complexity and interrelationships ensue. The boundaries between concepts can become blurred and feedback loops from one concept level to another emerge. These major concepts can be related to various types of systems, including information, expert, and decision support systems. The following sciences are intimately intertwined with NI: nursing, computer, and information science. Other sciences are used as required by the issue at hand.

Critical elements for NI include structured languages and HCI concepts. Taxonomies and other current structured languages for nursing were listed within this chapter. HCI concepts were briefly defined and discussed because they are critical to the success of

informatics solutions. In an ideal world, the authors would like to see the following:

Patients who are truly active participants in managing their own health. The use of technology, such as personal health records and automated monitoring devices, along with the implementation of participatory medicine, will make that possible.

Safe care, where more than 100,000 patients do not die each year from medical errors. Improved use of technology, such as decision support systems and alerts, will make that possible.

Health care that can be afforded by all. Increased use of prevention strategies and efficiencies gained from technology will make that possible.

A working environment for all nurses where data, information, and knowledge are effectively managed to ensure wisdom guides all nursing decisions.

THOUGHT-PROVOKING QUESTIONS

1. How is the concept of wisdom in NI like or unlike professional nursing judgment? 2. Can you create examples of how expert systems (not decision support systems but true expert systems) can be used to support nursing practice? 3. How would you incorporate the data-to-wisdom continuum into a job description for an NI specialist? 4. Can any aspect of nursing wisdom be automated? 5. The chapter states that research will be invaluable in building information systems to support expert healthcare practitioners and support the

decision-making processes of more novice nurses. What is the significance of this statement to the study of HCI in NI?

References Alivisatos, A. P. (2001). Less is more in medicine: Sophisticated forms of nanotechnology will find some of the first real-world applications in

biomedical research, disease diagnosis and possibly therapy. Scientific American, 285(3), 66–73. American Nurses Association (ANA). (2008). Nursing informatics: Scope and standards of practice. Springfield, MD: Nursesbooks.org Benner, P., Hooper-Kyriadkidis, P., & Stannard, D. (1999). Clinical wisdom and interventions in critical care: A thinking-in-action approach.

Philadelphia, PA: W. B. Saunders. Blum, B. (1986). Clinical information systems. New York, NY: Springer-Verlag. Brennan, R. (2003). One size doesn’t fit all: Pedagogy in the online environment: Vol. 1. Adelaide, Australia: National Centre for Vocational Education

Research. http://www.ncver.edu.au/research/proj/nr0F05e.htm Dix, A., Finlay, J., Abowd, G., & Beale, R. (2004). Human–computer interaction. Harlow, UK: Pearson, Prentice Hall. Graves, J., & Corcoran, S. (1989). The study of nursing informatics. Image, 21(4), 227–230. Haug, P., Gardner, R., & Evans, S. (1999). Hospital-based decision support. In E. S. Berner (Ed.), Clinical decision support systems: Theory and

practice (pp. 77–104). New York, NY: Springer-Verlag. Institute of Medicine. (2001). Crossing the quality chasm: A new health system for the 21st century. Washington, DC: National Academies Press. Joos, I., Nelson, R., & Smith, M. (2010). Introduction to computers for healthcare professionals (5th ed.). Sudbury, MA: Jones and Bartlett. Matney, S., Brewster, P., Sward, K., Cloyes, K., & Staggers, N. (2011, March). Philosophical approaches to the data–information–knowledge–wisdom

framework. Advances in Nursing Science, 34(1) 6–18. Medical Devices Today. (2007). New usability standard aims to help firms institute human factors programs.

http://www.medicaldevicestoday.com/2007/04/new_usability_s.html Nelson, R. (2002). Major theories supporting health care informatics. In S. Englebardt & R. Nelson (Eds.), Health care informatics: An interdisciplinary

approach (pp. 3–27). St. Louis, MO: Mosby-Year Book. Nelson, R., & Joos, I. (1989, Fall). On language in nursing: From data to wisdom. Pennsylvania League for Nursing PLN Vision (p. 6). Schleyer, R., & Beaudry, S. (2009). Data to wisdom: Informatics in telephone triage nursing practice. AAACN Viewpoint, 31(5), 1, 10–13. Scriven, M., & Paul, R. (1997). A working definition of critical thinking. Retrieved March 2008 from http://lonestar.texas.net/~mseifert/crit2.html Staggers, N., & Thompson, C. B. (2002). The evolution of definitions for nursing informatics: A critical analysis and revised definition. Journal of the

American Medical Informatics Association, 33(1), 75–81. Turley, J. (1996). Toward a model for nursing informatics. Image: Journal of Nursing Scholarship, 28(4), 309–313.

Chapter 7

Informatics Roles and the Knowledge Work of Nursing Julie A. Kenney and Ida Androwich

OBJECTIVES

1. Explore the concept of nurses as knowledge workers. 2. Discuss the evolving roles and competencies of nursing informatics practice.

Key Terms

Advocate/policy developer Certification Cognitive activity Consultant Continuous learner Data Data gatherer Decision support/outcomes manager Educator Entrepreneur Industrial Age Informatics Informatics innovator Informatics nurse specialist Information Information Age Information user Interdisciplinary knowledge team Knowledge Knowledge builder Knowledge user Knowledge worker Medical informatics Nursing informatics competencies Product developer Project manager Researcher Technologist TIGER initiative

Introduction The world has witnessed an unprecedented number of technological advances during the last 100 years. The early 20th century saw the invention of the car and the airplane; both modes of transportation drastically changed how people work and play. The entertainment world was dramatically altered by the invention of radio and television. The introduction of the computer altered the way data and information are viewed and used and changed the way business is conducted. The computer is now changing nursing and health care.

Nurses have historically gathered and interpreted data. Florence Nightingale is credited as one of the first statisticians to collect and use data to change the way she cared for her patients. While serving in the Crimean War, she began to gather data regarding the conditions in which patients were living and the diseases they contracted and from which they died. These data were later used to improve patient conditions at both city and military hospitals (O’Connor & Robertson, 2003).

Today, nurses are able to access information much more quickly and easily than their predecessors. Accessing information via the Internet or the electronic health record (EHR) allows the nurse to provide the best possible patient care. Genomic health care and the interaction between genetic factors and the environment require new understanding of the various types of information needed to make decisions—a vast array of data characterized as a “data tsunami” (Bakken, Stone, & Larson, 2008).

Nursing recognized early on that computers would change health care and became actively involved in shaping how computers

were used in health care. The American Nurses Association (ANA) first recognized nursing informatics (NI) as a specialty in 1992 (ANA, 2008; Saba & McCormick, 2006). The introduction of this specialty has spurred the development of many informatics jobs, organizations, and publications. Nurses now have the ability to further their education by attending informatics conferences, reading journals, obtaining certificates and advanced degrees, and participating in numerous hospital-based and national and international informatics committees and groups.

The Nurse as a Knowledge Worker As described in the Overview of Nursing Informatics chapter, all nurses use data and information. This information is then converted to knowledge. The nurse then acts on this knowledge by initiating a plan of care, updating an existing one, or maintaining status quo. Does this use of knowledge make the nurse a knowledge worker? This section focuses on the definition of a knowledge worker, the history of the term, and the ways in which it is used in health care and business. This chapter as a whole examines how nursing relates to the term knowledge worker and the effect a knowledge worker has on health care.

Definitions

Knowledge can be defined as “the distillation of information that has been collected, classified, organized, integrated, abstracted, and value added” (HIMSS, 2006, p. 49). A worker is “one that works especially at manual or industrial labor or with a particular material” (Merriam-Webster Online, 2011). The term knowledge worker was first coined by Peter Drucker in his 1959 book, Landmarks of Tomorrow (Drucker, 1994). Knowledge work is defined as nonrepetitive, nonroutine work that entails a significant amount of cognitive activity (Sorrells-Jones & Weaver, 1999a). Drucker (1994) describes a knowledge worker as one who has advanced formal education and is able to apply theoretical and analytical knowledge. According to Drucker, the knowledge worker must be a continuous learner and a specialist in a field. McCormick (2009) estimates that a knowledge worker spends at least 50% of his or her work time searching for and evaluating information.

According to Androwich (2010), it is important to understand that there is a dual role for accessing and using information (content) in health care. In the first instance, when the nurse is caring for an individual patient, evidenced-based information (content) and patient data need to be available at the point of care to inform the present patient encounter. In the second instance, patient data that are entered by the nurse in the process of documentation need to be entered in such a manner that they are able to be aggregated to inform future patient encounters.

Knowledge Worker Concept

The world is transitioning from the Industrial Age to the Information Age (Snyder-Halpern, Corcoran-Perry, & Narayan, 2001; Sorrells-Jones & Weaver, 1999a). In the early 1900s, the workforce consisted predominantly of farmers. After World War I, the workforce began to become predominantly industrial. This transition occurred when many farmers and domestic help moved to the cities to take jobs at factories. Today, the industrial worker is slowly being replaced by the technologist (Drucker, 1994); the technologist is adept at using both mind and hand. Many industrial workers are finding it increasingly more difficult to obtain jobs because they do not have the educational base or mindset required of knowledge workers (Drucker, 1994). The technologist is no longer trained on the job, as industrial workers traditionally were, which can cause significant problems for the industrial worker who does not have the education required to transition to a knowledge worker position (Drucker, 1994; Sorrells-Jones & Weaver, 1999a).

Knowledge workers are innovators, and the work they produce is the foundation for organizational sustainability and growth. Knowledge workers are specialized, have advanced education, and typically have a high degree of autonomy and control over their own work environments (Davenport, Thomas, & Cantrell, 2002; Sorrells-Jones & Weaver, 1999a). Such individuals are most efficient when they are working in a multidisciplinary team. These teams are typically composed of members with complementary knowledge bases. The team members possess problem-solving and decision-making skills and advanced interpersonal skills. All members of the team are considered equal and are there to contribute their expertise. Leadership shifts and changes as the team tackles different parts of the project, with the topic expert taking the lead. A well-functioning team can consistently outperform an individual (Sorrells-Jones & Weaver, 1999b). Many of these teams become focused and passionate about the project on which they are working.

A key impediment to team effectiveness is a lack of understanding between team members and a lack of respect for one another’s knowledge and experience (Sorrells-Jones & Weaver, 1999a). Another barrier to efficiency within the multidisciplinary team is the individual knowledge worker who does not want to give up his or her own identity even though he or she may be swayed by other professional opinions. Professionals have a more difficult time adjusting to working in a team than do nonprofessionals. Professionals fail very few times in their lives, which often results in their not being able to learn from their failures (Sorrells-Jones & Weaver, 1999b). Knowledge workers also tend to be resistant to change, and as a result they dig in their heels and refuse to adapt to changes that management has implemented to improve the work process or workflow (Davenport et al., 2002).

Companies that employ knowledge workers have been forced to change their management structures to better support these employees. Management no longer commands, but rather seeks to inspire workers to produce the best product (Drucker, 1992). Companies that rely on knowledge workers have come to the realization that the machines are unproductive without the knowledge of those workers. Loyalty is no longer purchased with a paycheck but is earned by giving knowledge workers the ability to use their knowledge effectively and innovatively (Drucker, 1992). In turn, the physical environment and workplace arrangements have been adjusted to maximize the workflow of the knowledge workers (Davenport et al., 2002). Many of these changes have occurred in the business world but have been slow to be adopted in health care.

Knowledge Workers and Health Care

The healthcare industry is firmly rooted in the Industrial Age. This long-standing tradition has resulted in an industry that is not

conducive to support the knowledge workers who represent most of the workforce (Sorrells-Jones & Weaver, 1999a; Wickramasinghe & Ginzberg, 2001). Sorrells-Jones and Weaver (1999a) state that “healthcare institutions are among the most rigidly bureaucratic and hierarchical, discipline-fragmented organizations in the U.S.” (p. 16). This organizational arrangement is evidenced by the multiple administrative levels that manage a single-function unit. Corporate values within health care reflect the desire for employees to be loyal and compliant, to avoid risk, and to see failure as negative instead of positive. Senior leadership keeps information tightly controlled and fails to see the need to bring in external intelligence and influence. Rewards are based on individual rather than team performance, and a significant pay difference exists between those at the top and those who produce the product (Weaver & Sorrells- Jones, 1999).

Right now, health care is in the process of transitioning from the Industrial Age to the Information Age. This transition has proved challenging because of the success of healthcare institutions that have enjoyed using current management methods. Its history of success will make it difficult for the healthcare industry to abandon the old so as to learn the new. A new philosophy recognizing that employees are mature, self-reliant, independent-thinking adults who function as partners in carrying out the work of the organization is needed. The organization needs to view (knowledge worker) employees as an asset and supply the resources, tools, information, and power they need to self-manage their work. Innovation needs to be supported, especially when it meets the customers’ needs, desires, and wishes (Weaver & Sorrells-Jones, 1999).

Currently, there is a healthcare management trend toward adoption of flatter management styles with fewer layers of administration. Organizations are beginning to switch to a clinical product or service line format. This format is typically designed with the physician serving as the content expert and the nurse serving as the patient care expert. Unfortunately, this approach does not represent a significant change from the way things are currently done (Weaver & Sorrells-Jones, 1999).

In the future, management needs to understand and support the knowledge work and nonknowledge work that are performed daily in health care, as both types of work are integral to caring for patients safely. Organizations must switch from measuring the number of tasks completed to measuring the outcomes obtained by knowledge workers (Sorrells-Jones & Weaver, 1999b). This trend is becoming more evident with the posting of hospital report cards that demonstrate how effectively the hospital is caring for certain types of patients.

Nurses as Knowledge Workers

The question to ask is, “Are nurses knowledge workers?” As shown in Table 7-1, when nursing characteristics are compared to the characteristics of a knowledge worker, the nurse does meet the criteria for a knowledge worker. Nursing entails a significant amount of knowledge and nonknowledge work. Knowledge work includes such duties as interpreting trends in laboratories and symptoms. Nonknowledge work includes such tasks as calling the laboratory to check on laboratory results or making beds. Nurses, on a daily basis, rely on their extensive clinical information and specialized knowledge to implement and evaluate the processes and outcomes related to patient care (Snyder-Halpern et al., 2001).

TABLE 7-1 A COMPARISON OF KNOWLEDGE WORKER CHARACTERISTICS AND NURSING CHARACTERISTICS

Knowledge Worker Characteristics Nursing Characteristics

Advanced formal education • All nurses have college degrees ranging from AD/AS to PhD.

Able to apply theoretical and analytical knowledge

• Nurses are educated on nursing theory and how to apply it in patient situations.

Continuous learner • Obtain advanced degrees. • Attend seminars. • Earn contact hours.

Specialized • Nursing specialties are as numerous as medical specialties.

Innovator • Nurses become innovative when they do not have proper equipment to care for the patients or

they feel that current products are inadequate.

Team member • Have been a member of the interdisciplinary team for a significant amount of time.

Snyder-Halpern et al. (2001) have identified four tasks associated with human information processing: (1) data gathering, (2) information use, (3) creative application of knowledge to clinical practice, and (4) generation of new knowledge. These four tasks are associated with four roles that nursing takes on as a knowledge worker: data gatherer, information user, knowledge user, and knowledge builder, respectively.

Nurses are data gatherers by nature. They collect and record objective clinical data on a daily basis. These items include such things as patient history information, vital signs, and patient assessment data. Nurses as data gatherers transition to information users when they begin to interpret the data that they have collected and recorded. Nurses as information users then structure the clinical data into information that can be used to guide patient care decisions (Snyder-Halpern et al., 2001). An example of this is when the nurse notices that the patient’s blood pressure is elevated. Information users transition to knowledge users when they begin to notice trends in a patient’s clinical data and determine whether the clinical data fall within or outside of the normal data range. Nurses transition from knowledge users to knowledge builders when they examine clinical data and trends across groups of patients. These trends are interpreted and compared to current scientific data to determine whether these data would improve the nursing knowledge domain. An example of the transition of a nurse as knowledge user to a nurse as knowledge builder is an observation of medication compliance

rates over a specified time period for patients diagnosed with chronic high blood pressure, with the nurse then comparing these rates to evidence-based literature to determine if this information improves the nursing knowledge base (Snyder-Halpern et al., 2001).

Snyder-Halpern et al. (2001) found that as nurses assumed each of these roles, they required different types of decision support processes to support their knowledge needs. The data gatherer requires a system that captures and stores data accurately and reliably and allows the data to be readily accessed. Most current healthcare decision support systems (DSSs) support the nurse in this role. The information user role requires a system that can transform clinical data into a format that allows for easy recognition of patterns and trends. These systems recognize the trend and display it for the nurse, who in turn uses this information to adjust the plan of care for the patient. The information user role is generally well supported by current DSSs. The knowledge user role is the least supported role, and many systems are currently looking at ways to support nurses in this role. One advantage of these DSSs is their ability to bring knowledge to nurses so that they do not have to retrieve the information themselves, which allows them to adjust a patient’s plan of care in a more efficient and timely manner. The knowledge builder role is typically seen in conjunction with the nurse researcher role and quality management roles. These roles typically look at aggregated data that have been captured over time and from numerous patients, with these data then being compared to clinical variables and interventions; this analysis results in the development of new domain knowledge (Snyder-Halpern et al., 2001). The knowledge needs of nurses will continue to evolve as the systems improve.

The Challenge of Nurses as Knowledge Workers

For nurses to be treated as knowledge workers, they must first be recognized as knowledge workers (Snyder-Halpern et al., 2001). Nurses have been part of the interdisciplinary team for years, but are they ready to become part of the interdisciplinary knowledge team? Nursing may be ready to take that step, but are other members of the healthcare team ready to acknowledge nurses as respected members of the team (Sorrells-Jones & Weaver, 1999a)? One reason that acceptance may be difficult to achieve is the fact that nurses tend to be the least educated members of the interdisciplinary knowledge team, although this situation is slowly changing. Another reason is that nurses, historically, have had a difficult time being active members of the interdisciplinary team (Sorrells-Jones & Weaver, 1999b). Nursing still has a long way to go before nurses are fully accepted as equal participants in the interdisciplinary knowledge team. For nurses to reach this goal, a major attitude change toward nursing needs to take place. In addition, nurses must become better educated and more involved in the interdisciplinary knowledge team.

The Knowledge Needs and Competencies of Nurses In the early days of medicine, the entire body of medical knowledge could fit into a single volume. Today, the amount of information available is vast and expanding exponentially, a fact that makes the healthcare industry the world’s most knowledge-intense environment (Snyder-Halpern et al., 2001). Computers, technology, and the informatics fields are assisting healthcare workers in dealing with this information explosion.

Knowledge Needs

Nurses deal with a vast amount of information and knowledge every day, which they use to care for their patients. Nurses rely on an extensive amount of clinical information and specialized knowledge to evaluate the processes they have implemented and to measure the corresponding outcomes (Snyder-Halpern et al., 2001). Although nurses rely on their own knowledge, sometimes this knowledge base is not adequate; on such occasions, they must access information to provide safe patient care. In a national survey, consulting a peer was reported to be the most frequent way that information was obtained. The same survey also found that most of those surveyed did not use information resources to gather practice information, and that only approximately 25% had been trained on how to use an electronic database (Barton, 2005). If a peer does not have the information the nurse is seeking, the nurse is likely to turn to a hospital policy, a journal, a textbook, a drug book, an online resource, the EHR, or many other possible sources to find the needed information. New technology tools are likely to change these behaviors so that the best and most current information is readily available and regularly used.

For this information to be beneficial to the nurse and the patient, it must be reliable and credible. The resource must be easily accessible and packaged in such a way that the nurse is able to find the necessary information quickly and with a minimal amount of difficulty. One way this can be accomplished is by implementing a DSS, which is designed to support nurses in their decision-making activities. DSSs are increasingly being incorporated into the EHR.

Nursing Informatics Competencies

One challenge that health care is currently facing relates to the vast differences in computer literacy and information management skills that healthcare workers possess (McNeil, Elfrink, Beyea, Pierce, & Bickford, 2006). Barton (2005) believes that new nurses should have the following critical skills: use e-mail, operate Windows applications, search databases, and know how to work with the institution-specific nursing software used for charting and medication administration. These skills should not be limited to just new nurses, but instead should be required of all nurses and healthcare workers.

Staggers, Gassert, and Curran (2001) advocate that nursing students and practicing nurses should be educated on core NI competencies. Although information technology and informatics concepts certainly need to be incorporated into nursing school curricula, progress in this area has been slow. In the 1980s, a nursing group of the International Medical Informatics Association convened to develop the first level of nursing competencies. While developing these competencies, the nursing group found that nurses fell in to one of the following three categories: (1) user, (2) developer, or (3) expert. These categories have since been expanded.

Staggers et al. (2001) decided that the NI competencies developed in the 1980s were inadequate and needed to be updated. These authors reviewed 35 NI competency articles and 14 job descriptions, which resulted in 1,159 items that were sorted into three broad categories: (1) computer skills, (2) informatics knowledge, and (3) informatics skills. These items were then placed in a database,

where redundant items were removed. When this process was completed, 313 items remained. When these items were then further subdivided, Staggers and colleagues, along with the American Medical Informatics

Association (AMIA) work group, realized that these competencies were not universal to all nurses; thus, before it could be determined if the competency was an NI competency, nursing skill levels needed to be defined. The group determined that practicing nurses could be classified into four categories:

(1) beginning nurse, (2) experienced nurse, (3) informatics nurse specialist (INS), and (4) informatics innovator. Each of these skill levels needed to be defined before Staggers et al. (2001) could determine which level was the most appropriate for that skill set. Table 7-2 provides the definition criteria for each skill level. Once the levels were defined, the group determined that 305 items were NI competencies and placed them into appropriate categories.

Staggers, Gassert, and Curran (2002) conducted a Delphi study to validate the placement of the competencies into the correct skill level. Of the 305 original competencies identified, 281 achieved an 80% approval rating for both importance as a competency and placement in the correct practice level. The authors stress that this is a comprehensive list; thus, for a nurse to enter a particular skill level, he or she need not have mastered every item listed for that skill level. To access the entire list of competencies by skill level, visit http://www.nursingconsult.com/nursing/journals/0029-6465/full-text/PDF/s0029646508000546.pdf?issn=0029- 6465&full_text=pdf&pdfName=s0029646508000546.pdf&spid=21480607&article_id=666314. Table 7-3 provides a modified version of the list.

TABLE 7-2 DEFINITIONS OF FOUR LEVELS OF PRACTICING NURSES Beginning Nurse • Has basic computer technology skills and information management skills • Uses institution’s information systems and the contained information to manage patients

Experienced Nurse • Proficient in a specialty • Highly skilled in using computer technology skills and information management skills to support his or her specialty area of practice • Pulls trends out of data and makes judgments based on this information • Uses current systems, but will collaborate with informatics nurse specialist regarding concerns or suggestions provided by staff

Informatics Nurse Specialist • RN with advanced education who possesses additional knowledge and skills specific to computer technology and information

management • Focuses on nursing’s information needs, which include education, administration, research, and clinical practice • Application and integration of the core informatics sciences: information, computer, and nursing science • Uses critical thinking, process skills, data management skills, systems life cycle development, and computer skills

Informatics Innovator • Conducts informatics research and generates informatics theory • Vision of what is possible • Keen sense of timing to make things happen • Creative in developing solutions • Leads the advancement of informatics practice and research • Sophisticated level of skills and understanding in computer technology and information management • Cognizant of the interdependence of systems, disciplines, and outcomes and is able to finesse situations to obtain the best outcome

Source: Republished with permission of SLACK Incorporated, from Journal of Nursing Education, Staggers, N., Gassert, C., & Curran, C. Staggers, N 40(7), 2001; 303–316; permission conveyed through Copyright Clearance Center, Inc.

In 2004, a group of nurses came together after attending a national informatics conference to ensure that nursing was equally recognized in the national informatics movement. This so-called Technology Informatics Guiding Education Reform (TIGER) team determined that using informatics was a core competency for all healthcare workers. They also determined that many nurses lack information technology skills, which limits their ability to access evidence-based information that could otherwise be incorporated into their daily practice. This group is currently working on a plan to include informatics courses in all levels of nursing education; when that effort is complete, they will examine how to get the information out to practicing nurses who are not currently enrolled in an academic program (TIGER Initiative, 2006). Many of the items identified by the TIGER team as lacking in both nursing students and practicing nurses are items that Staggers et al. (2002) determined to be NI competencies. To learn more about the TIGER initiative, visit http://www.tigersummit.com/.

What Is Nursing Informatics Specialty Practice? NI is an established, yet ever-evolving profession that began when computers were introduced into health care. Those choosing NI as a career find it full of numerous and varied opportunities. Until recently, most nurse informaticists entered the field by showing an understanding and enthusiasm for working with computers. Now, however, nurses have many educational opportunities available to become formally trained in the field of NI. This section of the chapter explores the scope and standards of NI, NI roles, education and specialization, rewards of working in the field, and organizations and professional journals of the INS.

Nursing Contributions to Healthcare Informatics

Nursing has been involved in the purchase, design, and implementation of IS since the 1970s (Saba & McCormick, 2006). One of the first health IS vendors studied how nurses managed patient care and realized that nursing activity was the core of patient activity and needed to be the foundation of the health or clinical IS. Nursing informaticians have been instrumental in developing, critiquing, and promoting standard nursing terminologies to be used in the health IS. Nursing is involved heavily in the design of educational materials for practicing nurses, student nurses, other healthcare workers, and patients. Computers have revolutionized the way individuals access information and have revolutionized educational and social networking processes.

Scopes and Standards

NI is important to nursing and health care because it focuses on representing nursing data, information, and knowledge. NI meets the following needs for health informatics (ANA, 2008; Brennan, 1994):

Provides a nursing perspective Showcases nursing values and beliefs Provides a foundation for nurses in NI Produces unique knowledge Distinguishes groups of practitioners Emphasizes the interest for nursing Provides needed nursing language and word context

In 2008, the ANA published a revised scope and standards of nursing informatics practice. This publication includes the most recent INS standards of practice and the INS standards of professional performance. There are three overarching standards of practice (ANA, 2008, p. 33):

1. Incorporate theories, principles, and concepts from appropriate sciences into informatics practice. 2. Integrate ergonomics and human–computer interaction (HCI) principles into informatics solution design, development,

selection, implementation, and evaluation. 3. Systematically determine the social, legal, and ethical impact of an informatics solution within nursing and health care.

The standards of practice and professional performance for an INS are listed in Box 7-1.

Nursing Informatics Roles

NI has become a viable and essential nursing specialty with the introduction of computers and the EHR to health care. Many nurses entered the NI field because of their natural curiosity and their dedication to being lifelong learners. Others who entered this field might have done so by accident: Perhaps they were comfortable working with computers and their coworkers used them as a resource for computer-related questions. The introduction of the EHR has forced all clinicians to learn to use this new technology and incorporate it into their already busy days. According to one estimate, nurses spend as little as 15% of their days with their patients and as much as 50% of their day documenting (HIMSS Nursing Informatics Awareness Task Force, 2007). Assisting nurses to incorporate this new technology into their daily workflow is one of many challenges that the INS may tackle.

BOX 7-1 INFORMATICS NURSE SPECIALIST STANDARDS OF PRACTICE AND PERFORMANCE Standards of Practice Standard 1: Assessment Standard 2: Problem and Issues Identification Standard 3: Outcomes Identification Standard 4: Planning Standard 5: Implementation Standard 5A: Coordination of Activities Standard 5B: Health Teaching and Health Promotion and Education Standard 5C: Consultation Standard 6: Evaluation

Standards of Professional Performance Standard 7: Education Standard 8: Professional Practice Evaluation Standard 9: Quality of Practice Standard 10: Collegiality Standard 11: Collaboration Standard 12: Ethics Standard 13: Research Standard 14: Resource Utilization Standard 15: Advocacy Standard 16: Leadership

Source: American Nurses Association (ANA). (2008). Nursing informatics: Scope and standards of practice. Silver Spring, MD: Nursesbooks.org. © 2008 American Nurses Association. Reprinted with permission. All Rights Reserved.

The INS may take on numerous roles. For example, one position that nurses fill quite well is the role of the project manager, as a result of their ability to manage multiple complex situations at one time (HIMSS Nursing Informatics Awareness Task Force, 2007). Because of the breadth of the NI field, however, many INSs find that they need to further specialize. The following list includes some typical INS positions. It is far from comprehensive, because this field changes rapidly, as does technology (ANA, 2008; Thede, 2003).

Project Manager. In the project manager role, the INS is responsible for the planning and implementing of an informatics project. The INS uses communication, change management, process analysis, risk assessment, scope definition, and team building. This role acts as the liaison between clinicians, management, IS, vendors, and all other interested parties.

Consultant. The INS who takes on the consultant role provides expert advice, opinions, and recommendations based on his or her area of expertise. Flexibility, good communication skills, excellent interpersonal skills, and extensive clinical and informatics knowledge are highly desirable skill sets needed by the NI consultant.

Educator. The success or failure of an informatics solution can be directly related to the education and training that were provided for end users. The INS who chooses the educator role develops and implements educational materials and educational sessions and provides education about the system to new or current employees during a system implementation or an upgrade.

Researcher. The researcher role entails conducting research (especially data mining) to create new informatics and clinical knowledge. Research may range from basic informatics research to developing clinical decision support tools for nurses.

Product Developer. An INS in the product developer role participates in the design, production, and marketing of new informatics solutions. An understanding of business and nursing is essential in this role.

Decision Support/Outcomes Manager. Nurses assuming the role of decision support/outcomes manager use tools to maintain data integrity and reliability. Contributing to the development of a nursing knowledge base is an integral component of this role.

Advocate/Policy Developer. INSs are a key to developing the infrastructure of health policy. Policy development on a local, national, and international level is an integral part of the advocate/policy developer role.

Clinical Analyst/System Specialist. INSs may work at varying levels and serve as a link between nursing and information services in healthcare organizations.

Entrepreneur. Those nurses involved in the entrepreneur role analyze nursing information needs and develop and market solutions.

Specialty Education and Certification

Many nurses who entered into NI did so without any formal education in this field. In many cases, these nurses served as the unit resource for computer or program questions. Often, they acquired their skills through on-the-job training or by attending classes. Although this pathway to the NI field is still available today, more formal ways of acquiring these skills also exist. The informatics nurse has a bachelor of science degree in nursing and additional knowledge and expertise in the informatics field (ANA, 2008). The INS holds an advanced degree or a post-master’s certificate and is prepared to assume roles requiring this advanced knowledge. INSs may attend informatics conferences and obtain contact hours or continuing education units.

Box 7-2 lists some of the pioneering colleges and universities that offer advanced degrees or certificates in NI. This is not a comprehensive list; new programs are continually being developed. Local colleges and universities should be researched to see which may have informatics programs.

BOX 7-2 FORMAL NURSING INFORMATICS EDUCATIONAL PROGRAMS

Graduate Degree Programs Duke University: http://nursing.duke.edu/modules/son_academic/index.php?id=101 Excelsior College: http://www.excelsior.edu/nursing-masters-informatics-faq Loyola University Chicago: http://www.luc.edu/nursing/graduate_hsm.shtml New York University: http://www.nyu.edu/nursing/academicprograms/masters/programs/informatics.html Northeastern University: http://www.healthinformatics.neu.edu/ University of Alabama at Birmingham: Retrieved September 2010 from http://main.uab.edu/shrp/default.aspx?pid=77369 University of Colorado at Denver: http://www.ucdenver.edu/academics/colleges/nursing/programs-admissions/masters-programs/ms-

program/specialties/healthcareinformatics/Pages/default.aspx University of Iowa: http://www.nursing.uiowa.edu/center-for-nursing-classification-and-clinical-effectiveness University of Kansas: http://nursing.kumc.edu/academics/master-of-science/nursing-informatics.html University of Maryland: http://nursing.umaryland.edu/academic-programs/grad/masters-degree/ms-academicprogram/nursing-informatics University of North Carolina at Chapel Hill: https://nursing.unc.edu/academics/master-of-science-in-nursing/health-care-systems-msn/ University of Pittsburgh: http://www.unmc.edu/nursing/programs.html University of Utah: http://nursing.utah.edu/programs/msnursinginformatics.php University of Washington: http://www.son.washington.edu/portals/cipct/ Vanderbilt University: http://www.nursing.vanderbilt.edu/msn/ni.html

Certificate Programs Chamberlain College of Nursing: http://www.chamberlain.edu/admissions/graduate/graduate-certificate-programs Indiana University: http://nursing.iupui.edu/continuing/informatics.shtml Loyola University Chicago: http://www.luc.edu/media/lucedu/nursing/pdfs/Informatics%20Certificate.pdf Northeastern University: http://www.healthinformatics.neu.edu/ Penn State University: http://www.worldcampus.psu.edu/degrees-and-certificates/nursing-informatics-certificate/overview University of Iowa: http://informatics.grad.uiowa.edu/health-informatics/curriculum

Nurses who choose to specialize in NI have two certifications available to them. The first is obtained through the American Nurses Credentialing Center. The Center’s examination is specific for the informatics nurse. The applicant must be a licensed registered nurse with at least 2 years of recent experience and have a baccalaureate degree in nursing. The applicant must have completed 30 contact hours of continuing education in informatics. The applicant must meet one of the following criteria: (1) 2,000 hours practicing as an informatics nurse, (2) 1,000 hours practicing as an informatics nurse and 12 semester hours of graduate academic credit toward an NI degree, or (3) completion of an NI degree that included at least 200 supervised practicum hours. For further information on this certification examination, visit http://www.nursecredentialing.org/Certification/NurseSpecialties/Informatics. This website includes the aforementioned criteria and provides further information about test eligibility, fees, examination content, examination locations, study materials, and practice tests.

The second certification examination is sponsored by the Healthcare Information and Management Systems Society (HIMSS). Candidates who successfully pass this examination are designated as certified professionals in healthcare information and management systems. The HIMSS examination is open to any candidate who is involved in healthcare informatics. Candidates must hold positions in the following fields: administration/management, clinical IS, e-health, IS, or management engineering. Candidates may include any of the following: chief executive officers, chief information officers, chief operating officers, senior executives, senior managers, IS technical staff, physicians, nurses, consultants, attorneys, financial advisors, technology vendors, academicians, management engineers, and students. Candidates must meet the following criteria to be eligible to sit for the examination: a baccalaureate degree plus 5 years of associated information and management systems experience, with 3 of those years being in health care; or a graduate degree plus 3 years of associated information and management systems experience, with 2 of those years being in health care. The information discussed in this text and additional information about the examination can be found by visiting http://www.himss.org/ASP/certification_cphims.asp.

Rewards of NI Practice

NI is a nursing specialty that does not focus on direct patient care but instead focuses on enhancing patient care and safety and improving the workflow and work processes of nurses and other healthcare workers. The INS is instrumental in designing the

electronic healthcare records that healthcare workers use on a daily basis. This nurse is also responsible for designing tools that allow healthcare workers to access patient information more efficiently than they have been able to do so in the past. Watching these changes take place brings great satisfaction to the INS.

Change is a factor that an INS deals with on a daily basis. This dynamic nature of the position is probably its most difficult aspect, because people deal with change differently. Understanding change theory and processes and appreciating how change affects people assist the INS in developing strategies to encourage healthcare workers to accept changes and become proficient in informatics solutions that have been implemented. Seeing the change adopted with a minimal amount of discord is very rewarding to the INS.

The INS also participates in informatics organizations that allow INSs to network and share experiences with one another. Such interactions allow INSs to bring these new solutions back to their respective organizations and improve informatics trouble spots. Attending professional conferences allows the INS to stay abreast of changes in the industry. Continuing education may help the INS to improve a process or workflow within the hospital or to change the way a system upgrade is rolled out.

NI Organizations and Journals

One of the first informatics organizations founded was the Healthcare Information and Management Systems Society. HIMSS, which celebrated its 50th year in 2011, was launched in 1961 and now has offices throughout the United States and Europe. HIMSS currently represents 20,000 individuals and 300 corporations. This organization supports both local and national chapters. It has many associated work groups, one of which is an NI work group. HIMSS is well known for its development of industry-wide policies and its educational and professional development initiatives, all of which are directed toward the goal of ensuring safe patient care. Membership in HIMSS offers many advantages for nurses, such as access to numerous weekly and monthly publications, and a scholarly journal, The Journal of Healthcare Information Management. HIMSS offers many educational programs, including virtual expos, which allow participants to experience the expo without having to travel. These educational opportunities allow participants to network with colleagues and peers, which is a valuable asset in this field. To find out more about HIMSS, visit its homepage at http://www.himss.org/ASP/index.asp (HIMSS, 2013). HIMSS also periodically conducts NI workforce surveys; see http://www.himss.org/ResourceLibrary/ResourceDetail.aspx?ItemNumber=11587 for the 2011 results.

The American Medical Informatics Association (AMIA) was founded in 1990 when 3 health informatics associations merged. AMIA currently has more than 3,000 members who reside in 42 countries. This organization focuses on the development and application of biomedical and healthcare informatics. Members include physicians, nurses, dentists, pharmacists, health information technology professionals, and biomedical engineers. AMIA offers many benefits to its members, such as weekly and monthly publications and a scholarly journal, JAMIA—The Journal of the American Medical Informatics Association. Members may join a working group that is specific to their specialty, including an NI work group. AMIA offers multiple educational opportunities and many opportunities for networking with colleagues. To view this information and to see other AMIA offerings, visit http://www.amia.org (AMIA, 2013).

The American Nursing Informatics Association (ANIA) was established in 1992 to provide an opportunity for southern California informatics nurses to meet. It has since grown to a national organization whose members include healthcare professionals who work with clinical IS, educational applications, data collection/research applications, and administrative/DSS, and those who have an interest in the field of NI. In 2009, ANIA merged with the Capital Area Roundtable on Informatics in Nursing (CARING). Membership benefits include the following:

Access to a network of more than 3,200 informatics professionals in 50 states and 30 countries Active e-mail list Quarterly newsletter indexed in CINAHL and Thomson Job bank with employee-paid postings Reduced rate at the ANIA Annual Conference Reduced rate for CIN: Computers, Informatics, Nursing ANIA Online Library of on-demand and webinar education activities Membership in the Alliance for Nursing Informatics Web-based meetings In-person meetings and conferences held nationally and worldwide

To view this information and learn more about ANIA, visit https://www.ania.org/ (ANIA, 2013). The Alliance of Nursing Informatics (ANI) is a coalition of NI groups that represents more than 3,000 nurses and 20 distinct NI

groups in the United States. Its membership represents local, national, and international NI members and groups. These individual groups have developed organizational structures and have established programs and publications. ANI functions as the link between NI organizations and the general nursing and healthcare communities and serves as the united voice of NI. To view this information and learn more about ANI, visit http://www.allianceni.org (Alliance of Nursing Informatics, 2013).

BOX 7-3 NURSING INFORMATICS WEBSITES AND CORRESPONDING JOURNALS

Alliance for Nursing Informatics Website: www.allianceni.org

American Health Information Management Association Website: www.ahima.org Journal: Journal of AHIMA & Perspectives in Health Information Management (online)

American Medical Informatics Association Website: www.amia.org

Journal: JAMIA—Journal of the American Medical Informatics Association NI website: http://www.amia.org/mbrcenter/wg/ni

American Nursing Informatics Association (includes Capital Area Roundtable on Informatics in Nursing [CARING]) Website: www.ania.org Resources link: http://www.ania.org/Resources.htm Journal: CIN: Computers, Informatics, Nursing

Health Information and Management Systems Society Website: www.himss.org Chapter websites: http://www.himss.org/ASP/chaptersHome.asp Journal: The Journal of Healthcare Information Management NI website: http://www.himss.org/asp/topics_nursingInformatics.asp

International Medical Informatics Association Website: www.imia.org Journal: International Journal of Medical Informatics NI website: http://www.imia.org/ni

Online Journal of Nursing Informatics Website: http://www.ojni.org

These groups have been instrumental in establishing the informatics community. Many other informatics groups that have not been covered here also exist. Box 7-3 lists some of these organizations and their publications.

The Future of Nursing Informatics NI is in its infancy, as is the technology that the INS uses on a daily basis. NI will continue to influence development of the EHR. In turn, the EHR will continue to improve and will one day accurately capture the care nurses give to their patients. This is a formidable challenge because much of the care provided by nurses is intangible in nature. In the future, the EHR will provide data to the INS that can then be used to improve nursing workflow and to determine whether current practices are the most efficient and beneficial to the patient.

Nursing and health care are on a roller-coaster ride that will undoubtedly prove very interesting. New technology is being introduced at a breakneck speed, and nursing and health care must be ready to ride this roller coaster. Programs need to be developed to keep nurses and healthcare workers abreast of the new technological changes as they occur, and educating new and current nurses presents a significant challenge to the INS. Overall, the INS’s future looks very promising and rewarding.

In the future, all healthcare providers will likely receive education on informatics. All healthcare providers need basic informatics skills, such as the ability to use search engines to find information about a specific topic. Consequently, all healthcare providers need to be able to attend classes to improve their computer literacy. Those entering the nursing field need a general knowledge of computer capabilities. Many new trends—such as Web 2.0, increased attention to evidence-based practice, and a better understanding of genomics—will impact care delivery in the 21st century, and NI nurses need to be prepared to lead these efforts to improve care (Bakken et al., 2008).

Change plays a significant part in health care today, and those interested in NI must embrace change. They must also be good at enticing others to embrace change. Nevertheless, NI candidates must realize that change is often accompanied by resistance. For their part, INSs must be ready to leave the bedside, because nurses entering into this field will no longer be giving hands-on care.

NI is a very challenging but very rewarding field. In an ideal world, all healthcare agencies will employ at least one INS, and all nurses will embrace the knowledge worker title.

Summary Nursing informatics is an emerging nursing specialty that combines nursing science, information science, and computer science. Informatics practices support nurses as they seek to care for their patients effectively and safely, by making the information that they need more readily available. Nurses have been actively involved in this field since computers were introduced to health care. With the advent of the EHR, it became apparent that nursing needed to develop its own terminology related to the new technology and its applications; NI has been instrumental in this process.

Today, the healthcare industry employs the largest number of knowledge workers in the world. As a consequence of this trend, healthcare administrators now realize that they must begin to change the way that they view their employees. Nurses and physicians are bright, highly skilled, and dedicated to giving the best patient care. Administrators who tap into this wealth of knowledge will discover that they have happier employees and find that patient care has become safer and more efficient.

NI is a specialty governed by standards that have been established by the ANA. Because NI is a very diverse field, many INSs eventually specialize in one segment of the field. NI is a recognized specialty, but it affects all nurses. Nursing informatics competencies have been developed to ensure that all entry-level nurses are ready to enter the more technologically advanced field of nursing. These competencies may be used to determine the educational needs of current staff members.

The growth of the NI field has resulted in the formation of numerous NI organizations or subgroups of the medical informatics organizations. Nurses no longer have to enter the field by chance but can obtain an advanced degree in NI at many well-established universities. In addition, INSs may continue their learning by attending the numerous conferences offered.

NI has grown tremendously as a specialty since its inception and has the expectation of continued growth. It will be interesting to see where technology takes health care in the future.

THOUGHT-PROVOKING QUESTIONS

1. A hospital is seeking to implement an EHR. It has been suggested that an INS be hired. This position does not involve direct patient care and the administration is struggling with how to justify the position. How can this position be justified?

2. This chapter discusses the fact that nurses are knowledge workers. How does nursing move from measuring the tasks completed to measuring the final outcome of the patient?

References Alliance of Nursing Informatics. (2013). Homepage. http://www.allianceni.org American Medical Informatics Association. (2013). Homepage.

http://www.amia.org American Nurses Association (ANA). (2008). Nursing informatics: Scope and standard of practice. Silver Spring, MD: Nursesbooks.org. American Nursing Informatics Association (ANIA). (2013). Homepage. http://www.ania.org Androwich, I. (2010, June). Paper presented at Delaware

Valley Nursing Informatics Annual Meeting, Malvern, PA. Bakken, S., Stone, P., & Larson, E. (2008). A nursing informatics research agenda for 2008–2018: Contextual influences and key components. Nursing

Outlook, 56(5), 206–214. Barton, A. J. (2005). Cultivating informatics competencies in a community of practice. Nursing Administration Quarterly, 29(4), 323–328. Brennan, P. F. (1994). On the relevance of discipline to informatics. Journal of the American Medical Informatics Association, 1(2), 200–201. Davenport, T. H., Thomas, R., & Cantrell, S. (2002). The mysterious art and science of knowledge worker performance. MIT Sloan Management

Review, 44(1), 23–30. Drucker, P. F. (1992). The new society of organizations. Harvard Business Review, 70(5), 95–104. Drucker, P. F. (1994). The age of social transformation. Atlantic Monthly, 274(5), 52–80. Health Information and Management Systems Society (HIMSS). (2006). HIMSS dictionary of healthcare information technology terms, acronyms and

organizations. Chicago, IL: Author. Health Information and Management Systems Society (HIMSS). (2013). Homepage. http://www.himss.org HIMSS Nursing Informatics Awareness Task Force. (2007). An emerging giant: Nursing informatics. Nursing Management, 13(10), 38–42. McCormick, J. (2009, May 14). Preparing for the future of knowledge work: A day in the life of the knowledge worker. Infomanagment Direct Online.

http://www.information-management.com/infodirect/2009_121/knowledge_worker_information_management_ecm_content_management- 10015405-1.html?pg=1

McNeil, B. J., Elfrink, V., Beyea, S. C., Pierce, S., & Bickford, C. J. (2006). Computer literacy study: Report of qualitative findings. Professional Nursing, 22(1), 52–59.

Merriam-Webster Online. (2011). http://mw1.merriam-webster.com/dictionary/worker O’Connor, J. J., & Robertson, E. F. (2003). Florence Nightingale biography. http://www-history.mcs.st-

andrews.ac.uk/history/Printonly/Nightingale.html Saba, V. K., & McCormick, K. A. (Eds.). (2006). Essentials of nursing informatics (4th ed.). New York, NY: McGraw-Hill. Snyder-Halpern, R., Corcoran-Perry, S., & Narayan, S. (2001). Developing clinical practice environments supporting the knowledge work of nurses.

Computers in Nursing, 19(1), 17–26. Sorrells-Jones, J., & Weaver, D. (1999a). Knowledge workers and knowledge-intense organizations, Part 1: A promising framework for nursing and

healthcare. Journal of Nursing Administration, 29(7/8), 12–18. Sorrells-Jones, J., & Weaver, D. (1999b). Knowledge workers and knowledge-intense organizations, Part 3: Implications for preparing healthcare

professionals. Journal of Nursing Administration, 29(10), 14–21. Staggers, N., Gassert, C., & Curran, C. (2001). Informatics competencies for nurses at four levels of practice. Journal of Nursing Education, 40(7), 303–

316. Staggers, N., Gassert, C., & Curran, C. (2002). A Delphi study to determine informatics competencies for nurses at four levels of practice. Nursing

Research, 51(6), 383–390. Thede, L. Q. (2003). Informatics and nursing: Opportunities and challenges (2nd ed.). Philadelphia, PA: Lippincott Williams & Wilkins. TIGER Initiative. (2006). Welcome to TIGER! http://www.tigersummit.com Weaver, D., & Sorrells-Jones, J. (1999). Knowledge workers and knowledge-intense organizations, Part 2: Designing and managing for productivity.

Journal of Nursing Administration, 29(9), 19–25. Wickramasinghe, N., & Ginzberg, M. J. (2001). Integrating knowledge workers and the organization: Role of IT. International Journal of Health Care

Quality Assurance, 14(6), 245–253.

Chapter 8

Information and Knowledge Needs of Nurses in the 21st Century Lynn M. Nagle, Nicholas Hardiker, Kathleen Mastrian, and Dee McGonigle

OBJECTIVES

1. Describe the goal of nursing informatics. 2. Explore the need for consistent terminology in nursing. 3. Describe the different approaches to terminology development. 4. Describe how clinical information technologies are impacting and will impact nursing practice. 5. Explore how nurses can create and derive clinical knowledge from information systems. 6. Speculate on the future of nursing in the context of health informatics.

Key Terms

Accessibility Clinical decision support Clinical information system Enumerative approach Evidence-based practice International Classification of Nursing Practice Nursing informatics Nursing knowledge Ontological approach Ontology Reusability Standardized nursing terminology Term Terminology Ubiquity

Introduction The information and knowledge informing the 21st century of healthcare delivery have been growing at an unprecedented pace in recent years. Research in particular has propelled the understanding of the efficacy of various clinical practices, treatment regimens, and interventions. Extended and expanded access to clinical research findings and decision support tools has been significantly influenced by the advent of computerization and the Internet. Indeed, the conduct of research itself has been accelerated by virtue of ubiquitous computing. Working in environments of increasingly complex clinical care and contending with the management of large volumes of information, nurses need to avail themselves of the technological tools that can support quality practice that is optimally safe, informed, and knowledge based. Although the increased deployment of information technologies within healthcare settings presumes that nurses and other health professionals are proficient in the use of computing devices, the processes and potential outcomes associated with informatics are yet to be fully realized or understood. Nurses need to participate in the creation of those possibilities.

This chapter defines and addresses the goal of informatics as it relates to nursing. More specifically, benefits to be derived from the creation of a culture of knowledge-based nursing practice that is enabled and advanced through the use of information and communication technologies are described. The chapter also addresses some of the challenges associated with the attainment of this goal, as well as the opportunities for nurses to create and derive knowledge from emerging health information technologies. Finally, the chapter provides a contemplative view of the future for nurses and informatics.

CASE STUDY: CASTING TO THE FUTURE

In the year 2025, nursing practice enabled by technology has created a professional culture of reflection, critical inquiry, and interprofessional collaboration. Nurses use technology at the point of care in all clinical settings (e.g., primary care, acute care, community, and long-term care) to inform their clinical decisions and effect the best possible outcomes for their clients. Information is gathered and retrieved via human–technology biometric interfaces including voice, visual, sensory, gustatory, and auditory interfaces, which continuously monitor physiologic parameters for potentially harmful imbalances. Longitudinal records are maintained for all citizens from their initial prenatal assessment to death; all lifelong records are aggregated into the knowledge bases of expert systems. These systems provide the basis of the artificial intelligence being embedded in emerging technologies. Smart technologies and invisible computing are ubiquitous in all sectors where care is delivered. Clients and families are empowered to review and contribute actively to their record of health and wellness. Invasive diagnostic techniques are obsolete, nanotechnology therapeutics are the norm, and robotics supplement or replace much of the traditional work of all health professions. Nurses provide expertise to citizens to help them effectively manage their health and wellness life plans, and navigate access to appropriate information and services.

Definition and Goal of Informatics Recall the definition of nursing informatics (NI) presented in the Overview of Nursing Informatics chapter (Staggers & Thompson, 2002):

A specialty that integrates nursing science, computer science, and information science to manage and communicate data, information, and knowledge in nursing practice. NI facilitates the integration of data, information, and knowledge to support patients, nurses, and other providers in their decision-making in all roles, and settings. This support is accomplished through the use of information structures, information processes, and information technology. (p. 260)

Nurses in identified informatics roles typically focus their efforts on articulating meaningful clinical nursing data and information structures that can be codified and processed; identifying the information processes associated with nurses’ work; and determining ways in which information and communication technologies can be most effectively used to support the capture, retrieval, and use of data, information, and knowledge. For nurses in other roles, however, the term “informatics” remains substantively obscure and misunderstood, if understood at all, as do the relevance and importance of the associated work.

More timely access to data and information—both clinical and financial—has been identified as a necessity in the climate of healthcare delivery in the 21st century (Hannah, 1995). Health service organizations, societies, and governments throughout the industrialized world are obsessed with ensuring that healthcare delivery is safer, knowledge based, cost-effective, seamless, and timely. Beyond these deliverables, there are expectations of improved efficiency and quality and of the active engagement of consumers in their care. In particular, given the evolving emphasis on such issues as chronic disease management and aging at home, the goals of informatics need to include the use of technologies to empower citizens to manage their own health and wellness more effectively.

A current challenge within the nursing profession is the pending human resources crisis and dire projections of imminent nurse shortages. Consequently, nursing’s focus on information technology (IT) has been elevated as a central means by which nurses can be sufficiently supported in their work environments. Most importantly, IT has the potential to reduce the waste of valuable nursing resources by reducing the time spent in the “care and feeding” of patient records. Having more time for direct client care that is supported by ready access to information and knowledge translates into the provision of safer, higher quality care. Nurses need to be appropriately equipped with the tools to manage data, information, and knowledge effectively and efficiently. The work of nurse informaticists has become germane to the future of all nurses’ work.

Health Information Technologies Impacting Nursing As established in the Informatics Roles and the Knowledge Work of Nursing chapter, nurses are classified as knowledge workers. Studies have identified that, depending on the setting, nurses spend between 25% and 50% of their day managing and recording clinical information and seeking knowledge to inform their practice (Gugerty et al., 2007). Nurses gather atomic-level data (e.g., blood pressure, pulse, blood glucose, pallor), aggregate data to derive information (e.g., impending shock), and apply knowledge (e.g., lowering the head of the bed to minimize the potentially deleterious effects of impending shock). Over the years, these data have been recorded into individuals’ hard-copy health records, thereby chronicling findings, actions, and outcomes; these data and information are then forever lost unless manually extracted for research purposes.

As evidence to support nursing practice continues to be uncovered by researchers and integrated into healthcare delivery, attention must be given to the tools that afford ready and easy access to this evidence. It could be suggested that as knowledge workers, nurses are also, albeit unwittingly, informaticians to a large extent. With the advent of clinical information systems (CISs), specifically electronic documentation and clinical decision support (CDS) applications, every nurse has the capacity to contribute to the advancement of nursing knowledge on many levels. Imagine the use of IT solutions to capture not only discrete, quantifiable data, but also the nurse’s experiential and intuitive personal knowledge not typically documented in paper records. Further add to that mix the family history, culture, environmental and social factors, past experiences, and perspectives from patients and families, and it becomes clear that the possibilities for generating new understandings within populations and across the life span and care continuum are endless.

The impact of CISs on the practice of nursing is just beginning to be explicated because the opportunities to study wholly computerized clinical environments have been limited to date. As yet, the evidence that CISs actually improve nursing efficiency has been inconclusive. Poissant, Pereira, Tamblyn, and Kawasumi (2005) found that bedside terminals and central station desktops resulted in a 24% reduction in the time nurses spent on documentation activities. However, surveys of nurses’ perceptions and attitudes toward CISs have suggested that although the quality of documentation may improve, the amount of time associated with completing computer-related tasks increases (DesRoches, Donelan, Buerhaus, & Zhonghe, 2008; Kossman & Scheidenhelm, 2008). Others have

not found a significant time savings for nurses using specific CIS applications (Asaro & Boxerman, 2008; Franklin, O’Grady, Donyai, Jacklin, & Barber, 2007; Hakes & Whittington, 2008). A 2005 Health Information Management Systems Society survey of nurses (n = 1,760) revealed that most nurses believe that CISs improve patient safety (86%) and facilitate interdisciplinary collaboration (69%) and independent decision making (72%) (Dykes et al., 2005).

A 2011 study by Hripcsak, Vawdrey, Fred, and Bostwick used clinical log data to track various members of the healthcare team and their time spent generating and reading clinical notes. Nurses spent between 21.4 and 38.2 minutes per day authoring notes and between 9.0 and 16.9 minutes per day viewing notes. This study did not track minutes per day spent on documenting items on flow sheets reflecting the plan of care, which most likely accounts for the bulk of nursing documentation time. Perhaps the most interesting finding of this study is the low rate at which members of the team actually read the notes generated by members of the team.

These findings are reflective of what promises to be a growing trend in clinical settings as the sophistication and functionality of CISs continue to advance. However, more research is needed to understand the full extent of the impact of the current and future CISs on nursing practice.

Nurses Creating and Deriving New Knowledge Nursing Data Standards There are major efforts under way—internationally through the International Council of Nurses’ (2010) International Classification of Nursing Practice (ICNP) and in many other initiatives among and within countries—in which nurses are attempting to standardize the language of nursing practice (Hannah, White, Nagle, & Pringle, 2009). These efforts are particularly important in the face of CISs, because the capacity to enforce consistent nomenclatures that reflect the practice of nurses is now possible. Standardized language gives both the nursing profession and healthcare delivery systems the capability to capture, codify, retrieve, and analyze the impact of nursing care on client outcomes. For example, with the use and documentation of standardized client assessments, including risk measures, interventions based on best practices, and consistently measured outcomes within different care settings and across the continuum of care, there will be an ability to demonstrate more clearly the contributions and impact of nursing care through the analysis of CIS outputs. Additionally, clinical outcomes can be further understood in the context of care environments, particularly implications related to the availability of human and material resources to support care delivery. The standardization of clinical inputs and outputs into CISs will eventually provide a rich knowledge base from which practice and research can be enhanced, and will inform better administrative and policy decisions (Nagle, White, & Pringle, 2010).

Although significant progress has been made in this standardization work, it is still in its early days. Box 8-1 discusses standardizing terminologies in nursing; it was contributed by Nicholas Hardiker (2011), a leader in the development of standardized languages that support clinical applications of information and communication technology.

BOX 8-1 THE NEED FOR STANDARDIZED TERMINOLOGIES TO SUPPORT NURSING PRACTICE Nicholas Hardiker Agreement on the consistent use of a term, such as “impaired physical mobility,” allows that term to be used for a number of purposes: to provide continuity of care from care provider to care provider, to ensure care quality by facilitating comparisons between care providers, or to identify trends through data aggregation. Since the early 1970s, there has been a concerted effort to promote consistency in nursing terminology. This work continues today, driven by the following increasing demands placed on health-related information and knowledge:

Accessibility: It should be easy to access the information and knowledge needed to deliver care or manage a health service. Ubiquity: With changing models of healthcare delivery, information and knowledge should be available anywhere. Longevity: Information should be usable beyond the immediate clinical encounter. Reusability: Information should be useful for a range of purposes.

Without consistent terminology, nursing runs the risk of becoming invisible; it will remain difficult to quantify nursing, the unique contribution and impact of nursing will go unrecognized, and the nursing component of electronic health record systems will remain at best rudimentary. Not least, without consistent terminology, the nursing knowledge base will suffer in terms of development and in terms of access, thereby delaying the integration of evidence-based health care into nursing practice.

External pressures merely compound this problem. For example, in the United States, the Health Information Technology for Economic and Clinical Health (HITECH) Act, signed in January 2009, provides a financial incentive for the use of electronic health records; similar steps are being taken in other regions. The HITECH Act mandates that electronic health records are used in a meaningful way; achieving this goal will be problematic without consistent terminology (see the Legislative Aspects of Nursing Informatics: HITECH and HIPAA chapter for more information on the HITECH Act). Finally, the current and future landscape of information and communication technologies (e.g., connection anywhere, borderless communication, Web-based applications, collaborative working, disintermediation and reintermediation, consumerization, ubiquitous advanced digital content [van Eecke, da Fonseca Pinto, & Egyedi, 2007]) and their inevitable infiltration into health care will only serve to reinforce the need for consistent nursing terminology while providing an additional sense of urgency.

This box explains what is meant by a standardized nursing terminology and lists several examples. It describes in detail the different approaches taken in the development of two example terminologies. It presents, in the form of an international technical standard, a means of ensuring consistency among the plethora of contemporary standardized nursing terminologies, with a view toward harmonization and possible convergence. Finally, it provides a rationale for the shared development of models of terminology use—models that embody both clinical and pragmatic knowledge to ensure that contemporary nursing record systems reflect the best available evidence and fit comfortably with routine practice.

STANDARDIZED NURSING TERMINOLOGIES A term at its simplest level is a word or phrase used to describe something concrete (e.g., leg) or abstract (e.g., plan). A nursing terminology is a body of the terms used in nursing. Many nursing terminologies exist, both formal and informal. Nursing terminologies allow nurses to consistently capture, represent, access, and communicate nursing data, information, and knowledge. A standardized nursing terminology is a nursing terminology that is in some way approved by an appropriate authority (de jure standardization) or by general consent (de facto standardization).

In North America, one such authority is the American Nurses Association (ANA, 2007), which operates a process of de jure standardization through its Committee for Nursing Practice Information Infrastructure (CNPII; http://www.nursingworld.org/MainMenuCategories/Policy-

Advocacy/Positions-and-Resolutions/ANAPositionStatements/Position-Statements-Alphabetically/PrivacyandConfidentiality.html). The ANA- approved list of nursing languages was presented in the Overview of Nursing Informatics chapter.

CNPII has also recognized two data element sets: the Nursing Minimum Data Set (NMDS) and the Nursing Management Minimum Data Set (NMMDS). Work on a standardized data element set for nursing, which in the United States began in the 1980s with the NMDS (Werley & Lang, 1988), provided an additional catalyst for the development of many of the aforementioned nursing terminologies that could provide values (e.g., chronic pain) for particular data elements in the NMDS (e.g., nursing diagnosis). The data element sets provide a framework for the uniform collection and management of nursing data; the use of a standardized nursing terminology to represent those data serves further to enhance consistency.

APPROACHES TO NURSING TERMINOLOGY From relatively humble beginnings, nursing terminologies have evolved significantly over the past several decades in line with best practices in

terminology work. The enumerative approach consists of simple lists of words or phrases represented in a list or a simple hierarchy. In the nursing diagnosis terminology system of the North American Nursing Diagnosis Association (NANDA), a nursing diagnosis has an associated name or label and a textual definition (NANDA International, 2008). Each nursing diagnosis may have a set of defining characteristics and related or risk factors. These additional features do not constitute part of the core terminology but instead are intended to be used as an aid to diagnosis. What an enumerative approach to standardizing terminology may lack in terms of hierarchical sophistication, it makes up for in terms of simplicity and potential ease of implementation and use.

In contrast, the ontological approach is compositional in nature and provides a partial representation of the entities within a domain and the relationships that hold between them. The evolution of this approach to terminology standardization has been facilitated by advances in knowledge representation (e.g., the refinement of the description logic that underpins many contemporary ontologies) and in their accompanying technologies (e.g., automated reasoners that can check consistency and identify equivalence) as well as the subsumption (i.e., subclass–superclass) relationships within those ontologies.

ICNP version 2 is an example of an ontology. ICNP is described as a unified nursing language system. It seeks to provide a resource that can be used to develop local terminologies and to facilitate cross-mapping between terminologies to compare and combine data from different sources; the existence of a number of overlapping but inconsistent standardized nursing terminologies is problematic in terms of data comparison and aggregation. The core of ICNP is represented in the Web ontology language (OWL), a recommendation of the World Wide Web Consortium (W3C) and a de facto standard language for representing ontologies (McGuiness & van Harmelen, 2004). Because it is underpinned by description logic, OWL permits the use of automated reasoners that can check consistency, identify equivalence, and support classification within the ICNP ontology.

The results of contemporary terminology work are encouraging. Nevertheless, further work is needed to harmonize standardized nursing terminologies and to scale up and mainstream the development and implementation of models of terminology use.

In an ideal world, one would see standardized nursing terminologies and the structures and systems that support their implementation and use merely as means to an end—that is, as tools to support good nursing practice and good patient care. Standardized nursing terminologies are important, but they do not obviate the need to think and work creatively, to do right by the people in our care, and to continue to advance nursing.

REFERENCES American Nurses Association (ANA). (2007). Nursing practice information infrastructure. http://www.nursingworld.org/MainMenuCategories/Policy-Advocacy/Positions-and-Resolutions/ANAPositionStatements/Position-Statements- Alphabetically/PrivacyandConfidentiality.html McGuiness, D. L., & van Harmelen, F. (Eds.). (2004). OWL Web ontology language overview. World Wide Web Consortium.

http://www.w3.org/TR/owl-features NANDA International. (2008). Nursing diagnoses: Definitions and classification 2009–2011 edition. Indianapolis, IN: Wiley-Blackwell. van Eecke, P., da Fonseca Pinto, P., & Egyedi, T., for the European Commission. (2007). EU study on the specific policy needs for ICT

standardisation [Final report]. http://ec.europa.eu/enterprise/ict/policy/doc/2007-ict-std-full-rep.pdf Werley, H. H., & Lang, N. M. (Eds.). (1988). Identification of the Nursing Minimum Data Set. New York, NY: Springer.

Integrated Decision Support Tools

CDS tools have evolved beyond the previously prevailing notion of these tools as accessible reference texts and written resource materials, such as policies and procedures. In the world of clinical computing, the capability to link various information sources and present a clinician with immediate guidance and support has begun to net benefits for safer care and improved clinical outcomes. Osheroff and colleagues (2007) defined CDS as tools that “provide clinicians, staff, patients, or other individuals with knowledge and person-specific information, intelligently filtered or presented at appropriate times, to enhance health and health care” (p. 141).

Most available CDS tools for nursing practice, although promising, are simplistic and in early development. Typically, CDS includes such tools as (1) computerized alerts and reminders (e.g., medication due, patient has an allergy, potassium level abnormal); (2) clinical guidelines (e.g., best practice for prevention of skin breakdown); (3) online information retrieval (e.g., CINAHL, drug information); (4) clinical order sets and protocols; and (5) online access to organizational policies and procedures. In the future, these tools may be expanded to include applications with embedded case-based reasoning.

Nursing Knowledge in Evolution

Many renowned nurse authors have described the knowledge used by nurses (Benner, 1983; Carper, 1978; Schultz & Meleis, 1988). According to Carper (1978), “nursing … depends on the scientific knowledge of human behavior in health and in illness, the esthetic perception of significant human experiences, a personal understanding of the unique individuality of the self and the capacity to make choices within concrete situations involving particular moral judgments” (p. 22).

In the context of nursing practice supported by CISs, nurses will eventually have access to evidence and knowledge derived from large aggregates of clinical data, including nursing interventions and resultant outcomes. Experiential evidence provides practice guidelines and directives to ensure concurrence with optimal clinical decisions and actions. To illustrate, consider this example: A nurse assesses a patient who has experienced a stroke for signs of skin breakdown, photographs and documents early ulcerations, and submits the photos and documentation to CIS. The nurse receives an option to review the best practices for care of the patient and to submit a request for a consult to a wound management specialist. The ongoing clinical findings, treatment, and response are logged and aggregated with similar cases, thereby contributing to the knowledge base related to nursing and care of the integumentary system.

The informational elements of CISs can also be designed to include specifics about individuals’ multicultural practices and beliefs.

Consider the situation where a client voices concerns about her prescribed dietary treatment and expresses a preference for a female care provider. With a query to the CIS for the client’s history and sociocultural background, the nurse obtains explanations for these requests that derive from the patient’s religious and cultural background and makes a notation to highlight and carry this information forward for any future admissions. Future systems may also be designed to provide access to standards of ethical practice and online access to experts in the field of moral reasoning to guide clinical interactions and decision making.

Through each and every instance of interacting with the CIS, nurses add to these repositories of knowledge by chronicling their daily clinical challenges and queries. The continued expansion and aggregation of knowledge about clients and populations; their personal, cultural, physical, and clinical presentations; and individuals’ experiences and the guidance received from others enhance the delivery of personalized, knowledge-based care.

Generating Nursing Knowledge Graves and Corcoran (1989) have suggested that nursing knowledge is “simultaneously the laws and relationships that exist between the elements that describe the phenomena of concern in nursing (factual knowledge) and the laws or rules that the nurse uses to combine the facts to make clinical nursing decisions” (p. 230). In their view, not only does knowledge support decision making, but it also leads to new discoveries. Thus one might think about the future creation of nursing knowledge as being the discovery of new laws and relationships that can continue to advance nursing practice.

New technologies have made the capture of multifaceted data and information possible through the use of such technologies as digital imaging (e.g., photography to support wound management). Now included as part of the clinical record, such images add a new dimension to the assessment, monitoring, and treatment of illness and the maintenance of wellness. Beyond the use of computer keyboards, input devices are being integrated with CISs and used to gather data and information for the following clinical and administrative purposes:

Biometrics (e.g., facial recognition, security) Voice and video recordings (e.g., client interviews and observations, diagnostic procedures, ultrasounds) Voice-to-text files (e.g., voice recognition for documentation) Medical devices, (e.g., infusion pumps, ventilators, hemodynamic monitors) Bar-code and radio-frequency identification (RFID) technologies (e.g., medication administration) Telehomecare monitoring (e.g., for use in diabetes and other chronic disease management)

These are but a few of the emerging capabilities that allow for numerous data inputs to be transposed, combined, analyzed, and displayed to provide information and views of clinical situations currently not possible in a world dominated by hard-copy documentation. Through the application of information and communication technologies to support the capture and processing (i.e., interpretation, organization, and structuring) of all relevant clinical data, relationships can be identified and formalized into new knowledge. This transformational process is at the core of generating new nursing knowledge at a rate never experienced before; in the context of current research paradigms, the same relationships would likely take years to uncover.

As CISs advance, nurses will eventually become generators of new knowledge by virtue of designs that embed machine learning and case-based reasoning methods within their core functionality. This functionality will become possible only with national and international adoption of standardized nursing language, as previously described. Imagine the power of having access to systems that aggregate the same data elements and information garnered from multiple clinical situations and provide a probability estimate of the likely outcome for individuals of a certain age, with a specific diagnosis and comorbid conditions, medication profile, symptoms, and interventions. How much more rapidly would an understanding of the efficacy of clinical interventions be elucidated? Historically, some knowledge might have taken years of research to discover (e.g., that long-standing practices are sometimes more harmful than beneficial). A case in point is the long-standing practice of instilling endotracheal tubes with normal saline before suctioning (O’Neal, Grap, Thompson, & Dudley, 2001). Based on the evidence gathered through several studies, the potentially deleterious effects of this practice have become widely recognized. Conceivably, a meta-analysis approach to clinical studies will be expedited by convergence of large clinical data repositories across care settings, thereby making available to practitioners the collective contributions of health professionals and longitudinal outcomes for individuals, families, and populations.

Nurses need to be engaged in the design of CIS tools that support access to and the generation of nursing knowledge. Of particular importance to the future design of CIS is the adoption of clinical data standards. Although this research is beyond the scope of this chapter, at least two decades of work effort has been directed toward articulating standardized data elements that reflect nursing practice. The nursing profession has been steadily moving toward consensus on the adoption of data standards, and recent work suggests that significant strides have been achieved (Bickford & Hunter, 2006; Delaney, 2006; Hannah et al., 2009). Consider that as CISs are widely implemented, as standards for nursing documentation and reporting are adopted, and as healthcare IT solutions continue to evolve, the synthesis of findings from a variety of methods and worldviews becomes much more feasible.

Challenges in Getting There Leadership The number of nurse leaders in health informatics has grown to a marked extent in the past two decades. Nevertheless, a significant knowledge gap remains to be addressed within the nursing leadership community. Many nurse leaders need to acquire a new set of skills and knowledge to understand and advance the adoption of information tools and technologies to support the delivery of clinical care. For several years, nurse informaticians have advocated for the need for all nursing leaders to become knowledgeable and engaged in setting the direction for informatics in the profession (Nagle, 2005; Pringle & Nagle, 2009; Simpson, 2000). Strategies for achieving this goal include the following: (1) identify the informatics education needs of nurse leaders, (2) develop mentorship programs for the

acquisition of informatics leadership skills, and (3) ensure enrollment of nurse leaders as sponsors for electronic health record (EHR) initiatives.

Clinical Practice

Despite valiant efforts to implement comprehensive CIS throughout North American healthcare settings, there are still many provider organizations with limited online functionality available to nurses. As indicated by numerous studies and reports on the state of IT adoption, many providers remain in the early phases of CIS acquisition and implementation (Eggert & Protti, 2006). In an unexpected twist, this lag is probably good news for nursing; it means there is an opportunity for nurses to immerse themselves in the developmental work of IT solutions to support practice.

Over the years, nurses have frequently been on the receiving end of systems that either did not add value to their work or, by virtue of their poor design, created additional work. Now nurses have the opportunity to head off future installations of IT solutions that do nothing to benefit and support the clinical practice of nurses and healthcare teams. It behooves nurses to become engaged in the acquisition, design, implementation, and evaluation of CIS to ensure that they realize the benefits of these systems for clinical care and outcomes.

It is equally important to consider that because of the average age of most practicing nurses, many have yet to develop a comfort level with the use of computers in their work settings. To minimize the anxiety associated with expected IT use, particular attention needs to be given to the issue of computer literacy. If nurses lack a solid footing in computer use, expectations for integration of informatics will prove difficult to realize. Strategies for nurses include the following: (1) seek encouragement and support to participate in the acquisition, design, implementation, and evaluation phases of CIS; (2) demand the adoption of IT solutions that support the delivery of safe, quality care; and (3) obtain the material and people resources needed to support their acquisition of informatics competencies.

Education

Over the years, numerous efforts have been undertaken to identify the core informatics competencies needed by nurses. These efforts have encompassed attempts to articulate core competencies for all nurses, from novice to expert (Hebert, 2000) and competencies for informatics experts (Hersh, 2006). In recognizing NI as a specialty, the American Nurses Association (2008) has articulated scope and standards of NI practice. What remains clear is that although progress has been made in the preparation of NI experts, much work remains to be done at the grassroots level of nursing education.

Studies of schools of nursing indicate that few basic nursing education programs have embedded the concepts and processes associated with informatics within the core curricula (Carty & Rosenfeld, 1998; Nagle & Clarke, 2004). Nevertheless, informatics content is now being slowly integrated into curricula. The primary obstacles to realizing curricula with embedded informatics concepts include a lack of faculty capacity, constraints of clinical practice environments (e.g., lack of student access to CISs), and limited funding. These barriers need to be addressed to ensure that graduates of the future are prepared to work in settings using information technology to support clinical care.

The core concepts and competencies of informatics are particularly well suited to a model of interprofessional education. Ideally, when emulating clinical settings, informatics knowledge should be integrated with the processes of interprofessional teamwork and decision making. Because simulation laboratories are becoming increasingly common fixtures in the delivery of health professional education, they provide a perfect opportunity to incorporate EHR applications, including access to CDS. The learning laboratory will then more closely approximate the IT-enabled clinical settings that are emerging in the real world.

An assumption is often made that future graduates will be more computer literate than the nurses currently in practice. Although this is likely true, computer comfort does not equate to an understanding of the facilitative and transformative role that IT will have in the future. It is essential that the future curricula of basic nursing programs incorporate the concepts related to the role of information technology in supporting clinical care delivery. Strategies to address the educational issues related to CIS include the following: (1) share prototypes of informatics integration among schools of nursing, (2) consider interprofessional education opportunities in addressing informatics concepts and competencies, (3) obligate nursing faculty to attain basic informatics competencies and support them as they do so, (4) seek and allocate funding for the development of innovative curricular models and associated technological support, and (5) incorporate accreditation criteria that necessitate an integration of informatics core concepts and competencies in all basic nursing programs.

In an Ideal World

The ideal is a nursing practice that has wholly integrated informatics and nursing education and that is driven by the use of information and knowledge from a myriad of sources, creating practitioners whose way of being is grounded in informatics. Nursing research is dynamic and an enterprise in which all nurses are engaged by virtue of their use of technologies to gather and analyze findings that inform specific clinical situations. In every practice setting, the contributions of nurses to health and wellbeing of citizens will be highly respected and parallel, if not exceed, the preeminence granted physicians.

The Future The future landscape is yet to be fully understood, as technology continues to evolve with a rapidity and unfolding that is rich with promise and potential peril. It is anticipated that computing power will be capable of aggregating and transforming additional multidimensional data and information sources (e.g., historical, multisensory, experiential, and genetic sources) into CIS. With the availability of such rich repositories, further opportunities will open up to enhance the training of health professionals, advance the design and application of CDSs, deliver care that is informed by the most current evidence, and engage with individuals and families

in ways yet unimagined. The basic education of all health professions will evolve over the next decade to incorporate core informatics competencies. In

general, the clinical care environments will be connected, and information will be integrated across disciplines to the benefit of care providers and citizens alike. The future of health care will be highly dependent on the use of CIS and CDS to achieve the global aspiration of safer, quality care for all citizens.

Summary This chapter advanced the view that every nurse’s practice will make contributions to new nursing knowledge in dynamically interactive CIS environments. The core concepts and competencies associated with informatics will become embedded in the practice of every nurse, whether administrator, researcher, educator, or practitioner. Informatics will be prominent in the knowledge work of nurses, yet it will be a subtlety because of its eventual fulsome integration with clinical care processes. Clinical care will be substantially supported by the capacity and promise of technology today and tomorrow.

Most importantly, readers need to contemplate a future without being limited by the world of practice as it is known today. Information technology is not a panacea for all of the challenges found in health care, but it will provide the nursing profession with an unprecedented capacity to generate and disseminate new knowledge at rapid speed. Realizing these possibilities necessitates that all nurses understand and leverage the informatician within and contribute to the future.

THOUGHT-PROVOKING QUESTIONS

1. What are the possibilities to accelerate the generation and uptake of new nursing knowledge? 2. What should be the areas of priority for the advancement of informatics in nursing? 3. How can a single agreed-upon model of terminology use (with linkages to a single terminology) help to integrate knowledge into routine clinical

practice?

References American Nurses Association (ANA). (2008). Scope and standards of nursing informatics practice. Washington, DC: Author. Asaro, P.V., & Boxerman, S. B. (2008). Effects of computerized provider order entry and nursing documentation on workflow. Journal of the American

Medical Informatics Association, 14(4), 415–423. Benner, P. (1983). Uncovering the knowledge embedded in clinical practice. Image: The Journal of Nursing Scholarship, 15(2), 36–41. Bickford, C. J., & Hunter, K. M. (2006). Theories, models, and frameworks. In V. K. Saba & K. A. McCormick (Eds.), Essentials of nursing informatics

(4th ed., pp. 265–278). New York, NY: McGraw-Hill. Carper, B. A. (1978). Fundamental patterns of knowing in nursing. Advances in Nursing Science, 1(1), 13–23. Carty, B., & Rosenfeld, P. (1998). From computer technology to information technology: Findings from a national study of nursing education.

Computers in Nursing, 16(5), 259–265. Delaney, C. W. (2006). Nursing minimum data set systems. In V. K. Saba & K. A. McCormick (Eds.), Essentials of nursing informatics (4th ed., pp.

249–261). New York, NY: McGraw-Hill. DesRoches, C. K., Donelan, P., Buerhaus, P., & Zhonghe, L. (2008). Registered nurses’ use of electronic health records: Findings from a national

survey. Medscape Journal of Medicine, 10(7), 164–173. Dykes, P., Cashen, M., Foster, M., Gallagher, J., Kennedy, M., MacCallum, R., … Whetstone, S. (2005). Surveying acute care providers in the U.S. to

explore the impact of HIT on the role of nurses and the interdisciplinary communication in acute care settings. Journal of Healthcare Information Management, 20(2), 36–44.

Eggert, C., & Protti, D. (2006). Clinical electronic communications: A new paradigm that is here to stay? Electronic Healthcare, 5(2), 88–96. Franklin, B. D., O’Grady, K., Donyai, P., Jacklin, A., & Barber, N. (2007). The impact of a closedloop electronic prescribing and administration system

on prescribing errors, administration errors, and staff time: A before and after study. Quality and Safety in Health Care, 16(4), 279–284. Graves, J. R., & Corcoran, S. (1989). The study of nursing informatics. Image: The Journal of Nursing Scholarship, 21(4), 227–231. Gugerty, B., Maranda, M. J., Beachley, M., Navarro, V. B., Newbold, S., Hawk, W., … Wilhelm, D. (2007). Challenges and opportunities in

documentation of the nursing care of patients. Baltimore, MD: Documentation Work Group, Maryland Nursing Workforce Commission. http://www.mbon.org/commission2/documentation_challenges.pdf

Hakes, B., & Whittington, J. (2008). Assessing the impact of an electronic medical record on nurse documentation time. Computers Informatics Nursing, 26(4), 234–241.

Hannah, K. J. (1995). Transforming information: Data management support of healthcare reorganization. Journal of the American Medical Informatics Association, 2(3), 147–155.

Hannah, K. J., White, P., Nagle, L. M., & Pringle, D. M. (2009). Standardizing nursing information in Canada for inclusion in electronic health records: C-HOBIC. Journal of the American Medical Informatics Association, 16(4), 524–530.

Hardiker, N. (2011). Developing standardized terminologies to support nursing practice. In D. McGonigle & K. Mastrian (Eds.), Nursing informatics and the foundation of knowledge (2nd ed., pp. 11–119). Sudbury, MA: Jones & Bartlett Learning.

Hersh, W. (2006). Who are the informaticians? What we know and should know. Journal of the American Medical Informatics Association, 13(2), 166– 170.

Hripcsak, G., Vawdrey, D., Fred, M., & Bostwick, S. (2011). Use of electronic clinical documentation: Time spent and team interactions. Journal of the American Medical Informatics Association, 18, 112–117.

International Council of Nurses. (2010). ICNP version 2.0. http://www.icn.ch/pillarsprograms/international-classification-for-nursing-practicer/ Kossman, S. P., & Scheidenhelm, S. L. (2008). Nurses’ perceptions of the impact of electronic health records on work and patient outcomes. Computers

Informatics Nursing, 26(2), 69–77. Nagle, L. M. (2005). Dr. Lynn Nagle and the case for nursing informatics. Canadian Journal of Nursing Leadership, 18(1), 16–18. Nagle, L. M., & Clarke, H. F. (2004). Assessing informatics in Canadian schools of nursing. Proceedings 11th World Congress on Medical Informatics

[CD]. San Francisco, CA. Nagle, L. M., White, P., & Pringle, D. (2010). Realizing the benefits of standardized measures of clinical outcomes. Electronic Healthcare, 9(2), e3–e9. O’Neal, P. V., Grap, M. J., Thompson, C., & Dudley, W. (2001). Level of dyspnea experienced in mechanically ventilated adults with and without saline

instillation prior to endotracheal suctioning. Intensive Critical Care Nursing, 17(6), 356–363. Osheroff, J. A., Teich, J. M., Middleton, B., Steen, E. B., Wright, A., & Detmer, D. E. (2007). A roadmap for national action on clinical decision

support. Journal of the American Medical Informatics Association, 14(2), 141–145. Poissant, L. J., Pereira, R., Tamblyn, R., & Kawasumi, Y. (2005). The impact of electronic health records on time efficiency of physicians and nurses: A

systematic review. Journal of the American Medical Informatics Association, 12(5), 505–516. Pringle, D., & Nagle, L. M. (2009). Leadership for the information age: The time for action is now. Canadian Journal of Nursing Leadership, 22(1), 1–

6. Schultz, P. R., & Meleis, A. I. (1988). Nursing epistemology: Traditions, insights, questions. Image: The Journal of Nursing Scholarship, 20(4), 217–

221. Simpson, R. L. (2000). Need to know: Essential survival skills for the information age. Nursing Administration Quarterly, 25(1), 142–147. Staggers, N., & Thompson, C. B. (2002). The evolution of definitions for nursing informatics: A critical analysis and revised definition. Journal of the

American Medical Informatics Association, 9(3), 255–261.

Chapter 9

Legislative Aspects of Nursing Informatics: HITECH and HIPAA Kathleen M. Gialanella, Kathleen Mastrian, and Dee McGonigle

OBJECTIVES

1. Describe the purposes of the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009. 2. Explore how the HITECH Act is enhancing the security and privacy protections of the Health Insurance Portability and Accountability Act

(HIPAA) of 1996. 3. Determine how the HITECH Act and its impact on HIPAA apply to nursing practice.

Key Terms

Access Agency for Healthcare Research and Quality American National Standards Institute American Recovery and Reinvestment Act Centers for Medicare and Medicaid Services Certified EHR technology Civil monetary penalties Compliance Confidentiality Consequences Electronic health record Enterprise integration Entity Gramm-Leach-Bliley Act Health disparities Health information technology Health Insurance Portability and Accountability Act Health Level 7 Healthcare-associated infections International Standards Organization Meaningful use National Institute of Standards and Technology Office of Civil Rights Office of the National Coordinator for Health Information Technology Open systems interconnection Patient-centered care Policy Privacy Protected health information Qualified electronic health record Rights Sarbanes-Oxley Act Security Standard Standards-developing organizations Treatment/payment/operations

Introduction The federal Health Information Technology for Economic and Clinical Health Act of 2009 (HITECH Act; Leyva & Leyva, 2011),

enacted February 17, 2009, is part of the American Recovery and Reinvestment Act (ARRA). The ARRA, also known as the “Stimulus” law, was enacted to stimulate various sectors of the U.S. economy during the most severe recession this country had experienced since the Great Depression of the late 1920s and early 1930s. The health information technology (HIT) industry was one area where lawmakers saw an opportunity to stimulate the economy and improve the delivery of health care at the same time. This explains why the title of the HITECH Act contains the phrase “for Economic and Clinical Health.”

The ARRA is a lengthy piece of legislation that is organized into two major sections: Division A and Division B. Each division contains several titles. Title XIII of Division A of the ARRA is the HITECH Act. It addresses the development, adoption, and implementation of HIT policies and standards and provides enhanced privacy and security protections for patient information—an area of the law that is of paramount concern in nursing informatics. Title IV of Division B of the ARRA is considered part of the HITECH Act. It addresses Medicare and Medicaid HIT and provides significant financial incentives to healthcare professionals and hospitals that adopt and engage in the “meaningful use” of electronic health record (EHR) technology.

This chapter presents an overview of the HITECH Act, including the Medicare and Medicaid HIT provisions of the law. Nurses need to be familiar with the goals and purposes of this law, know how it enhances the security and privacy protections of the Health Insurance Portability and Accountability Act (HIPAA) of 1996, and appreciate how it otherwise affects nursing practice in the emerging EHR age. The concepts of “meaningful use” and “certified EHR technology” also are explored in this chapter.

Overview of the HITECH Act At the time the HITECH Act was enacted, it was estimated that less than 8% of U.S. hospitals used a basic EHR system in at least one of their clinical units, and less than 2% of U.S. hospitals had an EHR system in all of their clinical settings (Ashish, 2009). Not surprisingly, the cost of an EHR system has been a major barrier to widespread adoption of this technology in most healthcare facilities. The HITECH Act seeks to change that situation by providing each person in the United States with an EHR. In addition, a nationwide HIT infrastructure will be developed so that access to a person’s EHR will be readily available to every healthcare provider who treats the patient, no matter where the patient may be located at the time treatment is rendered.

Definitions

The HITECH Act includes some important definitions that anyone involved in nursing informatics should know:

“Certified EHR Technology”: an EHR that meets specific governmental standards for the type of record involved, whether it is an ambulatory EHR used by office-based healthcare practitioners or an inpatient EHR used by hospitals. The specific standards that are to be met for any such EHRs are set forth in federal regulations.

“Enterprise Integration”: “the electronic linkage of healthcare providers, health plans, the government and other interested parties, to enable the electronic exchange and use of health information among all the components in the health care infrastructure.

“Healthcare Provider”: hospitals, skilled nursing facilities, nursing homes, long-term care facilities, home health agencies, hemodialysis centers, clinics, community mental health centers, ambulatory surgery centers, group practices, pharmacies and pharmacists, laboratories, physicians, and therapists, among others.

“Health Information Technology” (HIT): “hardware, software, integrated technologies or related licenses, intellectual property, upgrades, or packaged solutions sold as services that are designed for or support the use by healthcare entities or patients for the electronic creation, maintenance, access, or exchange of health information.”

“Qualified Electronic Health Record”: “an electronic record of health-related information on an individual.” A “qualified” EHR contains a patient’s demographic and clinical health information, including the medical history and a list of health problems, and is capable of providing support for clinical decisions and entry of physician orders. It must also have the capacity “to capture and query information relevant to health care quality” and “exchange electronic health information with, and integrate such information from other sources” (Readthestimulus.org, 2009, pp. 32–35).

Purposes

The HITECH Act established the Office of the National Coordinator for Health Information Technology (ONC) within the U.S. Department of Health and Human Services (HHS). The ONC is headed by the national coordinator, who is responsible for overseeing the development of a nationwide HIT infrastructure that supports the use and exchange of information to achieve the following goals:

1. Improve healthcare quality by enhancing coordination of services between and among the various healthcare providers a patient may have, fostering more appropriate healthcare decisions at the time and place of delivery of services, and preventing medical errors and advancing the delivery of patient-centered care

2. Reduce the cost of health care by addressing inefficiencies, such as duplication of services within the healthcare delivery system, and by reducing the number of medical errors

3. Improve people’s health by promoting prevention, early detection, and management of chronic diseases 4. Protect public health by fostering early detection and rapid response to infectious diseases, bioterrorism, and other situations

that could have a widespread impact on the health status of many individuals 5. Facilitate clinical research 6. Reduce health disparities 7. Better secure patient health information

Improving healthcare quality has been an ongoing challenge in the United States. According to the Agency for Healthcare Research and Quality (AHRQ), quality health care is care that is “safe, timely, patient centered, efficient, and equitable” (AHRQ, 2009, p. 1). AHRQ, an agency within HHS, has been releasing a national healthcare quality report (NHQR) every year since 2003, and the current report, not unlike previous reports, finds the quality of health care in this country to be “suboptimal” (p. 2). The NHQR has also discussed the need for HIT to support the goal of improving quality of care.

Providers need reliable information about their performance to guide improvement activities. Realistically, HIT infrastructure is needed to ensure that relevant data are collected regularly, systematically, and unobtrusively while protecting patient privacy and confidentiality … Systems need to generate information that can be understood by end users and that are interoperable across different institutions’ data platforms …

Quality improvement typically requires examining patterns of care across panels of patients rather than one patient at a time … Ideally, performance measures should be calculated automatically from health records in a format that can be easily shared and compared across all providers involved with a patient’s care. (AHRQ, 2009, p. 13)

The prevalence of healthcare-associated infections serves as an excellent example of how use of EHR technology and a nationwide HIT infrastructure can play a significant role in addressing healthcare quality issues. According to the NHQR, “wound infections are a common occurrence following surgery, but hospitals can reduce the risk of these health care–associated infections by making sure patients receive an appropriate antibiotic within an hour before their procedures” (AHRQ, 2009, p. 110). The Centers for Medicare and Medicaid Services (CMS) already has the capacity to track Medicare patients who receive this prophylactic treatment and the rate of postsurgical wound infections for those patients who do and do not receive the treatment. Imagine being able to track this issue for all surgical patients and developing evidence-based care plans to ensure that all patients within the infrastructure receive the same quality of care. This is just one of many examples in which the end result of EHR adoption is better patient outcomes.

EHR technology also will make it easier for all providers involved in a patient’s care to readily access that patient’s complete and current healthcare record, thereby allowing providers to make well-informed, efficient, and effective decisions about a patient’s care at the time those decisions need to be made. This is of tremendous benefit to the patient and promotes a higher level of patient-centered care. It also allows effective coordination of care between and among all providers involved in the patient’s care, including doctors, nurses, therapists, nutritionists, hospitals, nursing homes, rehabilitation facilities, home health agencies, laboratories, and other diagnostic centers, thereby assuring the continuum of patient care.

Such an integrated system would have clear benefits for patients and providers alike. For example, imagine how much easier it would be for a patient with a rare form of cancer to obtain a second oncologist’s opinion before beginning a course of treatment. The patient’s complete record, including the results of numerous diagnostic tests conducted at multiple sites, such as blood tests, biopsies, radiographs, and scans, would be readily available to the second oncologist. Imagine how much easier it would be for a patient with end-stage renal disease, who is receiving outpatient hemodialysis several times a week, to receive appropriate treatment if he or she is suddenly hospitalized or would like to take a vacation out of state. Imagine how much easier it would be for nurses to complete a medication reconciliation for a newly admitted patient. The possibilities are endless, and the savings realized from enhancing quality, avoiding duplication of services, and streamlining delivery of patient care are obvious.

Reducing healthcare errors has been another ongoing challenge in the United States. Healthcare providers strive to meet the standard of care and avoid harm to patients. Patients have a right to receive appropriate care, but that does not always happen. Ten years ago, the Institute of Medicine’s Committee on the Quality of Health Care in America undertook a comprehensive literature review and summarized the results of more than 40 studies about healthcare errors in its seminal report, To Err Is Human: Building a Safer Health System (Institute of Medicine, 2000). That report concluded that approximately 44,000–98,000 people in the United States die each year as a result of healthcare errors. Many thousands more who do not die are seriously injured from such errors. In addition to the human pain and suffering associated with healthcare errors, the monetary costs of these errors are substantial. Although some progress in reducing healthcare errors has been made since the release of To Err Is Human, substantial work remains to be done. It is anticipated that a nationwide HIT infrastructure will contribute to a reduction in healthcare errors by providing mechanisms to assist with the prevention of errors and to provide timely warnings of the possibility of a repetitive error that may affect many patients.

Containing and reducing healthcare costs in the United States, where more than $2 trillion is spent on health care each year (Keehan, Sisko, & Truffler, 2008), is another daunting challenge. Using EHR technology and a nationwide HIT infrastructure to improve quality and reduce errors within the healthcare delivery system is one way to address this challenge. Imagine the billions of dollars that could be saved just by reducing the estimated 1.7 million cases of healthcare-associated infections contracted by patients in U.S. hospitals each year (AHRQ, 2009, p. 108).

Promoting prevention, early detection, and management of chronic diseases is another purpose of the HITECH Act. The delivery of health care in the United States traditionally has been based on a disease model rather than a wellness model. Having an EHR for each individual could help with the necessary transition as providers and their patients become more aware of the variables that positively or negatively impact health. The ability to identify appropriate choices to promote wellness and either prevent illness and injury or detect and manage chronic diseases sooner will be enhanced.

Chronic diseases are of major concern to this country, not only because of the impact they have on individuals, but also because of the tremendous cost associated with providing treatment for patients with these conditions. Adult-onset diabetes, for example, has reached epidemic proportions. A national HIT infrastructure will help providers better identify those patients who are at risk for developing this disease and provide treatment strategies to avoid it. For those patients who develop type 2 diabetes, their providers will be able to diagnose the condition much sooner and manage it more effectively because of the vast resources that a national HIT infrastructure can provide.

Improving public health is another purpose of the HITECH Act. The recent H1N1 flu pandemic is illustrative of how a national HIT infrastructure can protect public health by fostering early detection and rapid response to infectious diseases, bioterrorism, and other situations that could have a widespread impact on the health status of many individuals and groups.

The impact that a national HIT infrastructure will have on clinical research is self-evident. Once the infrastructure becomes

operational, the amount of data that will become readily available for clinical research will increase exponentially compared to what is available today. The ability of researchers to conduct studies and provide clinicians with the most current evidence-based practice will be of tremendous benefit to patients everywhere.

Reducing health disparities is another purpose of the HITECH Act. According to the AHRQ (2010), “Health care disparities are differences or gaps in the care experienced by one population compared with another population” (p. 1). Detailed information about healthcare disparities can be found at the website for the Office of Minority Health and Health Disparities at www.cdc.gov/omhd. The AHRQ routinely examines the issue of disparities in health care and reports its findings to the public. Its current report, the National Healthcare Disparities Report of 2012, confirms that some Americans continue to receive inferior care because of such factors as race, ethnicity, and socioeconomic status (AHRQ, 2013). This report found disparities in the following areas:

Across all dimensions of healthcare quality: effectiveness, patient safety, timeliness, and patient centeredness. Across all dimensions of access to care: facilitators and barriers to care and health care utilization. Across many levels and types of care: preventive care, treatment of acute conditions, and management of chronic diseases. Across many clinical conditions: cancer, diabetes, end-stage renal disease, heart disease, HIV disease, mental health and

substance abuse, and respiratory diseases. Across many care settings: primary care, home health care, hospice care, emergency department, hospitals, and nursing homes. Within many subpopulations: women, children, older adults, residents of rural areas, and individuals with disabilities and other

special healthcare needs (AHRQ, 2013, pp. H1–H4).

All patients, regardless of race, ethnicity, or socioeconomic status, should receive care that is effective, safe, and timely. When the national HIT infrastructure contemplated by the HITECH Act is fully implemented, such disparities are bound to decrease. The ability to monitor for disparities and promote the delivery of appropriate care to all patients will be enhanced. Clinicians will be prompted to base their treatments on appropriate factors and avoid biased care.

Perhaps the most important task facing the national coordinator during the development and implementation of a nationwide HIT infrastructure is ensuring the security of the patient health information within that system. The ability to secure and protect confidential patient information has always been of paramount importance to clinicians, who view this consideration as an ethical and legal obligation of practice. Patients value their privacy and they have a right to expect that their confidential health information will be properly safeguarded. Nurses have been complying with the regulatory requirements of HIPAA for years, and the HITECH Act has enhanced the security and privacy protections each patient has a right to expect under HIPAA (Box 9-1). The specific changes are discussed in greater detail later in this chapter.

BOX 9-1 OVERVIEW OF THE HEALTH INSURANCE PORTABILITY AND ACCOUNTABILITY ACT OF 1996 Dee McGonigle, Kathleen Mastrian, and Nedra Farcus HIPAA was signed into law by President Bill Clinton in 1996. Hellerstein (1999) summarized the intent of the act as follows: to curtail healthcare fraud and abuse, enforce standards for health information, guarantee the security and privacy of health information, and ensure health insurance portability for employed persons. Consequences were put into place for institutions and individuals who violate the requirements of this act. For this text, we concentrate on the health information security and privacy aspects of HIPAA, which are outlined as follows:

The privacy provisions of the federal law, the Health Insurance Portability and Accountability Act of 1996 (HIPAA), apply to health information created or maintained by healthcare providers who engage in certain electronic transactions, health plans, and healthcare clearinghouses. The Department of Health and Human Services (HHS) has issued the regulation, “Standards for Privacy of Individually Identifiable Health Information,” applicable to entities covered by HIPAA. The Office for Civil Rights (OCR) is the Departmental component responsible for implementing and enforcing the privacy regulation. (See the Statement of Delegation of Authority to the Office for Civil Rights, as published in the Federal Register on December 28, 2000. (U.S. Department of Health and Human Services, 2006, para. 1)

The need and means to guarantee the security and privacy of health information have been the focus of numerous debates. Comprehensive standards for the implementation of this portion of the act eventually were finalized, but the process to adopt final standards took years. In August 1998, the U.S. Department of Health and Human Services released a set of proposed rules addressing health information management. Proposed rules specific to health information privacy and security were released in November 1999. The purpose of the proposed rules was to balance patients’ rights to privacy and providers’ needs for access to information (Hellerstein, 2000).

One of the biggest stumbling blocks to implementation of comprehensive standards for privacy was the associated cost. The administrative simplification portion of HIPAA calling for standardized forms for claims, medical records, laboratory reports, insurance forms, and so forth was expected to save as much as $250 billion after the initial conversion costs. HHS projected that compliance with the proposed security and privacy rules would cost $6.7 billion. Not everyone agreed with the HHS estimate, however; a study conducted by Blue Cross/Blue Shield and reported by Egger (2000) suggested that the costs would be closer to $43 billion over 5 years. An overview of the proposed standards helps to illustrate why implementation was estimated to be so costly.

Hellerstein (2000) summarized the proposed privacy rules. The rules do the following:

Define protected health information as “information relating to one’s physical or mental health, the provision of one’s health care, or the payment for that health care, that has been maintained or transmitted electronically and that can be reasonably identified with the individual it applies to” (Hellerstein, 2000, p. 2).

Propose that authorization by patients for release of information is not necessary when the release of information is directly related to treatment and payment for treatment. Specific patient authorization is not required for research, medical or police emergencies, legal proceedings, and collection of data for public health concerns. All other releases of health information require a specific form for each release and only information pertinent to the issue at hand is allowed to be released. All releases of information must be formally documented and accessible to the patient on request.

Establish patient ownership of the healthcare record and allow for patient-initiated corrections and amendments. Mandate administrative requirements for the protection of healthcare information. All healthcare organizations are required to have a privacy

official and an office to receive privacy violation complaints. A specific training program for employees that includes a certification of completion and a signed statement by all employees that they will uphold privacy procedures must be developed and implemented. All employees must re- sign the agreement to uphold privacy every 3 years. Sanctions for violations of policy must be clearly defined and applied.

Mandate that all outside entities that conduct business with healthcare organizations (e.g., attorneys, consultants, auditors) must meet the same standards as the organization for information protection and security.

Allow protected health information to be released without authorization for research studies. Patients may not access their information in blinded research studies because this access may affect the reliability of the study outcomes.

Propose that protected health information may be deidentified before release in such a manner that the identity of the patient is protected. The healthcare organization may code the deidentification so that the information can be reidentified once it has been returned.

Apply only to health information maintained or transmitted by electronic means.

As concerns mounted and deadlines loomed, the healthcare arena prepared to comply with the requirements of the law. The administrative simplification portion of this law is intended to decrease the financial and administrative burdens by standardizing the electronic transmission of certain administrative and financial transactions. This section also addresses the security and privacy of healthcare data and information for the covered entities of healthcare providers who transmit any health information in electronic form in connection with a covered transaction, health plans, and healthcare clearinghouses. For more information, visit http://aspe.hhs.gov/ (U.S. Department of Health and Human Services, 2007).

The privacy requirements, which went into effect on April 14, 2003, limit the release of protected health information without the patient’s knowledge and consent. Covered entities must comply with the requirements. Notably, they must dedicate a privacy officer, adopt and implement privacy procedures, educate their personnel, and secure their electronic patient records. Most individuals are familiar with the need to notify patients of their privacy rights, having signed forms on interacting with healthcare providers.

According to the HHS (2002), the privacy rule provides certain rights to patients: the right to request restrictions to access of the health record; the right to request an alternative method of communication with a provider; the right to receive a paper copy of the notice of privacy practices; the right to file a complaint if the patient believes his or her privacy rights were violated; the right to inspect and copy one’s health record; the right to request an amendment to the health record; and the right to see an account of disclosures of one’s health record. This places the burden of maintaining privacy and accuracy on the healthcare system, rather than the patient.

On October 16, 2003, the electronic transaction and code set standards became effective. They do not require electronic transmission, but rather mandate that if transactions are conducted electronically, they must comply with the required federal standards for electronically filed healthcare claims. “The Secretary has made the Centers for Medicare & Medicaid Services (CMS) responsible for enforcing the electronic transactions and code sets provisions of the law” (“Guidance on Compliance with HIPAA Transactions and Code Sets,” 2003, para. 3).

The security requirements went into effect on April 21, 2005, and require the covered entities to put safeguards that protect the confidentiality, integrity, and availability of protected health information when stored and transmitted electronically into place. According to Savage (2006), “After HIPAA’s security requirements went into effect, many organizations in the healthcare industry continue to improve their security” (para. 5). Some organizations have needed to shell out enormous amounts of resource expenditures involving people, time, and money. Savage states that “While the HIPAA rules have driven security improvements, their lack of specifics and a dearth of enforcement leaves much room for interpretation, making compliance hard to gauge” (para. 5).

Because it is believed that the pursuit of a perfect security system is a wild goose chase, this journey should be reflective of the needs of each organization and its unique requirements in relation to its obligations to the patients it serves while addressing the demands of HIPAA. The safeguards that are addressed are administrative, physical, and technical. The administrative safeguards refer to the documented formal policies and procedures that are used to manage and execute the security measures. They govern the protection of healthcare data and information and the conduct of the personnel. The physical safeguards refer to the policies and procedures that must be in place to limit physical access to electronic information systems. Technical safeguards are the policies and procedures used to control access to healthcare data and information. Safeguards need to be in place to control access whether the data and information are at rest, residing on a machine or storage medium, being processed, or in transmission, such as being backed up to storage or disseminated across a network.

How a National HIT Infrastructure Is Being Developed Developing a national HIT infrastructure is an enormous and extremely complex undertaking that requires extensive financial, technologic, and human resources. The HITECH Act established the ONC, as noted earlier, and HHS appointed a national coordinator, who is responsible for the development of the infrastructure. The HITECH Act also established two committees within the ONC: the HIT Policy Committee and the HIT Standards Committee.

The Policy Committee is responsible for making recommendations to the coordinator about how to implement the requirements of the HITECH Act, such as the technologies to use in the infrastructure. The Policy Committee has a total of 20 members, one of whom must be a member from a labor organization and two of whom must be healthcare providers. At least one of the healthcare providers must be a physician. There is no specific requirement that a nurse be on the Policy Committee. A complete list of the Policy Committee members is available at www.healthit.hhs.gov.

The Standards Committee is responsible for recommending standards by which health information is to be electronically exchanged. The HITECH Act does not designate the number of members to be on the committee; however, its members include healthcare providers, ancillary healthcare workers, consumers of health care, and others. Again, there is no specific requirement that a nurse be on the Standards Committee, and a complete list of the Standards Committee members is available at www.healthit.hhs.gov.

The HITECH Act also made provisions to include meaningful public input in the development of a national HIT infrastructure. Both the Policy Committee and the Standards Committee hold public meetings, and anyone interested in this process can participate. A schedule of meetings, committee agendas, and the transcripts of past meeting are posted at www.healthit.hhs.gov.

The national coordinator has several duties. He or she decides whether to endorse the recommendations of the Policy and Standards Committees and acts as a liaison among the committees and various federal agencies involved in the process of developing a national HIT infrastructure. He or she consults with these other agencies, including the National Institute of Standards and Technology, and along with those agencies updates the Federal HIT Strategic Plan (U.S. Department of Commerce, 2011). The Federal HIT Strategic Plan was published in June 2008, before enactment of the HITECH Act, and can be accessed at http://www.hrsa.gov/healthit/toolbox/ruralhealthittoolbox/gettingstarted/strategicplan.html.

The HITECH Act also provides significant monetary incentives for providers who engage in meaningful use of health information technology. “Meaningful use” is defined as “using electronic health records (EHRs) in a meaningful manner, which includes, but is not limited to electronically capturing health information in a coded format, using that information to track key clinical conditions, communicating that information to help coordinate care, and initiating the reporting of clinical quality measures and public health information” (CMS, 2010, para. 3).

Monetary incentives are available to clinicians and facilities that implement EHR systems that meet the specific standards.

Providers that fail to adopt such systems within a specified time frame may be subject to significant governmental penalties.

How the HITECH Act Changed HIPAA HIPAA Privacy and Security Rules Nurses have been complying with HIPAA for years. HIPAA was enacted by the federal government for several purposes, including better portability of health insurance as a worker moved from one job to another; deterrence of fraud, abuse, and waste within the healthcare delivery system; and simplification of the administrative functions associated with the delivery of health care, such as reimbursement claims sent to Medicare and Medicaid. Simplification of administrative functions entailed the adoption of electronic transactions that included sensitive healthcare information. To protect the privacy and security of health information, two sets of federal regulations were implemented. The Privacy Rule became effective in 2003, and the Security Rule became effective in 2005. Many practitioners that refer to HIPAA are not referring to the comprehensive federal statute enacted in 1996, but rather to the Privacy Rule and the Security Rule—that is, the federal regulations that were adopted years after HIPAA became law.

Under the Privacy Rule, patients have a right to expect privacy protections that limit the use and disclosure of their health information. Under the Security Rule, providers are obligated to safeguard their patients’ health information from improper use or disclosure, maintain the integrity of the information, and ensure its availability. Both rules apply to protected health information (PHI), defined as any physical or mental health information created, received, or stored by a “covered entity” that can be used to identify an individual patient, regardless of the form of the health information (i.e., it can be electronic, handwritten, or verbal) (V|lex, 2011). Covered entities include hospitals and other healthcare providers that transmit any health information electronically, as well as health insurance companies and healthcare clearinghouses (V|lex, 2011).

Clinicians have become very knowledgeable about the requirements of the Privacy and Security Rules. They are familiar with their obligations to protect patient information and the rights afforded to their patients under these regulations. Patients are entitled to a notice of privacy practices from their healthcare provider. Inpatients are entitled to opt out of the facility’s directory, thereby protecting disclosure of information that they are even a patient in the facility. Under certain circumstances, patients must authorize disclosure of their PHI before it can be released by the provider. Patients can request and obtain access to their own healthcare records and may request that corrections and additions be made to their records. Providers must consider a patient’s request to amend a healthcare record, but they are not required to make such an amendment if the request is unwarranted. Unauthorized access or use or any loss of healthcare information must be disclosed to any patient affected by the breach. Patients may request an accounting of anyone who accessed their healthcare information, and the provider is required to provide that information in a timely manner. Finally, patients have a right to complain if they perceive that the privacy or security of their healthcare information has been compromised in some way. Such complaints can be made directly to the provider or to the Office of Civil Rights (OCR).

The OCR, which is part of HHS, is responsible for enforcing HIPAA. It provides significant information and guidance to clinicians who must comply with the Privacy and Security Rules. It has been tracking complaints and investigating violations since 2003. Guidance and information about the complaint process and the violations that the OCR has handled are available on its website at http://www.hhs.gov/ocr/privacy/hipaa/modelnotices.html. As an example, one such violation involved a nurse practitioner who had privileges within a healthcare system. She accessed her ex-husband’s medical records without his authorization by using the system- wide EHRs. A complaint was filed and the OCR investigated the matter. The OCR resolved the complaint with the healthcare system. As part of this resolution, the healthcare system curtailed the nurse practitioner’s access to its EHRs and it required her to undergo remedial training. In addition, it reported the nurse practitioner to her professional board (U.S. Department of Health and Human Services, Office of Civil Rights, n.d.)

Many businesses are moving to enact a “bring your own device” (BYOD) policy for employees. This policy, which helps to streamline the lives of employees by maintaining personal and business information on one device, can also result in cost savings for the organization overall. BYOD is an issue, however, when dealing with PHI. Healthcare organizations typically do not encourage use of personal devices for professional matters, and in many instances they actually have policies in place forbidding employees from using personal devices in the workplace. According to HIT Consultant (2013), approximately 50% of healthcare organizations report that personal mobile devices can be used to access the Internet within their facilities but these devices are not given access to the organization’s network. Typically, only devices that are issued by the organization, secured, and routinely audited are able to access to the network. Nurses must exercise caution when bringing their personal devices into the healthcare organization to ensure that they are not violating any specifics of the BYOD policy.

Compliance with the Privacy and Security Rules is mandatory for all covered entities, and the HITECH Act extends compliance with these requirements directly to other entities that are business associates of a covered entity. Requirements include designation of privacy and information security officials to protect health information and appropriate handling of any complaints. Sanctions must be imposed if a violation of HIPAA occurs. The Privacy and Security Rules also mandate that certain physical and technical safeguards be implemented for PHI, and they require entities to conduct periodic training of all staff to ensure compliance with these safeguards. Most entities adhere to industry standards and provide their personnel with yearly training. In addition, entities are to conduct regular audits to ensure compliance, and any breaches in the privacy or security of PHI must be remedied immediately. It is important to avoid a security incident, defined as “the attempted or successful unauthorized access, use, disclosure, modification, or destruction of information or interference with system operations in an information system” (U.S. Department of Human Services, CMS, 2008, p. 1). Such incidents trigger certain notification requirements.

The HITECH Act Enhanced HIPAA Protections The HITECH Act has had a significant impact on HIPAA’s Privacy and Security Rules in the following ways:

HHS is to provide annual guidance about how to secure health information.

Notification requirements in the event of a breach in the security of health information have been enhanced. HIPAA requirements now apply directly to any business associates of a covered entity. The rules that pertain to providing an accounting to patients who want to know who accessed their health information have

changed. Enforcement of HIPAA has been strengthened.

These measures are being implemented to provide further assurance that health information will be protected as the country transitions to a nationwide HIT infrastructure (Box 9-2).

BOX 9-2 OTHER ORGANIZATIONS ASSISTING HIPAA Dee McGonigle, Kathleen Mastrian, and Nedra Farcus Several other organizations have been involved in HIPAA implementation. The American National Standards Institute (ANSI) X12N and Health Level 7 (HL7) standards organizations worked together to develop an electronic standard for claims attachments to recommend to HHS (Spencer & Bushman, 2006, para. 2). The American National Standards Institute (ANSI, n.d.) was founded in 1918 and has served as the coordinator of the U.S. voluntary standards and conformity assessment system (para. 1). ANSI provides a forum where the private and public sectors can cooperatively work together toward the development of voluntary national consensus standards and the related compliance programs (para. 2). HL7 (n.d.) is one of several American National Standards Institute–accredited standards-developing organizations (SDOs) operating in the healthcare arena (para. 1). It states that its mission is to provide standards for interoperability that improve care delivery, optimize workflow, reduce ambiguity, and enhance knowledge transfer among all stakeholders, including healthcare providers, government agencies, the vendor community, fellow SDOs, and patients (para. 5).

HL7 was initially associated with HIPAA in 1996 through the creation of a claims attachments special interest group charged with standardizing the supplemental information needed to support healthcare insurance and other e-commerce transactions. The initial deliverable of this group was six claim attachments. This special interest group is currently known as the Attachment Special Interest Group. As the attachment projects continue, they are slated to include skilled nursing facilities, home health care, preauthorization, and referrals.

The “Level Seven” in HL7’s name refers to the highest level of the International Standards Organization’s (ISO) communications model for Open Systems Interconnection (OSI) application level. The application level addresses definition of the data to be exchanged, the timing of the interchange, and the communication of certain errors to the application. The seventh level supports such functions as security checks, participant identification, availability checks, exchange mechanism negotiations and, most importantly, data exchange structuring. (HL7, n.d., para. 5)

The OSI was an attempt to standardize networking by the ISO. HL7 addresses the distinct requirements of the systems in use in hospitals and other facilities, is more concerned with application than the other levels, and considers user authentication and privacy (Webopedia, 2008). The lower levels of OSI address hardware, software, and data reformatting.

HL7’s mission is supported through two separate groups, the Extensible Markup Language (XML) special interest group and the structured documents technical committee. The XML special interest group makes recommendations on use of XML standards for all of HL7’s platform- and vendor-independent healthcare specifications (HL7, n.d., para. 21). XML began as a simplified subset of the standard generalized markup language; its major purpose is to facilitate the exchange of structured data across different information systems, especially via the Internet. It is considered an extensible language because it permits users to define their own elements, thereby supporting customization to enable purpose-specific development. The structured documents technical committee supports the HL7 mission through development of structured document standards for health care (para. 21). HL7 also organizes, maintains, and sustains a repository for the vocabulary terms used in its messages to provide a shared, well-defined, and unambiguous knowledge base of the meaning of the data transferred.

ISO (2008a) is a network of the national standards institutes of 157 countries. It includes one member per country, and a central secretariat in Geneva, Switzerland, coordinates the system (para. 1). ISO is a nongovernmental organization; its members are not delegations of national governments (unlike the case in the United Nations system). Nevertheless, ISO occupies a special position between the public and private sectors. On the one hand, many of its member institutes are part of the governmental structure of their countries or are mandated by their government. On the other hand, other members have their roots uniquely in the private sector, having been set up by national partnerships of industry associations (ISO, 2008a, para. 2).

This placement enables ISO to become a bridging organization where members can reach agreement on solutions that meet both the requirements of business and the broader needs of society, consumers, and users. These international agreements become standards that use the prefix ISO followed by the number of the standard. An example is the health informatics, health cards, numbering system, and registration procedure for issuer identifiers, ISO 20302:2006; it is designed to confirm, via a numbering system and registration procedure, the identities of both the healthcare application provider and the health card holder so that information may be exchanged by using cards issued for healthcare service (ISO, 2008b, para. 12). ISO provides standards for interoperability that improve care delivery, optimize workflow, reduce ambiguity, and enhance knowledge transfer among all of its stakeholders, including healthcare providers, government agencies, the vendor community, fellow SDOs, and patients. The standards are used on a voluntary basis because ISO has no power to force their enactment.

All of the organizations described here have guidelines, standards, and rules to help healthcare entities collect, store, manipulate, dispose of, and exchange secure PHI. Many SDOs work to help develop standards. HIPAA guarantees the security and privacy of health information and curtails healthcare fraud and abuse while enforcing standards for health information.

UNITED STATES AND BEYOND Health care was not the only focus of U.S. legislative acts. One often sees “GLBA” and “SOX” when searching for information on HIPAA. The Gramm-Leach-Bliley Act (GLBA) is federal legislation in the United States to control how financial institutions handle the private information they collect from individuals. The Sarbanes-Oxley (SOX) Act is legislation put in place to protect shareholders and the public from deceptive accounting practices in organizations.

Privacy and data regulations are also being established around the world, such as the Data Protection Act 1998 in the United Kingdom (Ministry of Justice, 2008); the Dutch Data Protection Authority (2007), which released privacy legislation guidelines on publishing personal data on the Internet; and Finland’s Personal Data File Act 1988 and Personal Data Act 1999 (Data Protection Board, n.d.). New Zealand’s Health Information Privacy Code 1994 had amendment number six come into effect in November 2007; this amendment defined entities such as the ethics committee; hospital, health, or disability services; health professional body; registered health professional; and health practitioner (Privacy Commissioner, 2007). Argentina’s Privacy and Data Protection (2007) states that it is the first Latin American country to be awarded the status of “adequate country” from the point of view of European Data Protection authorities—a breakthrough that is expected to encourage other countries in the region to work toward improving data protection rights for individuals (Privacy and Data Protection, 2007, para. 7). In Canada, the Personal Information Protection and Electronic Documents Act received royal assent in 2000 (Office of the Privacy Commissioner of Canada, 2004). Safe Harbor deals with the transfer of personal data from the European Union to the United States; the regulations in Article 25 and 26 of the European Data Protection Directive serve as the basis for governing this transfer. According to these regulations, transferring data to third countries is in principle possible only if these countries guarantee an adequate level of protection as required by the directive (Federal Commissioner for Data Protection and Freedom of

Information, n.d., para. 1). It is quite evident that privacy and security have become global concerns.

Avoiding security incidents has become a paramount concern for healthcare organizations and providers. Providers must protect their information and prevent unauthorized persons from accessing, using, disclosing, changing, or destroying a patient’s health information, or otherwise interfering with the operations of a health information system, such as an EHR. To facilitate a provider’s ability to do this, the HITECH Act requires HHS to provide annual guidance to secure health information. PHI can be secured or unsecured. PHI is considered unsecured if the provider does not follow the guidance provided by HHS for implementing technologies and methodologies that make PHI “unusable, unreadable, or indecipherable to unauthorized individuals” (U.S. Department of Health and Human Services, 2009). PHI can be secured through encryption, shredding and other forms of complete destruction, or electronic media sanitation.

The distinction between secured and unsecured PHI is important because providers that experience a breach in the privacy or security of their PHI must adhere to certain notification requirements depending on the type of PHI affected by the breach. The HITECH Act enhanced the breach notification requirements of HIPAA. If the PHI is unsecured, the provider must take certain steps to notify those individuals who have been affected. Providers can avoid these onerous breach notification requirements if the PHI is secured in accordance with the specifications of HHS.

A breach is considered discovered as soon as an employee other than the individual who committed the breach knows or should have known of the breach, such as unauthorized access or even an unsuccessful attempt to access information. For example, if a nurse knows that a colleague has accessed or attempted to access the record of a patient for whom the colleague is not providing care (e.g., the nurse practitioner who accessed her ex-husband’s EHR, as discussed previously), the nurse’s employer is deemed to have discovered the breach as soon as the nurse learned of it. The discovery of a breach triggers the beginning of the time frame during which the provider must fulfill the notification requirements. A provider must fulfill these requirements within a reasonable period of time; under no circumstances may a provider take more than 60 days from discovery of the breach. It is easy to understand why providers require their employees to report knowledge of such breaches immediately to the privacy or information security officer. A provider’s failure to adhere to the breach notification requirements could result in OCR sanctions, including monetary penalties.

Whenever a breach involves unsecured PHI, covered entities are responsible for alerting each individual affect by mail, or by e- mail if preferred by the individual. If there is insufficient contact information for 10 or more patients, the provider is required to place conspicuous postings on the home page of its website or in major print or broadcast media (without identifying patients). A toll-free telephone number must be provided so that affected individuals can call for information about the breach. For breaches involving unsecured PHI of more than 500 individuals, a prominent media outlet must also be notified. Notice must be given to HHS as well, and HHS will post the information on its public website (U.S. Department of Health and Human Services, 2009). It is easy to see why providers would want to avoid these requirements by making sure their PHI is secured. Having to post such notices undermines the trust that exists between healthcare providers and the patients and communities they serve.

The HITECH Act has improved the privacy and security of patient health information by applying the requirements of HIPAA directly to the business associates of covered entities. In the past, it was up to the covered entity to enter into contracts with its business associates to ensure compliance with HIPAA. Now business associates are responsible for their own compliance. An example of such a business associate is a HIT company hired by a hospital to implement or upgrade an EHR system. The technology company has access to the hospital’s EHR system and must comply with the HIPAA Privacy and Security Rules, just as covered entities must comply with these rules. This includes being subject to enforcement by the OCR for any violations.

Existing accounting rules are enhanced under the HITECH Act, giving patients the right to access their EHR and receive an accounting of all disclosures. Before the HITECH Act, HIPAA regulations provided an exception to the accounting requirements. Providers and other covered entities were not required to include in the accounting any disclosures that were made to facilitate the treatment of patients, the payment for services, or the operations of the entity—a provision commonly known as the “TPO exception.” This exception ended in January 2011 for providers that recently implemented new EHR systems. For those providers with EHR systems that were implemented before the HITECH Act, the TPO exception ends in January 2014. It is easy to understand why this exception is ending. As all providers implement comprehensive EHR systems, it will be very easy to generate an electronic record with an accounting of anyone who accessed a patient’s record.

Finally, the HITECH Act strengthens the enforcement of HIPAA. HHS can conduct audits, which will be even easier to accomplish once a nationwide HIT infrastructure is in place. In addition, stiffer civil monetary penalties (CMP) for violations of HIPAA became effective as soon as the HITECH Act became law in February 2009. CMPs are divided into three tiers. A Tier 1 CMP, in which the covered entity had no reason to know of a violation, is $100 per incident, up to a cap of $25,000 per year. A Tier 2 CMP, in which the covered entity had reasonable cause to know of a violation, is $1,000 per incident, up to a cap of $100,000 per year. A Tier 3 CMP, in which the covered entity engaged in willful neglect that resulted in a breach, is $10,000 per incident, up to a cap of $250,000 per year. In addition, the HITECH Act gives authority to impose an additional CMP of $50,000 to $1.5 million if the covered entity does not properly correct a violation. Criminal penalties also can be imposed when warranted. It is imperative that providers avoid these penalties.

Before enactment of the HITECH Act, the federal government alone enforced HIPAA. Now, state attorneys general can play a significant role in the enforcement and prosecution of HIPAA violations. Once the HITECH Act became law, state attorneys general were authorized to pursue civil claims for HIPAA violations and collect up to $25,000 plus attorneys’ fees. As of 2012, individuals who are damaged by such violations became eligible to share in any monetary awards obtained by these state officials.

Implications for Nursing Practice Being Involved and Staying Informed The development and implementation of a nationwide EHR system holds great promise for nursing practice and nursing informatics. The profession of nursing will benefit from the many enhancements such an infrastructure has to offer, including the ability to improve

the delivery of nursing care and the quality of that care, the ability to make more efficient and timely nursing care decisions for patients, the ability to avoid errors that may harm patients, and the ability to promote health and wellness for the patients whom nurses serve. On a broader scale, nurse researchers will have the ability to more readily access data that can be used to continue to foster evidence-based practice. The possibilities seem endless. For those who devote their professional careers to nursing informatics or plan to do so, the opportunities abound. Much work remains to be done as this country transitions to a nationwide HIT infrastructure, however, and there are monetary incentives available from the government for adopting systems that comply with the meaningful use requirement.

All nurses need to be engaged in this process, whether they treat patients, are managers within healthcare organizations, teach, develop computer programs, or help create institutional or governmental policies. Nurses, as the end users of developing technologies, cannot afford to be left behind in these exciting times. Their voices must be heard, whether it is within the facility where they work as changes to the EHR system are contemplated, or whether it is in the public policy arena. How often are nurses the last to know that a new EHR system has been adopted by their hospital? How many times have nurses been trained to use a system that would have benefited from their input before it was implemented or even purchased? Nurses often are not invited to the table when entities make decisions about informatics, so they should not be afraid to ask to be included, whether it is to be heard within the workplace or within the governmental agencies that are overseeing the changes that are taking place.

Even nurses who do not get involved in this process need to stay current with the rapid changes that are taking place. Information about federal initiatives is available from the ONC and the OCR. Both offices are housed within HHS and are excellent resources for additional information about the HITECH Act and HIPAA. Regulations to implement the HITECH Act and enhance the HIPAA protections required by it are being proposed and adopted at a rapid pace. The ONC can be accessed at http://www.ahrq.gov/healthcare-information/topics/topic-hit.html. The OCR can be accessed at http://www.hhs.gov/ocr/privacy/hipaa/modelnotices.html. State resources also are available.

Protecting Yourself

Nurses who strive to protect the privacy and security of patient information are protecting themselves from ethical lapses and violations of law. The American Nurses Association’s Code of Ethics for Nurses with Interpretive Statements mandates that nurses protect a patient’s rights to privacy and confidentiality.

Associated with the right to privacy, the nurse has a duty to maintain confidentiality of all patient information. Nurses who engage with social media need to be especially cognizant of the potential for breaching the confidentiality of patient information. Box 9-3 provides more information related to nurses’ use of social media. The patient’s well-being could be jeopardized and the fundamental trust between patient and nurse destroyed by unnecessary access to data or by the inappropriate disclosure of identifiable patient information. The rights, well-being, and safety of the individual patient should be the primary factors in arriving at any professional judgment concerning the disposition of confidential information received from or about the patient, whether oral, written, or electronic. The standard of nursing practice and the nurse’s responsibility to provide quality care require that relevant data be shared with only those members of the healthcare team who have a need to know that information. Only information pertinent to a patient’s treatment and welfare should be disclosed, and only to those directly involved with the patient’s care. When using electronic communications, special effort should be made to maintain data security (American Nurses Association, 2010, p. 6).

BOX 9-3 USE OF SOCIAL NETWORKS BY NURSES Glenn Johnson and Jeff Swain New opportunities to share information via social networks have grabbed the headlines. Since their inception in 2004, the growth in popularity of social networking tools, such as Facebook (http://www.facebook.com) and Twitter (http://twitter.com), has been phenomenal. What makes these sites so attractive? Web-based applications, such as Facebook, allow users to connect and share information in ways that were not previously possible. Users develop online profiles that contain information they select to share with others. Using simple online utilities, users can easily connect and share their profiles, communicating with friends over the Internet. Virtual groups of users with similar profiles may be created, connecting users with others who have similar interests. Twitter, a micro-blogging platform, allows users to create interpersonal networks for socializing, support, and information sharing. The power of such tools as Twitter lies in their being lightweight, their limiting of updates to 140 or fewer characters, and their convenience—users can update their status from any device that has an Internet connection or text messaging capabilities.

The popularity of social and mobile networking applications is one indication of how new Web-based technologies are changing communication preferences. The Web is no longer a destination place, but instead has become a vehicle of communication where individuals use application software (“apps”), which are installed or downloaded, to connect with others. Individuals act as their own portal and can connect from anywhere with their various communities. This makes it difficult to separate out various communities and social networks. Where once it was relatively easy to separate work relationships from friends and family, networked communities tend to overlap, blurring the boundaries between them. The phenomenon of overlapping networks means that the unintended audience is almost always greater than the intended one. A status update that may be construed as harmless and funny to one’s friends could be taken an entirely different way by family or colleagues. This is not to say networked communities are harmful or bad. Indeed, the benefits of such communities far exceed their negatives. However, the immediacy and the permanence of the updates shared mean that the user must think about the impact beyond the intended audience in ways never before required (Johnson & Swain, 2011).

Nurses and other healthcare workers who use social media must be aware that the overlapping of networks may unintentionally create privacy and confidentiality breaches. Even when patients are not identified by name, general sharing of information or venting about a difficult day may constitute a privacy breach. The National Council of State Boards of Nursing (NCSBN, 2011) has collaborated with the ANA to develop specific guidelines for the use of social media by nurses. See https://www.ncsbn.org/Social_Media.pdf to read a white paper discussing common misconceptions about social media, consequences for breaching confidentiality using social media, guidelines for appropriate use of social media, and case scenarios with discussion.

REFERENCES Johnson, G., & Swain, J. (2011). Professional development and collaboration tools. In McGonigle, D. & Mastrian, K. eds. Nursing informatics and the foundation of knowledge (2nd ed., pp. 185–195).

National Council of State Boards of Nursing. (2011). White paper: a nurses guide to the use of social media. www.ncsbn.org

The similarities between these ethical obligations and the legal requirements of HIPAA and other federal and state privacy and confidentiality laws are readily apparent to nurses. By complying with their ethical code, nurses were complying with the Privacy and Security Rules before they were required to do so. Since the adoption of the HIPAA Privacy and Security Rules, and now with the passage of the HITECH Act, it is more important than ever for nurses to understand their obligations in this area and avoid the pitfalls of violations.

In addition to the sanctions imposed by the OCR, violations can lead to disciplinary actions by employers and professional licensing boards, as well as litigation. Such actions can have a serious negative impact on the nurse’s reputation and financial wellbeing. If a nurse is terminated for invading a patient’s privacy or breaching the confidentiality of a patient’s information, some state laws require reporting the information to prospective employers of the nurse; other laws require reporting to the State Board of Nursing. State Boards of Nursing have the authority to publicly discipline a nurse who has engaged in professional misconduct by invading a patient’s privacy, which includes inappropriately accessing a patient’s EHR, and breaching confidentiality of patient information, such as allowing or tolerating unauthorized access to a patient’s EHR. These types of situations can also cause patients to file complaints with the OCR and lawsuits against the offenders. Nurses must be ever mindful of their obligations to report a breach in the privacy or security of PHI to their employers, even if it entails reporting a colleague.

Finally, some view the EHR as a convenient method for employers to monitor the performance of its nurses. Clearly, an EHR system provides a wealth of information that can be, and often is required to be, monitored. Audits are required to make sure that no breaches in the system’s security occur. Audits are not necessarily required to determine, for example, which nurses are failing to complete the hospital’s documentation requirements in a timely fashion, which nurses are improperly altering (attempting to correct) the record, or which nurses are dispensing more pain medication than the average. Nurses have been challenged by employers who allege failure to document, improper or false documentation, and suspected diversion of narcotics. These types of situations are unsettling and may be on the rise as more providers adopt or augment EHR systems. Thus it behooves every nurse who works with such a system to obtain proper training and to know the policies and procedures that pertain to its use.

Summary The HITECH Act and the HIPAA Privacy and Security Rules are intended to enhance the rights of individuals. These laws provide patients with greater access and control over their PHI. They can control its uses, dissemination, and disclosures. Covered entities must establish not only a required level of security for PHI, but also sanctions for employees who violate the organization’s privacy policies and administrative processes for responding to patient requests regarding their information. Therefore, they must be able to track the PHI, note access from the perspective of which information was accessed and by whom, and identify any disclosures. Finally, readers should recognize that there is global awareness of the need for privacy protections for personal information or PHI. Over the next few years, international efforts will accelerate, enhancing international data exchange.

THOUGHT-PROVOKING QUESTIONS

1. Why is it important to establish patient ownership of the healthcare record? 2. What are the potential negative consequences of the right of amendment and correction of healthcare records by patients? 3. One of the largest problems with healthcare information security has always been inappropriate use by authorized users. How do HIPAA and the

HITECH Act help to curb this problem? 4. How do you envision Health Level 7, HIPAA, and the HITECH Act evolving in the next decade? 5. Imagine that you are the designated privacy officer in a healthcare institution. Which types of monitoring procedures would you develop? 6. If you were the privacy officer, what would you include in your sanctions for violations policy? 7. As privacy officer, how would you address the following:

a. Tracking each point of access of the patient’s database, including who entered the data. b. Nurses in your hospital have an access code that gives them access to only their unit’s patients. A visitor accidently comes to the wrong unit

looking for a patient and asks the nurse to find out which unit the patient is on. c. Encouraging nurses to report privacy and security breaches.

References Agency for Healthcare Research and Quality (AHRQ). (2009). National healthcare quality report (NHQR) 2009: Crossing the quality chasm: A new

healthcare system for the 21st century. Washington, DC: National Academies Press. Agency for Healthcare Research and Quality (AHRQ). (2013). 2012 national healthcare disparities report.

http://www.ahrq.gov/research/findings/nhqrdr/nhdr12/nhdr12_prov.pdf American National Standards Institute (ANSI). (n.d.). ANSI: A historical overview. http://ansi.org/about_ansi/introduction/history.aspx?menuid=1 American Nurses Association. (2010). Code of ethics for nurses with interpretive statements.

http://www.nursingworld.org/MainMenuCategories/EthicsStandards/CodeofEthicsforNurses/Code-of-Ethics.aspx Ashish, J. (2009). Use of electronic health records in U.S. hospitals. New England Journal of Medicine, 360(16), 1628–1638. Centers for Medicare and Medicaid Services (CMS). (2010). Meaningful use. https://www.cms.gov/EHRIncentivePrograms/30_Meaningful_Use.asp Data Protection Board. (n.d.). Legislation for the protection of privacy. http://www.tietosuoja.fi/27305.htm Dutch Data Protection Authority (DPA). (2007, December). Dutch DPA publication of personal data on the Internet.

http://www.dutchdpa.nl/downloads_overig/en_20071108_richtsnoeren_internet.pdf?refer=true&theme=purple Egger, E. (2000). HIPAA offers hospitals the good, the bad, and the ugly. Health Care Strategic Management, 18(4), 1, 21–23. Federal Commissioner for Data Protection and Freedom of Information. (n.d.). Safe harbor.

http://www.bfdi.bund.de/cln_007/nn_671558/EN/EuropeanInternationalAffaires/Artikel/SafeHarbor.html Guidance on compliance with HIPAA transactions and code sets. (2003). http://www.cms.gov/Regulations-and-Guidance/HIPAA-Administrative-

Simplification/TransactionCodeSetsStands/index.html?redirect=/transactioncodesetsstands/02_transactionsandcodesetsregulations.asp Health Level Seven (HL7). (n.d.). What is HL7? http://www.hl7.org/ Hellerstein, D. (1999). HIPAA’s impact on healthcare. Health Management Technology. Retrieved January 2008 from

http://findarticles.com/p/articles/mi_m0DUD/is_3_20/ai_54396227 Hellerstein, D. (2000). HIPAA and health information privacy rules: Almost there. Health Management Technology. Retrieved January 2008 from

http://findarticles.com/p/articles/mi_m0DUD/is_4_21/ai_61523494 HIT Consultant. (2013). 3 Do’s and don’ts of effective HIPAA compliance for BYOD & mHealth. http://www.hitconsultant.net/2013/06/11/3-dos-and-

donts-of-effective-hipaacompliance-for-byod-mhealth/ Institute of Medicine. (2000). To err is human: Building a safer health system. Washington, DC: National Academies Press. International Standards Organization (ISO). (2008a). About ISO. http://www.iso.org/iso/about.htm International Standards Organization (ISO). (2008b). Health informatics. http://www.iso.org/iso/iso_catalogue/catalogue_tc/catalogue_detail.htm?

csnumber=35376 Keehan, S., Sisko, A.Q., & Truffler, C. (2008). Health spending projections through 2017: The baby-boom generation is coming to Medicare. Health

Affairs, 27(2), 145–155. Leyva, C., & Leyva, D. (2011). HITECH Act. http://www.hipaasurvivalguide.com/hitech-act-text.php Ministry of Justice. (2008). Data sharing and protection. http://www.justice.gov.uk/information-access-rights/data-protection Office of the Privacy Commissioner of Canada. (2004). Privacy legislation. http://www.privcom.gc.ca/legislation/02_06_07_e.asp Privacy and Data Protection. (2007). Data protection in Argentina. http://www.protecciondedatos.com.ar Privacy Commissioner. (2007). Health information privacy code. http://www.privacy.org.nz/health-information-privacy-code Readthestimulus.org. (2009). Committee print, January 16, 2009. http://govinfo.sla.org/2009/02/08/readthestimulusorg/ Savage, M. (2006). Security news: Perfect HIPAA security impossible, experts say.

http://searchsecurity.techtarget.com/originalContent/0,289142,sid14_gci1268986,00.html Spencer, J., & Bushman, M. (2006). HIPAAdvisory: The next HIPAA frontier: Claims attachments. Retrieved January 2008 from

http://www.hipaadvisory.com/action/tcs/nextfrontier.htm U.S. Department of Commerce. (2011). National Institute of Standards and Technology (NIST). http://www.nist.gov/index.html U.S. Department of Health and Human Services. (2002). Federal Register, Part V, Department of Health and Human Services: Standards for privacy of

individually identifiable health information; Final rule. http://www.hhs.gov/ocr/privacy/hipaa/administrative/privacyrule/privrulepd.pdf U.S. Department of Health and Human Services. (2006). Medical privacy: National standards to protect the privacy of personal health information.

http://www.ihs.gov/hipaa/ U.S. Department of Health and Human Services. (2007). Administrative simplification in the health care industry. Retrieved January 2008 from

http://aspe.os.dhhs.gov/admnsimp U.S. Department of Health and Human Services. (2009). Federal Register: 45 CFR Parts 160 and 164: Breach Notification for Unsecured Protected

Health Information; Interim. U.S. Department of Health and Human Services, CMS. (2008). CMS information security incident handling and breach analysis/notification process.

Retrieved January 2008 from https://www.cms.gov/informationsecurity/downloads/incident_handling_procedure.pdf U.S. Department of Health and Human Services, Office of Civil Rights. (n.d.). Health information privacy: All case examples.

http://www.hhs.gov/ocr/privacy/hipaa/enforcement/examples/allcases.html#case1 V|lex. (2011). 45 CFR 160.103—Definitions. http://cfr.vlex.com/vid/160-103-definitions-19933565 Webopedia. (2008). The 7 layers of the OSI model. http://www.webopedia.com/quick_ref/OSI_Layers.asp

Section III

Nursing Informatics Administrative Applications: Precare and Care Support

Chapter 10 Systems Development Life Cycle: Nursing Informatics and Organizational Decision Making Chapter 11 Administrative Information Systems Chapter 12 The Human–Technology Interface Chapter 13 Electronic Security Chapter 14 Nursing Informatics: Improving Workflow and Meaningful Use

Nursing informatics (NI) and information technology (IT) have invaded nursing, and some nurses are happy with the capabilities afforded by this specialty. Others, however, remain convinced that the changes wrought by IT are nothing more than a nuisance. In the past, nursing administrators have found the implementation of technology tools to be an expensive venture with minimal rewards. This disappointment is likely related to their lack of knowledge about NI, which caused nursing administrators to listen to vendors or other colleagues; in essence, it was decision making based on limited and biased information. There were at least two reasons for the experience of limited rewards. First, nurses were rarely included in the testing and implementation of products designed for nurses and nursing tasks. Second, the new products they purchased had to interface with old, legacy systems that were not at all compatible or seemed compatible until the glitches arose. These glitches caused frustration for clinicians and administrators alike. They purchased tools that should have made the nurses happy, but instead all they did was grumble.

The good news is that approaches have changed as a result of the difficult lessons learned from the early forays into technology tools. Nursing personnel are involved both at the agency level and at the vendor level, in the decision-making process and development of new systems and products charged with enhancing the practice of nursing. Older legacy systems are being replaced with newer systems that have more capacity to interface with other systems. Nurses and administrators have become more astute in the realm of NI, but there is still a long way to go. The Systems Development Life Cycle: Nursing Informatics and Organizational Decision Making chapter introduces the system development life cycle, which is used to make important and appropriate organizational decisions for technology adoption.

Administrators need information systems that facilitate their administrative role, and they particularly need systems that provide financial, risk management, quality assurance, human resources, payroll, patient registration, acuity, communication, and scheduling functions. The administrator must be open to learning about all of the tools available. One of the most important tasks that an administrator can oversee and engage in is data mining, or the extraction of data and information from sizeable data sets that have been collected and warehoused. Data mining helps to identify patterns in aggregate data, gain insights, and ultimately discover and generate knowledge applicable to nursing science. To take advantage of these benefits, nursing administrators must become astute informaticists—knowledge workers who harness the information and knowledge at their fingertips to facilitate the practice of their clinicians, improve patient care, and advance the science of nursing.

Clinical information systems (CIS) have traditionally been designed for use by one unit or department within an institution. However, because clinicians working in other areas of the organization need access to this information, these data and information are generally used by more than one area. The new initiatives arising with the development of the electronic health record place institutions in the position of striving to manage their CIS through the electronic health record. Currently, there are many CISs, including nursing, laboratory, pharmacy, monitoring, and order entry, plus additional ancillary systems to meet the individual institutions’ needs. The Administrative Information Systems chapter provides an overview of administrative information systems and helps the reader to understand the powerful data aggregation and data mining tools afforded by these systems.

The Human–Technology Interface chapter discusses the need to improve quality and safety outcomes significantly in the United States. Through the use of IT, the designs for human–technology interfaces can be radically improved so that the technology better fits both human and task requirements. A number of useful tools are currently available for the analysis, design, and evaluation phases of development life cycles and should be used routinely by informatics professionals to ensure that technology better fits both task and user requirements. In this chapter, the author stresses that the focus on interface improvement using these tools has dramatically improved patient safety in a specific area of health care: anesthesiology. With increased attention from informatics professionals and engineers, the same kinds of improvements should be possible in other areas. This human–technology interface is a crucial area if the theories, architectures, and tools provided by the building block sciences are to be implemented.

Each organization must determine who can access and use its information systems and provide robust tools for securing information in a networked environment. The Electronic Security chapter addresses the important safeguards for protecting information. As new technologies designed to enhance patient care are adopted, barriers to implementation and resistance by practitioners to change are frequently encountered. The Nursing Informatics: Improving Workflow and Meaningful Use chapter provides insights into clinical workflow analysis and provides advice on improving efficiency and effectiveness to achieve meaningful

use of caring technologies. Pause to reflect on the Foundation of Knowledge model (Figure III-1) and its relationship to both personal and organizational

knowledge management. Consider that organizational decision making must be driven by appropriate information and knowledge developed in the organization and applied with wisdom. Equally important to adopting technology within an organization is the consideration of the knowledge base and knowledge capabilities of the individuals within that organization. Administrators must use the system development life cycle wisely and carefully consider organizational workflow as they adopt NI technology for meaningful use.

The reader of this section is challenged to ask the following questions: (1) How can I apply the knowledge gained from my practice setting to benefit my patients and enhance my practice; (2) How can I help my colleagues and patients understand and use the current technology that is available; and (3) How can I use my wisdom to create the theories, tools, and knowledge of the future?

Figure III-1 Foundation of Knowledge Model Source: Designed by Alicia Mastrian

Chapter 10

Systems Development Life cycle: nursing informatics and organizational Decision Making Dee McGonigle and Kathleen Mastrian

OBJECTIVES

1. Describe the systems development life cycle (SDLC). 2. Explore selected approaches to SDLC. 3. Assess interoperability and its importance in addressing and meeting the challenges of implementing the HITECH Act in health care. 4. Reflect on the past to move forward into the future to determine how new systems will be developed, integrated, and made interoperable in

health care.

Key Terms

Chief information officer Computer-aided software engineering Dynamic system development method End users Health management information system Hospital information system Information technology Integration Interoperability Iteration Milestones MoSCoW Object-oriented systems

development Open source software Prototype Rapid application development Rapid prototyping Repository Systems development life cycle TELOS strategy Waterfall model

Introduction The following case scenario demonstrates the need to have all of the stakeholders involved from the beginning to the end of the systems development life cycle (SDLC). Creating the right team to manage the development is a key. Various methodologies have been developed to guide this process. This chapter reviews the following approaches to SDLC: waterfall, rapid prototyping or rapid application development (RAD), object-oriented system development (OOSD), and dynamic system development method (DSDM). When reading about each approach, think about the case scenario and how important it is to understand the specific situational needs and the various methodologies for bringing a system to life. As in this case, it is generally necessary or beneficial to use a hybrid approach that blends two or more models for a robust development process.

As the case demonstrates, the process of developing systems or SDLC is an ongoing development with a life cycle. The first step in developing a system is to understand the problem or business needs. It is followed by understanding the solution or how to address those needs; developing a plan; implementing the plan; evaluating the implementation; and finally, maintenance, review, and destruction. If the system needs major upgrading outside of the scope of the maintenance phase, if it needs to be replaced because of

technological advances, or if the business needs change, a new project is launched, the old system is destroyed, and the life cycle begins anew.

SDLC is a way to deliver efficient and effective information systems that fit with the strategic business plan of an organization. The business plan stems from the mission of the organization. In the world of health care, its development includes a needs assessment for the entire organization, which should include outreach linkages (as seen in the case scenario) and partnerships and merged or shared functions. The organization’s participating physicians and other ancillary professionals and their offices are included in thorough needs assessments. When developing a strategic plan, the design must take into account the existence of the organization within the larger healthcare delivery system and assess the various factors outside of the organization itself, including technological, legislative, and environmental issues that impact the organization. The plan must identify the needs of the organization as a whole and propose solutions to meet those needs or a way to address the issues.

SDLC can occur within an organization, be outsourced, or be a blend of the two approaches. With outsourcing, the team hires an outside organization to carry out all or some of the development. Developing systems that truly meet business needs is not an easy task and is quite complex. Therefore, it is common to run over budget and miss milestones. When reading this chapter, reflect on the case scenario and in general the challenges teams face when developing systems.

CASE SCENARIO

Envision two large healthcare facilities that merge resources to better serve their community. This merger is called the Wellness Alliance, and its mission is to establish and manage community health programming that addresses the health needs of the rural, underserved populations in the area. The Wellness Alliance would like to establish pilot clinical sites in five rural areas to promote access and provide health care to these underserved consumers. Each clinical site will have a full-time program manager and three part-time employees (a secretary, a nurse, and a doctor). Each program manager will report to the wellness program coordinator, a newly created position within the Wellness Alliance.

Because you are a community health nurse with extensive experience, you have been appointed as the wellness program coordinator. Your directive is to establish these clinical sites within 3 months and report back in 6 months as to the following: (1) community health programs offered, (2) level of community involvement in outreach health programs and clinical site–based programming, (3) consumer visits made to the clinical site, and (4) personnel performance.

You are excited and challenged, but soon reality sets in: You know that you have five different sites with five different program managers. You need some way to gather the vital information from each of them in a similar manner so that the data are meaningful and useful to you as you develop your reports and evaluate the strengths and weaknesses of the pilot project. You know that you need a system that will handle all of the pilot project’s information needs.

Your first stop is the chief information officer of the health system, a nurse informaticist. You know her from the health management information system miniseminar that she led. After explaining your needs, you share with her the constraint that this system must be in place in 3 months when the sites are up and running before you make your report. When she begins to ask questions, you realize that you do not know the answers. All you know is that you must be able to report on which community health programs were offered, track the level of community involvement in outreach health programs and clinical site–based programming, monitor consumer visits made to the clinical site, and monitor the performance of site personnel. You know that you want accessible, real-time tracking, but as far as programming and clinical site–related activities are concerned, you do not have a precise description of either the process and procedures that will be involved in implementing the pilot or the means by which they will gather and enter data.

The chief information officer requires that you and each program manager remain involved in the development process. She assigns an information technology (IT) analyst to work with you and your team in the development of a system that will meet your current needs. After the first meeting, your head is spinning: The IT analyst has challenged your team not only to work out the process for your immediate needs, but also to envision what your needs will be in the future. At the next meeting, you tell the analyst that your team does not feel comfortable trying to map everything out at this point. He states that there are several ways to go about building the system and software by using the systems development life cycle (SDLC). Noticing the blank look on everyone’s faces, he explains that the SDLC is a series of actions used to develop an information system. The SDLC is similar to the nursing process, in which the nurse must assess, diagnose, plan, implement, evaluate, and revise. If the plan developed in this way does not meet the patient’s need or if a new problem arises, the nurse either revises and updates the plan or starts anew. Likewise, you will plan, analyze, design, implement, operate, support, and secure the proposed community health system.

The SDLC is an iterative process—a conceptual model that is used in project management describing the phases involved in building or developing an information system. It moves from assessing feasibility or project initiation, to design analysis, to system specification, to programming, to testing, to implementation, to maintenance, and to destruction—literally from beginning to end. As the IT analyst describes this process, once again he sees puzzled looks. He quickly states that even the destruction of the system is planned—that is, how it will be retired, broken down, and replaced with a new system. Even during upgrades, destruction tactics can be invoked to secure the data and even decide if servers are to be disposed of or repurposed. The security people will tell you that this is their phase, where they make sure that any sensitive information is properly handled and decide whether the data are to be securely and safely archived or destroyed.

After reviewing all of the possible methods and helping you to conduct your feasibility and business study, the analyst chooses the dynamic system development method (DSDM). This SDLC model was chosen because it works well when the time span is short and the requirements are fluctuating and mainly unknown at the outset. The IT analyst explains that this model works well on tight schedules and is a highly iterative and incremental approach stressing continuous user input and involvement. As part of this highly iterative process, the team will revisit and loop through the same development activities numerous times; this repetitive

examination provides ever-increasing levels of detail, thereby improving accuracy. The analyst explains that you will use a mockup of the hospital information system (HIS) and design for what is known; you will then create your own minisystem that will interface with the HIS. Because time is short, the analysis, design, and development phases will occur simultaneously while you are formulating and revising your specific requirements through the iterative process so that they can be integrated into the system.

The functional model iteration phase will be completed in 2 weeks based on the information that you have given to the analyst. At that time, the prototype will be reviewed by the team. The IT analyst tells you to expect at least two or more iterations of the prototype based on your input. You should end with software that provides some key capabilities. Design and testing will occur in the design and build iteration phase and continue until the system is ready for implementation, the final phase. This DSDM should work well because any previous phase can be revisited and reworked through its iterative process.

One month into the SDLC process, the IT analyst tells the team that he will be leaving his position at Wellness Alliance. He introduces his replacement. She is new to Wellness Alliance and is eager to work with the team. The initial IT analyst will be there 1 more week to help the new analyst with the transition. When he explains that you are working through DSDM, she looks a bit panicky and states that she has never used this approach. She has used the waterfall, prototyping, iterative enhancement, spiral, and object-oriented methodologies—but never the DSDM. From what she heard, DSDM is new and often runs amok because of the lack of understanding as to how to implement it appropriately. After 1 week on

the project, the new IT analyst believes that this approach was not the best choice. As the leader of this SDLC, she is growing concerned about having a product ready at the point when the clinical sites open. She might combine another method to create a hybrid approach with which she would be more comfortable; she is thinking out loud and has everyone very nervous.

The IT analyst reviews the equipment that has arrived for the sites and is excited to learn that the Mac computers were ordered from Apple. They will be powerful and versatile enough for your needs.

Two months after the opening of the clinical sites, you as the wellness program coordinator are still tweaking the system with the help of the IT analyst. It is hard to believe how quickly the team was able to get a robust system in place. As you think back on the process, it seems so long ago that you reviewed the HIS for deficiencies and screen shots. You reexamined your requirements and watched them come to life through five prototype iterations and constant security updates. You trained your personnel on its use, tested its performance, and made final adjustments before implementation. Your own stand-alone system that met your needs was installed and fully operational on the Friday before you opened the clinic doors on Monday, 1 day ahead of schedule. You are continuing to evaluate and modify the system, but that is how the SDLC works: It is never finished, but rather constantly evolving.

Waterfall Model The waterfall model is one of the oldest methods and literally depicts a waterfall effect—that is, the output from each previous phase flows into or becomes the initial input for the next phase. This model is a sequential development process in that there is one pass through each component activity from conception or feasibility through implementation in a linear order. The deliverables for each phase result from the inputs and any additional information that is gathered. There is minimal or no iterative development where one takes advantage of what was learned during the development of earlier deliverables. Many projects are broken down into six phases (Figure 10-1), especially small- to medium-size projects.

Figure 10-1 Waterfall phases

Feasibility

As the term implies, the feasibility study is used to determine whether the project should be initiated and supported. This study should generate a project plan and estimated budget for the SDLC phases. Often, the TELOS strategy—technological and systems, economic, legal, operational, and schedule feasibility—is followed. Technological and systems feasibility addresses the issues of technological capabilities, including the expertise and infrastructure to complete the project. Economic feasibility is the cost–benefit analysis, weighing the benefits versus the costs to determine whether the project is fiscally possible to do and worth undertaking. Formal assessments should include return on investment. Legal feasibility assesses the legal ramifications of the project, including current contractual obligations, legislation, regulatory bodies, and liabilities that could affect the project. Operational feasibility determines how effective the project will be in meeting the needs and expectations of the organization and actually achieving the goals of the project or addressing and solving the business problem. Schedule feasibility assesses the viability of the time frame, making sure it is a reasonable estimation of the time and resources necessary for the project to be developed in time to attain the benefits and meet constraints. TELOS helps to provide a clear picture of the feasibility of the project.

Analysis

During the analysis phase, the requirements for the system are teased out from a detailed study of the business needs of the organization. As part of this analysis, work flows and business practices are examined. It may be necessary to consider options for changing the business process.

Design

The design phase focuses on high- and low-level design and interface and data design. At the high-level phase, the team establishes

which programs are needed and ascertains how they will interact. At the low-level phase, team members explore how the individual programs will actually work. The interface design determines what the look and feel will be or what the interfaces will look like. During data design, the team critically thinks about and verifies which data are required or essential.

The analysis and design phases are vital in the development cycle, and great care is taken during these phases to ensure that the software’s overall configuration is defined properly. Mockups or prototypes of screen shots, reports, and processes may be generated to clarify the requirements and get the team or stakeholders on the same page, limiting the occurrence of glitches that might result in costly software development revisions later in the project.

Implement

During this phase, the designs are brought to life through programming code. The right programming language, such as C++, Pascal, Java, and so forth, is chosen based on the application requirements.

Test

The testing is generally broken down into five layers: (1) the individual programming modules, (2) integration, (3) volume, (4) the system as a whole, and (5) beta testing. Typically, the programs are developed in a modular fashion, and these individual modules are then subjected to detailed testing. The separate modules are subsequently synthesized, and the interfaces between the modules are tested. The system is evaluated with respect to its platform and the expected amount or volume of data. It is then tested as a complete system by the team. Finally, to determine if the system performs appropriately for the user, it is beta tested. During beta testing, users put the new system through its paces to make sure that it does what they need it to do to perform their jobs.

Maintain

Once the system has been finalized from the testing phase, it must be maintained. This could include user support through actual software changes necessitated through use or time.

The waterfall approach is linear and progresses sequentially. The main lack of iterative development is seen as a major weakness, according to Purcell (2007). No projects are static, and typically changes occur during the SDLC. As requirements change, there is no way to address them formally using the waterfall method after project requirements are developed. The waterfall model should be used for simple projects when the requirements are well known and stable from the outset.

Rapid Prototyping or Rapid Application Development As technology advances and faster development is expected, rapid prototyping, also known as rapid application development (RAD), provides a fast way to add functionality through prototyping and user testing. It is easier for users to examine actual prototypes rather than documentation. A rapid requirements-gathering phase relies on workshops and focus groups to build a prototype application using real data. This prototype is then beta tested with users, and their feedback is used to perfect or add functionality and capabilities to the system. According to Alexandrou (2010), “RAD (rapid application development) proposes that products can be developed faster and of higher quality” (para. 1). The RAD approach uses informal communication, repurposes components, and typically follows a fast-paced schedule. Object-oriented programming using such languages as C++ and Java promotes software repurposing and reuse.

The major advantage is the speed with which the system can be deployed; a working, usable system can be built within 3 months. The use of prototyping allows the developers to skip steps in the SDLC process in favor of getting a mockup in front of the user. At times, the system may be deemed acceptable if it meets a predefined minimum set of requirements rather than all of the identified requirements. This rapid deployment also limits the project’s exposure to change elements. Unfortunately, the fast pace can be its biggest disadvantage in some cases. Once one is locked into a tight development schedule, the process may be too fast for adequate testing to be put in place and completed. The most dangerous lack of testing is in the realm of security.

The RAD approach is chosen because it builds systems quickly through user-driven prototyping and adherence to quick, strict delivery milestones. This approach continues to be refined and honed, and other contemporary manifestations of RAD continue to emerge in the agile software development realm.

Object-Oriented Systems Development The object-oriented systems development (OOSD) model “combines the logic of the systems development life cycle with the power of object-oriented modeling and programming” (Stair & Reynolds, 2008, p. 501). Object-oriented modeling makes an effort to represent real-world objects, by modeling the real-world entities or things (e.g., hospital, patient, account, nurse) into abstract computer software objects. Once the system is object oriented, all of the interactions or exchanges take place between or among the objects. The objects are derived from classes, and “an object consists of both data and the actions that can be performed on the data” (p. 501).

Class hierarchy allows objects to inherit characteristics or attributes from parent classes, which fosters object reuse resulting in less coding. The object-oriented programming languages, such as C++ and Java, promote software repurposing and reuse. Therefore, the class hierarchy must be clearly and appropriately designed to reap the benefits of this SDLC approach, which uses object-oriented programming to support the interactions of objects.

For example, in the case scenario, a system could be developed for the Wellness Alliance to manage the community health programming for the clinic system being set up for outreach. There could be a class of programs, and well-baby care could be an object in the class of programs; programs is a relationship between Wellness Alliance and well-baby care. The program class has

attributes, such as clinic site, location address, or attendees or patients. The relationship itself may be considered an object having attributes, such as pediatric programs. The class hierarchy from which all of the system objects are created with resultant object interactions must be clearly defined.

The OOSD model is a highly iterative approach. The process begins by investigating where object-oriented solutions can address business problems or needs, determining user requirements, designing the system, programming or modifying object modeling (class hierarchy and objects), implementing, user testing, modifying, and implementing the system, and ends with the new system being reviewed regularly at established intervals and modifications being made as needed throughout its life.

Dynamic System Development Method The dynamic system development method (DSDM) is a highly iterative and incremental approach with a high level of user input and involvement. The iterative process requires repetitive examination that enhances detail and improves accuracy. The DSDM has three phases: (1) preproject; (2) project life cycle (feasibility and business studies, functional model iteration, design and build iteration, and implementation); and (3) postproject.

In the preproject phase, buy-in or commitment is established and funding is secured. This helps to identify the stakeholders (administration and end users) and gain support for the project.

In the second phase, the project’s life cycle begins. This phase includes five steps: (1) feasibility, (2) business studies, (3) functional model iteration, (4) design and build iteration, and (5) implementation.

In steps 1 and 2, the feasibility and business studies are completed. The team ascertains if this project meets the required business needs while identifying the potential risks during the feasibility study. In step 1, the deliverables are a feasibility report, project plan, and a risk log. Once the project is deemed feasible, step 2, the business study, is begun. The business study extends the feasibility report by examining the processes, stakeholders, and their needs. It is important to align the stakeholders with the project and secure their buy-in because it is necessary to have user input and involvement throughout the entire DSDM process. Therefore, bringing them in at the beginning of the project is imperative.

Using the MoSCoW approach, the team works with the stakeholders to develop a prioritized requirements list and a development plan. MoSCoW stands for “Must have, Should have, Could have, and Would have.” The “must have” requirements are needed to meet the business needs and are critical to the success of the project. “Should have” requirements are those that would be great to have if possible, but the success of the project does not depend on them being addressed. The “could have” requirements are those that would be nice to have met, and the “would have” requirements can be put off until later; these may be undertaken during future developmental iterations. Timeboxing is generally used to develop the project plan. In timeboxing, the project is divided into sections, each having its own fixed budget and dates or milestones for deliverables. The MoSCoW approach is then used to prioritize the requirements within each section; the requirements are the only variables because the schedule and budget are set. If a project is running out of time or money, the team can easily omit the requirements that have been identified as the lowest priority to meet their schedule and budget obligations. This does not mean that the final deliverable, the actual system, would be flawed or incomplete. Instead, it meets the business needs. According to Haughey (2010), the 80/20 rule or Pareto principle can be applied to nearly everything. The Pareto principle states that 80% of the project comes from 20% of the system requirements; therefore, the 20% of requirements must be the crucial requirements or those with the highest priority. One also must consider the pancake principle: The first pancake is not as good as the rest, and one should know that the first development will not be perfect. This is why it is extremely important to clearly identify the “must have” and “should have” requirements.

In the third step of the project life cycle phase, known as functional model iteration, the deliverables are a functional model and prototype ready for user testing. Once the requirements are identified, the next step is to translate them into a functional model with a functioning prototype that can be evaluated by users. This could take several iterations to develop the desired functionality and incorporate the users’ input. At this stage, the team should examine the quality of the product and revise the list requirements and risk log. The requirements are adjusted, the ones that have been realized are deleted, and the remaining requirements are prioritized. The risk log is revised based on the risk analysis completed during and after prototype development.

The design and build iteration step focuses on integrating functional components and identifying the nonfunctional requirements that need to be in the tested system. Testing is crucial; the team will develop a system that the end users can safely use on a daily basis. The team will garner user feedback and generate user documentation. These efforts provide this step’s deliverable, a tested system with documentation for the next and final phase of the development process.

In the final step of the project life cycle phase, known as implementation, deliverables are the system (ready to use), documentation, and trained users. The requirements list should be satisfied, along with the users’ needs. Training users and implementing the approved system is the first part of this phase, and the final part consists of a full review. It is important to review the impact of the system on the business processes and to determine if it addressed the goals or requirements established at the beginning of the project. This final review determines if the project is completed or if further development is necessary. If further development is needed, preceding phases are revisited. If the project is complete and satisfies the users, then it moves into maintenance and ongoing development.

The final phase is labeled “postproject.” In this phase, the team verifies that the system is functioning properly. Once verified, the maintenance schedule is begun. Because the DSDM is iterative, this postproject phase is seen as ongoing development and any of the deliverables can be refined. This is what makes the DSDM such an iterative development process.

DSDM is one of an increasing number of agile methodologies being introduced, such as Scrum and Extreme Programming. These new approaches address the organizational, managerial, and interpersonal communication issues that often bog down SDLC projects. Empowerment of teams and user involvement enhance the iterative and programming strengths provided in these SDLC models.

Computer-Aided Software Engineering Tools

When reviewing SDLC, the computer-aided software engineering (CASE) tools that will be used must be described. “CASE tools automate many of the tasks required in a systems development effort and encourage adherence to the SDLC, thus instilling a high degree of rigor and standardization [in] the entire systems development process” (Stair & Reynolds, 2008, p. 500). These tools help to reduce cost and development time while enriching the quality of the product. CASE tools contain a repository with information about the system: models, data definitions, and references linking models together. They are valuable in their ability to make sure the models follow diagramming rules and are consistent and complete.

The various types of tools can be referred to as upper CASE tools or lower CASE tools. The upper CASE tools support the analysis and design phases, whereas the lower CASE tools support implementation. The tools can also be general or specific in nature, with the specific tools being designed for a particular methodology.

Two examples of CASE tools are Visible Analyst and Rational Rose. According to Andoh-Baidoo, Kunene, and Walker (n.d.), Visible Analyst “supports structured and object-oriented design (UML),” whereas Rational Rose “supports solely object-oriented design (UML)” (p. 372). Both tools can “build and reverse database schemas for SQL and Oracle” and “support code generation for pre.NET versions of Visual Basic” (p. 372). Visible Analyst can also support shell code generation for pre.NET versions of C and COBOL, whereas Rational Rose can support complete code for C++ and Java. In addition, Andoh-Baidoo et al. found that Rational Rose “[p]rovides good integration with Java, and incorporates common packages into class diagrams and decompositions through classes” (p. 372).

CASE tools have many advantages, including decreasing development time and producing more flexible systems. On the down side, they can be difficult to tailor or customize and use with existing systems.

Open Source Software and Free/Open Source Software Another area that must be discussed with SDLC is open source software (OSS). An examination of job descriptions or advertisements for candidates shows that many IS and IT professionals need a thorough understanding of SDLC and OSS development tools (e.g., PHP, MySQL, and HTML). With OSS, any programmer can implement, modify, apply, reconstruct, and restructure the rich libraries of source codes available from proven, well-tested products. As Vauldes (2008) notes, “Examples of FOSS successes are the Internet, Google, Web 2.0, the GNU/Linux operating system, courseware such as Moodle, and the Veterans Affairs VistA hospital system” (p. 7).

To transform health care, it is necessary for clinicians to use information systems that can share patient data (Goulde & Brown, 2006). This all sounds terrific and many people wonder why it has not happened yet, but the challenges are many. How does one establish the networks necessary to share data between and among all healthcare facilities easily and securely? “Healthcare IT is beginning to adopt open source software to address these challenges” (p. 4). Early attempts at OSS ventures in the healthcare realm failed because of a lack of support or buy-in for sustained effort, technologic lags, authority and credibility, and other such issues. “Spurred by a greater sense of urgency to adopt IT, health industry leaders are showing renewed interest in open source solutions” (p. 5). Health care is realizing the benefits of OSS. According to Goulde and Brown, “other benefits of open source software—low cost, flexibility, opportunities to innovate—are important but independence from vendors is the most relevant for health care” (p. 10).

Interoperability Interoperability, the ability to share information across organizations, will remain paramount under the HITECH Act (see the Legislative Aspects of Nursing Informatics: HITECH and HIPAA chapter). The ability to share patient data is extremely important, both within an organization and across organizational boundaries. According to the Health Information and Management Systems Society (HIMSS, 2010), few healthcare systems take advantage of the full potential of the current state of the art in computer science and health informatics. The consequences of this situation include a drain on financial resources from the economy, the inability to truly mitigate the occurrence of medical errors, and a lack of national preparedness to respond to natural and manmade epidemics and disasters. HIMSS has created the Integration and Interoperability Steering Committee to guide the industry on allocating resources to develop and implement standards and technology needed to achieve interoperability (para. 2).

As we enter into SDLCs, we must be aware of how this type of development will affect both our own healthcare organization and the healthcare delivery system as a whole. In an ideal world, we would all work together to create systems that are integrated within our own organization while having the interoperability to cross organizational boundaries and unite the healthcare delivery system to realize the common goal of improving the quality of care provided to consumers.

Summary At times during the SDLC, new information affects the outputs from earlier phases; the development effort may be reexamined or halted until these modifications can be reconciled with the current design and scope of the project. At other times, teams are overwhelmed with new ideas from the iterative SDLC process that result in new capabilities or features that exceed the initial scope of the project. Astute team leaders will preserve these ideas or initiatives so they can be considered at a later time. The team should develop a list of recommendations to improve the current software when the project is complete. This iterative and dynamic exchange makes the SDLC robust.

As technology and research continue to advance, new SDLC models are being pioneered and revised to enhance development techniques. The interpretation and implementation of any model selected reflect the knowledge and skill of the team applying the model. The success of the project is often directly related to the quality of the organizational decision making throughout the project— that is, how well the plan was followed and documented. United efforts to create systems that are integrated and interoperable will define the future of health care.

THOUGHT-PROVOKING QUESTIONS

1. How would you describe cognitive informatics? Reflect on a plan of care that you have developed for a patient. How could cognitive informatics be used to create tools to help with this important work?

2. Think of a clinical setting you are familiar with and envision artificial intelligence tools. Are there any current tools in use? Which tools would enhance practice in this setting and why?

3. Reflect on the SDLC in relation to the quality of the organizational decision making throughout the project. What are some of the major stumbling blocks faced by healthcare organizations?

References Alexandrou, M. (2010). Rapid application development (RAD) methodology. http://www.mariosalexandrou.com/methodologies/rapid- application-development.asp

Andoh-Baidoo, F., Kunene, K., & Walker, R. (n.d.). An evaluation of CASE tools as pedagogical aids in software development courses. http://www.swdsi.org/swdsi2009/Papers/9K10.pdf

Goulde, M., & Brown, E. (2006). Open source software: A primer for health care leaders. http://www.protecode.com/an-open- source-world-a-primer-on-licenses-obligations-and-your-company/

Haughey, D. (2010). Pareto analysis step by step. http://www.projectsmart.co.uk/pareto-analysis-step-by-step.html Health Information and Management Systems Society (HIMSS). (2010). Integration and interoperability.

http://www.himss.org/library/interoperability-standards?navItemNumber=13323 Purcell, J. (2007). Comparison of software development lifecycle methodologies. Retrieved June 2010 from

http://www2.giac.org/resources/whitepaper/application/217.pdf Stair, R., & Reynolds, G. (2008). Principles of information systems (8th ed.). Boston, MA: Thomson Course Technology. Vauldes, I. (2008). Free and open source software in healthcare 1.0: American Medical Informatics Association. Open Source

Working Group white paper. Retrieved July 2010 from https://www.amia.org/files/final-os-wg%20white%20paper_11_19_08.pdf

Chapter 11

Administrative Information Systems Marianela Zytkowski, Susan Paschke, Dee McGonigle, and Kathleen Mastrian

OBJECTIVES

1. Explore agency-based health information systems. 2. Evaluate how administrators use core business systems in their practice. 3. Assess the function and information output from selected information systems used in healthcare organizations.

Key Terms

Acuity system Admission, discharge, and transfer system American National Standards Institute (ANSI) Attribute Care plan Case management information system Clinical documentation system Clinical information system Collaboration Column Communication system Computerized physician order entry system Core business system Data dictionary Data file Data mart Data mining Data warehouse Database Database management system Decision support Drill-down Electronic health record Entity Entity–relationship diagram Field Financial system Information system Information technology International Organization for Standardization (ISO) Key field Knowledge exchange Laboratory information system Managed care information system Master patient index Order entry system Patient care information system Patient care support system Patient centered Pharmacy information system Picture and archiving communication system Primary key Query Radiology information system Record

Relational database management system (RDMS) Repository Row Scheduling system Stakeholder Standardized plan of care Structured Query Language (SQL) Table Tuple

Introduction To compete in the ever-changing healthcare arena, organizations require quick and immediate access to a variety of types of information, data, and bodies of knowledge for daily clinical, operational, financial, and human resource activities. Information is continuously shared between units and departments within healthcare organizations and is also required or requested from other healthcare organizations, regulatory and government agencies, educational and philanthropic institutions, and consumers.

Healthcare organizations integrate a variety of clinical and administrative types of information systems. These systems collect, process, and distribute patient-centered data to aid in managing and providing care. Together, they create a comprehensive record of the patient’s medical history and support organizational processes. Each of these systems is unique in the way it functions and provides information to clinicians and administrators. An understanding of how each of these types of systems works within healthcare organizations is fundamental in the study of informatics.

Types of Healthcare Organization Information Systems Case Management Information Systems Case management information systems identify resources, patterns, and variances in care to prevent costly complications related to chronic conditions and to enhance the overall outcomes for patients with chronic illness. These systems span past episodes of treatment and search for trends among the records. Once a trend is identified, case management systems provide decision support promoting preventive care. Care plans are a common tool found in case management systems. A care plan is a set of care guidelines that outline the course of treatment and the recommended interventions that should be implemented to achieve optimal results. By using a standardized plan of care, these systems present clinicians with treatment protocols to maximize patient outcomes and support best practices.

Case management information systems are especially beneficial for patient populations with a high cost of care and complex health needs, such as the elderly or patients with chronic disease conditions. For example, such systems may be used in treating patients with AIDS. The case management system applies a care plan to treat the patient and manage care better from outpatient to inpatient visits, where opportunistic infections, such as Pneumocystis jiroveci pneumonia, are common complications (DiJerome, 1992). Avoiding these types of complications requires identifying the right resources for care and implementing preventive treatments across all medical visits. Ultimately, this preventive care decreases the costs of care for patients with AIDS and supports a better quality of life. Such systems increase the value of individual care while controlling the costs and risks associated with long-term health care.

Case management systems compile massive amounts of information obtained over a patient’s lifetime by reaching far beyond the walls of the hospital and track care from one medical visit to the next (Simpson & Falk, 1996). Information collected by these systems is processed in a way that helps to reduce risks, ensure quality, and decrease costs.

Communication Systems Communication systems promote interaction among healthcare providers and between providers and patients. Such systems have historically been kept separate from other types of health information systems and from one another. Healthcare professionals overwhelmingly recognize the value of these systems, however, so they are now more commonly integrated into the design of other types of systems as a newly developing standard within the industry. Examples of communication systems include call light systems, wireless telephones, pagers, e-mail, and instant messaging, which have traditionally been forms of communication targeted at clinicians. Other communication systems target patients and their families. Some patients are now able to access their electronic chart from home via an Internet connection. They can update their own medical record to inform their physician of changes to their health or personal practices that impact their physical condition. Inpatients in hospital settings also receive communication directly to their room. Patients and their families may, for example, review individualized messages with scheduled tests and procedures for the day and confirm menu choices for their meals. These types of systems may also communicate educational messages, such as smoking cessation advice.

As health care begins to introduce more of this technology into practice, the value of having communication tools integrated with other types of systems is being widely recognized. Integrating communication systems with clinical applications provides a real-time approach that facilitates inter-actions among the entire healthcare team, patients, and their families to enhance care. These systems enhance the flow of communication within an organization and promote an exchange of information to care better for patients. The next generation of communication systems will be integrated with other types of healthcare systems and guaranteed to work together smoothly. The Research Brief discusses the economic impact of communication inefficiencies in U.S. hospitals.

Core Business Systems

Core business systems enhance administrative tasks within healthcare organizations. Unlike clinical information systems (CISs), whose aim is to provide direct patient care, these systems support the management of health care within an organization. Core business systems provide the framework for reimbursement, support of best practices, quality control, and resource allocation. There are four common core business systems: (1) admission, discharge, and transfer (ADT) systems; (2) financial systems; (3) acuity systems; and (4) scheduling systems. Admission, discharge, and transfer systems provide the backbone structure for the other types of clinical and business systems (Hassett & Thede, 2003). Admitting, billing, and bed management departments most commonly use ADT systems. These systems hold key information on which all other systems rely. For example, ADT systems maintain the patient’s name; medical record number; visit or account number; and demographic information, such as age, gender, home address, and contact information. Such systems are considered the central source for collecting this type of patient information and communicating it to other types of healthcare information systems.

Research Brief

Researchers attempted to quantify the costs of poor communication, termed “communication inefficiencies,” in hospitals. This qualitative study was conducted in seven acute care hospitals of varying sizes via structured interviews with key informants at each facility. The interview questions focused on four broad categories: (1) communication bottlenecks, (2) negative outcomes as a result of those bottlenecks, (3) subjective perceptions of the potential effectiveness of communication improvements on the negative outcomes, and (4) ideas for specific communication improvements. The researchers independently coded the interview data and then compared results to extract themes.

All of the interviewees indicated that communication was an issue. Inefficiencies revolved around time spent tracking people down to communicate with them, with various estimates provided: 3 hours per nursing shift wasted tracking people down, 20% of productive time wasted on communication bottlenecks, and a reported average of five to six telephone calls to locate a physician. Several respondents pointed to costly medical errors that were the direct result of communication issues. Communication lapses also resulted in inefficient use of clinician resources and increased length of stay for patients.

The researchers developed a conceptual model of communication quality with four primary dimensions: (1) efficiency of resource use, (2) effectiveness of resource use, (3) quality of work life, and (4) service quality. They concluded that the total cost of communication inefficiencies in U.S. hospitals is more than $12 billion annually and estimated that a 500-bed hospital could lose as much as $4 million annually because of such problems. They urge the adoption of information technologies to redesign workflow processes and promote better communication.

Source: The full article appears in Agarwal, R., Sands, D., Schneider, J., & Smaltz, D. (2010). Quantifying the economic impact of communication inefficiencies in U.S. hospitals. Journal of Healthcare Management, 55(4), 265–281.

Financial systems manage the expenses and revenue for providing health care. The finance, auditing, and accounting departments within an organization most commonly use financial systems. These systems determine the direction for maintenance and growth for a given facility. They often interface to share information with materials management, staffing, and billing systems to balance the financial impact of these resources within an organization. Financial systems report fiscal outcomes, which can then be tracked and related to the organizational goals of an institution. These systems are key components in the decision-making process as healthcare institutions prepare their fiscal budgets. They often play a pivotal role in determining the strategic direction for an organization.

Acuity systems monitor the range of patient types within a healthcare organization using specific indicators. They track these indicators based on the current patient population within a facility. By monitoring the patient acuity, these systems provide feedback about how intensive the care requirement is for an individual patient or group of patients. Identifying and classifying a patient’s acuity can promote better organizational management of the expenses and resources necessary to provide care. Acuity systems help predict the ability and capacity of an organization to care for its current population. They also forecast future trends to allow an organization to successfully strategize on how to meet upcoming market demands.

Scheduling systems coordinate staff, services, equipment, and allocation of patient beds. They are frequently integrated with the other types of core business systems. By closely monitoring staff and physical resources, these systems provide data to the financial systems. For example, resource-scheduling systems may provide information about operating room use or availability of intensive care unit beds and regular nursing unit beds. These systems also provide great assistance to financial systems when they are used to track medical equipment within a facility. Procedures and care are planned when the tools and resources are available. Scheduling systems help to track resources within a facility while managing the frequency and distribution of those resources.

Order Entry Systems Order entry systems are one of the most important systems in use today. They automate the way that orders have traditionally been initiated for patients—that is, clinicians place orders using these systems instead of creating traditional handwritten transcriptions onto paper. Order entry systems provide major safeguards by ensuring that physician orders are legible and complete, thereby providing a level of patient safety that was historically missing with paper-based orders. Computerized physician order entry systems provide decision support and automated alert functionality that was unavailable with paper-based orders.

The Institute of Medicine estimates that medical errors cost the United States approximately $37.6 billion each year; nearly $17 billion of those costs are associated with preventable errors. Consequently, the federal agency recommends eliminating reliance on handwriting for ordering medications and other treatment needs (Agency for Healthcare Research and Quality, 2000).

Because of the global concern for patient safety as a result of incorrect and misinterpreted orders, healthcare organizations are incorporating order entry systems into their operations as a standard tool for practice. Such systems allow for clear and legible orders, thereby both promoting patient safety and streamlining care. Although much of the health information technology literature suggests that physicians are resistant to adopting health information technology, a recent study by Elder, Wiltshire, Rooks, BeLue, and Gary (2010) found that physicians who use information technology were more satisfied overall with their careers. The Informatics Tools to Promote Patient Safety and Clinical Outcomes chapter provides more information about the use of computerized physician order entry systems in clinical care.

Patient Care Support Systems Most specialty disciplines within health care have an associated patient care information system. These patient-centered systems focus on collecting data and disseminating information related to direct care. Several of these systems have become mainstream types of systems used in health care. The four systems most commonly encountered in health care include (1) clinical documentation systems, (2) pharmacy information systems, (3) laboratory information systems, and (4) radiology information systems.

Clinical documentation systems, also known as “clinical information systems,” are the most commonly used type of patient care support system within healthcare organizations. CISs are designed to collect patient data in real time. They enhance care by putting data at the clinician’s fingertips and enabling decision making where it needs to occur—that is, at the bedside. For that reason, these systems often are easily accessible at the point of care for caregivers interacting with the patient. CISs are patient centered, meaning they contain the observations, interventions, and outcomes noted by the care team. Team members enter information, such as the plan of care, hemodynamic data, laboratory results, clinical notes, allergies, and medications. All members of the treatment team use clinical documentation systems; for example, pharmacists, allied health workers, nurses, physicians, support staff, and many others access the clinical record for the patient using these systems. Frequently these types of systems are also referred to as the electronic patient record or electronic health record. The Electronic Health Record and Clinical Informatics chapter provides a comprehensive overview of CISs and the electronic health record.

Pharmacy information systems also have become mainstream patient care support systems. They typically allow pharmacists to order, manage, and dispense medications for a facility. They also commonly incorporate information regarding allergies and height and weight to ensure effective medication management. Pharmacy information systems streamline the order entry, dispensing, verification, and authorization process for medication administration. They often interface with clinical documentation and order entry systems so that clinicians can order and document the administration of medications and prescriptions to patients while having the benefits of decision support alerting and interaction checking.

Laboratory information systems were perhaps some of the first systems ever used in health care. Because of their long history of use within medicine, laboratory systems have been models for the design and implementation of other types of patient care support systems. Laboratory information systems report on blood, body fluid, and tissue samples, along with biological specimens collected at the bedside and received in a central laboratory. They provide clinicians with reference ranges for tests indicating high, low, or normal values to make care decisions. Often, the laboratory system provides result information directing clinicians toward the next course of action within a treatment regimen.

The final type of patient care support system commonly found within health care is the radiology information system (RIS) found in radiology departments. These systems schedule, result, and store information related to diagnostic radiology procedures. One feature found in most radiology systems is a picture archiving and communication system (PACS). The PACS may be a stand-alone system, kept separate from the main radiology system, or it can be integrated with the RIS and CIS. These systems collect, store, and distribute medical images, such as computed tomography scans, magnetic resonance images, and X-rays. PACS replace traditional hard-copy films with digital media that are easy to store, retrieve, and present to clinicians. The benefit of RIS and PACS is their ability to assist in diagnosing and storing vital patient care support data. Beird (2000) identified the main benefits of PACS as streamlined workflow, enhanced productivity, and better patient care. Imaging studies can be available in minutes as opposed to 2–6 hours for images in a film-based system. The digital workstations provide enhanced imaging capabilities and on-screen measurement tools to improve diagnostic accuracy. Finally, the archive system stores images in a database that is readily accessible, so that images can be easily retrieved and compared to subsequent testing or shared instantly with consultants.

The mobility of patients both geographically and within a single healthcare delivery system challenges information systems because data must be captured wherever and whenever the patient receives care. In the past, managed care information systems were implemented to address these issues. According to Ciotti and Zodda (1996), a managed care information system “can nimbly cross organizational boundaries, includes an enterprise-wide master patient index (MPI), and offers access across provider, geographic, and departmental lines” (para. 10). Consequently, data can be obtained at any and all of the areas where a patient interacts with the healthcare system. Patient tracking mechanisms continue to be honed, but the financial impact of health care also has changed these systems to some extent. The information systems currently in use enable nurses and physicians to make clinical decisions while being mindful of their financial ramifications. In the future, vast improvements in information systems and systems that support health information exchange are likely to continue to emerge.

Many healthcare organizations now aggregate data in a data warehouse (DW) for the purpose of mining the data to discover new relationships and to build organizational knowledge. Mekhjian, Vasila, and Jones (2008) provide insights into how their healthcare system integrated data from 30 different silos containing physician information into a single comprehensive database. None of the disparate information systems was able to communicate with any of the others, resulting in poor communication, billing errors, and issues with continuity of care. By developing a single comprehensive database, the healthcare system was able to facilitate communications among physicians, particularly consulting physicians from outside the system, and maintain compliance with privacy regulations.

The most basic element of a database system is the data. Data refers to raw facts that can consist of unorganized text, graphics, sound, or video. Information is data that has been processed—it has meaning; information is organized in a way that people find meaningful and useful. Even useful information can be lost if one is mired in unorganized information. Computers can come to the rescue by helping to create order out of chaos. Computer science and information science are designed to help cut down the amount of information to a more manageable size and organize it so that users can cope with it more efficiently through the use of databases and database programs technology. Learning about basic databases and database management programs is paramount so that users can apply data and information management principles in health care. Databases are structured or organized collections of data that are typically the main component of an information system. Databases and database management software allow the user to input, sort, arrange structure, organize, and store data and turn those data into useful information. An individual can set up a personal database to organize recipes, music, names and addresses, notes, bills, and other data. In health care, databases and information systems make key information available to healthcare providers and ancillary

personnel to promote the provision of quality patient care. Box 11-1 provides a detailed description of a database.

BOX 11-1 OVERVIEW OF DATABASE CONSTRUCTION Databases consist of fields (columns) and records (rows). Within each record, one of the fields is identified as the primary key or key field. This primary key contains a code, name, number, or other information that acts as a unique identifier for that record. In the healthcare system, for example, a patient is assigned a patient number or ID that is unique for that patient. As you compile related records, you create data files or tables. A data file is a collection of related records. Therefore, databases consist of one or more related data files or tables.

An entity represents a table, and each field within the table becomes an attribute of that entity. The database developer must critically think about the attributes for each specific entity. For example, the entity “disease” might have the attributes of “chronic disease,” “acute disease,” or “communicable disease.” The name of the entity, “disease,” implies that the entity is about diseases. The fields or attributes are “chronic,” “acute,” or “communicable.”

The entity–relationship diagram specifies the relationship among the entities in the database. Sometimes the implied relationships are readily apparent based on the entities’ definitions; however, all relationships should be specified as to how they relate to one another. Typically, three relationships are possible: (1) one to one, (2) one to many, and (3) many to many. A one-to-one relationship exists between the entities of the table about a patient and the table about the patient’s birth. A one-to-many relationship could exist when one entity is repeatedly used by another entity. Such a one-to-many relationship could then be a table query for age that could be used numerous times for one patient entity. The many-to-many relationship reflects entities that are all used repeatedly by other entities. This is easily explained by the entities of patient and nurse. The patient could have several nurses caring for him or her, and the nurse could have many patients assigned to him or her (see Figure 11-1).

The relational model is a database model that describes data in which all data elements are placed in relation in two-dimensional tables; the relations or tables are analogous to files. A relational database management system (RDMS) is a system that manages data using this kind of relational model. A relational database could link a patient’s table to a treatment table (e.g., by a common field, such as the patient ID number). To keep track of the tables that constitute a database, the database management system uses software called a data dictionary. The data dictionary contains a listing of the tables and their details, including field names, validation settings, and data types. The data type refers to the type of information, such as a name, a date, or a time.

Figure 11-1 Example of an entity relationship diagram (ERD)

The database management system is an important program because before it was available, many health systems and businesses had dozens of database files with incompatible formats. Because patient data come from a variety of sources, these separated, isolated data files required duplicate entry of the same information, thereby increasing the risk of data entry error. The design of the relational databases eliminates data duplication. Some examples of popular database management system software include Microsoft’s Access or Visual FoxPro, Corel’s Paradox, Oracle’s Oracle Database 10g, and IBM’s DB2.

On a large scale, a data warehouse is an extremely large database or repository that stores all of an organization’s or institution’s data and makes these data available for data mining. The DW can combine an institution’s many different databases to provide management personnel with flexible access to the data. On the smaller scale, a data mart represents a large database where the data used by one of the units or a division of a healthcare system are stored and maintained. For example, a university hospital system might store clinical information from its many affiliate hospitals in a DW, and each separate hospital might have a data mart housing its data.

There are many ways to access and retrieve information in databases. Searching information in databases can be done through the use of a query, as is used in Microsoft’s Access database. A query asks questions of the database to retrieve specific data and information. Box 11-2 provides a detailed description of the Structured Query Language (SQL).

Data mining software sorts thorough data to discover patterns and ascertain or establish relationships. This software discovers or uncovers previously unidentified relationships among the data in a database by conducting an exploratory analysis looking for hidden patterns in data. Using such software, the user searches for previously undiscovered or undiagnosed patterns by analyzing the data stored in a DW. Drill-down is a term that means the user can view DW information by drilling down to lower levels of the database to

focus on information that is pertinent to his or her needs at the moment. As users move through databases within the healthcare system, they can access anything from enterprise-wide DWs to data marts.

For example, an infection-control nurse might notice a pattern of methicillin-resistant Staphylococcus aureus infections in the local data mart (a single hospital within a larger system). The nurse might want to find out if the outbreak is local (data mart) or more widespread in the system (DW). The nurse might also query the database to determine if certain patient attributes (e.g., age or medical diagnosis) are associated with the incidence of infection.

These kinds of data mining capabilities are also quite useful for healthcare practitioners who wish to conduct clinical research studies. For example, one might query a database to tease out attributes (patient characteristics) associated with asthma-related hospitalizations. For a more detailed description and review of data mining, refer to the Data Mining as a Research Tool chapter.

BOX 11-2 SQL SQL was originally called SEQUEL or Structured English Query Language. SQL, still pronounced “sequel,” now stands for Structured Query Language; it is a database querying language, rather than a programming language. It is a standard language for accessing and manipulating databases. SQL is “used with relational databases; it allows users to define the structure and organization of stored data, verify and maintain data integrity, control access to the data, and define relationships among the stored data items” (University of California at San Diego, 2010, para. 8). In this way, it simplifies the process of retrieving information from a database in a functional or usable form while facilitating the reorganization of data within the databases.

The relational database management system is the foundation or basis for SQL. An RDMS stores data in “database objects called tables” (W3Schools.com, 2010, para. 6). A table is a collection of related data that consists of columns and rows; as noted earlier, columns are also referred to as fields, and rows are also referred to as records or tuples. Databases can have many tables, and each table is identified by a name (see the Database Example: School of Nursing Faculty).

SQL statements handle most of the actions users need to perform on a database. SQL is an International Organization for Standardization (ISO) standard and American National Standards Institute (ANSI) standard, but many different versions of the SQL language exist (Indiana University, 2010). To remain compliant with the ISO and ANSI standards, SQL must handle or support the major commands of SELECT, UPDATE, DELETE, INSERT and WHERE in a similar manner (W3Schools.com, 2010). The SELECT command allows you to extract data from a database. UPDATE updates the data, DELETE deletes the data, and INSERT inserts new data. WHERE is used to specify selection criteria, thereby restricting the results of the SQL query. Thus SQL allows you to create databases and manipulate them by storing, retrieving, updating, and deleting data.

The database example provided here reflects the faculty listing for a school of nursing. The table that contains the data is identified by the name “Faculty.” The faculty members are each categorized by the following fields (columns): Last Name, First Name, Department Affiliation, Office Phone Number, Office Location, and UserID. Each individual faculty member’s information is a record (tuple or row).

Using the SQL command SELECT, all of the records in the “Faculty” table can be selected:

SELECT * FROM Faculty This command would SELECT all (*) of the records FROM the table known as FACULTY. The asterisk (*) is used to select all of the columns.

According to Mishra, Sharma, and Pandey (2013), there is a new set of challenges and opportunities for managing data, data mining, and establishing algorithms in the clouds. Data mining in the clouds is emerging and evolving. This frontier is becoming a potent way to take advantage of the power of cloud computing and combine it with SQL. The world as we know it is changing: “Clouds” are leading us to develop revolutionary data mining technologies. There are five typical clinical applications for databases: (1) hospitals, (2) clinical research, (3) clinical trials, (4) ambulatory care, and (5) public health. Some healthcare systems are connecting their hospitals together by choosing a single CIS to capture data on a system-wide basis. In such healthcare organizations, multiple application programs share a pool of related data. Think about how potent such databases might potentially be in managing organizations and providing insights into new relationships that may ultimately transform the way work is done.

Department Collaboration and Exchange of Knowledge and Information The implementation of systems within health care is the responsibility of many people and departments. All systems require a partnership of collaboration and knowledge sharing to implement and maintain successful standards of care. Collaboration is the sharing of ideas and experiences for the purposes of mutual understanding and learning. Knowledge exchange is the product of collaboration when sharing an understanding of information promotes learning from past experiences to make better future decisions.

Depending on the type of project, collaboration may occur at many different levels within an organization. At an administrative level, collaboration among key stakeholders is critical to the success of any project. Stakeholders have the most responsibility for completing the project. They have the greatest influence in the overall design of the system, and ultimately they are the people who are most impacted by a system implementation. Together with the organizational executive team, stakeholders collaborate on the overall budget and time frame for a system implementation.

Collaboration may also occur among the various departments impacted by the system. These groups frequently include

representatives from information technology, clinical specialty areas, support services, and software vendors. Once a team is assembled, it defines the objectives and goals of the system. The team members work strategically to align their goals with the goals of the organization where the system is to be used. The focus for these groups is on planning, resource management, transitioning, and ongoing support of the system. Their collaboration determines the way in which the project is managed, the deliverables for the project, the individuals held accountable for the project, the time frame for the project, opportunities for process improvement using the system, and the means by which resources are allocated to support the system.

From collaboration comes the exchange of information and ideas through knowledge sharing. Specialists exchange knowledge within their respective areas of expertise to ensure that the system works for an entire organization. From one another, they learn requirements that make the system successful. This exchange of ideas is what makes healthcare information systems so valuable. A multidisciplinary approach ensures that systems work in the complex environment of healthcare organizations that have diverse and complex patient populations.

Summary The integration of technology within healthcare organizations offers limitless possibilities. As new types of systems emerge, clinicians will become smarter and more adept at incorporating these tools into their daily practice. Success will be achieved when health care incorporates technology systems in a way that they are not viewed as separate tools to support healthcare practices, but rather as necessary instruments to provide health care. Patients, too, will become savvier at using healthcare information systems as a means of communication and managing their personal and preventive care. In the future, these two mindsets will become expectations for health care and not simply a high-tech benefit, as they are often viewed today.

Ultimately, it is not the type of systems that are adopted that is important, but rather the method in which they are put into practice. In an ideal world, robust and transparent information technologies will support clinical and administrative functions and promote safe, quality, and cost-effective care.

THOUGHT-PROVOKING QUESTIONS

1. Which type of technology exists today that could be converted into new types of information systems to be used in health care? 2. How could collaboration and knowledge sharing at a single organization be used to help individuals preparing for information technology at a

different facility? 3. Discuss the administrative information systems and their applications.

References Agency for Healthcare Research and Quality. (2000). Medical errors: The scope of the problem. An epidemic of errors. Retrieved August 2007 from

http://www.ahrq.gov/qual/errback.htm Beird, L. (2000). How to satisfy both clinical and information technology goals in designing a successful picture archiving and communication system.

Journal of Digital Imaging, 13 (suppl), 10–12. Retrieved from ProQuest Nursing & Allied Health Source (Document ID: 1951921891). Ciotti, V., & Zodda, F. (1996). Selecting managed care information systems. Journal of the Healthcare Financial Management Ass ociation, 50(6), 35–

36. http://findarticles.com/p/articles/mi_m3257/is_n6_v50/ai_18515376 DiJerome, L. (1992). The nursing case management computerized system: Meeting the challenge of health care delivery through technology. Computers

in Nursing, 10(6), 250–258. Elder, K., Wiltshire, J., Rooks, R., BeLue, R., & Gary, L. (2010, Summer). Health information technology and physician career satisfaction.

Perspectives in Health Information Management, 1–18. Retrieved from ProQuest Nursing & Allied Health Source (Document ID: 2118694921). Hassett, M., & Thede, L. (2003). Information in practice: Clinical information systems. In B. Cunningham (Ed.), Informatics and nursing opportunities

and challenges (2nd ed., rev., pp. 222–239). Philadelphia, PA: Lippincott Williams & Wilkins. Indiana University. (2010). University information technology services knowledge base: What is SQL? http://kb.iu.edu/data/ahux.html Mekhjian, H., Vasila, M., & Jones, K. (2008). Combine and conquer: Computing from a single database. Physician Executive, 34(5), 30–32, 34–35.

Retrieved from ABI/INFORM Global (Document ID: 1651800501). Mishra, N., Sharma, S. & Pandey, A. (2013). High performance cloud data mining algorithm and data mining in clouds.

http://www.iosrjournals.org/iosr-jce/papers/Vol8-Issue4/I0845461.pdf Simpson, R. L., & Falk, C. (1996). Technology and case management. In J. Hodgson (Ed.), Information management in nursing and healthcare (pp.

144–152). Springhouse, PA: Springhouse Corporation. University of California at San Diego. (2010). Data warehouse terms. http://blink.ucsd.edu/technology/help-desk/queries/warehouse/terms.html#s W3Schools.com. (2010). Introduction to SQL. http://www.w3schools.com/SQL/sql_intro.asp

Chapter 12

The Human–Technology Interface Judith A. Effken, Dee McGonigle, and Kathleen Mastrian

OBJECTIVES 1. Describe the human–technology interface. 2. Explore human–technology interface problems. 3. Reflect on the future of the human–technology interface.

Key Terms

Cognitive task analysis Cognitive walkthrough Cognitive work analysis Earcons Ergonomics Field study Gulf of evaluation Gulf of execution Heuristic evaluation Human–computer interaction Human factors Human–technology interaction Human–technology interface Mapping Situational awareness Task analysis Usability Workaround

Introduction Several years ago, one of this chapter’s authors stayed in a new hotel on the outskirts of London. When she entered her room, she encountered three wall-mounted light switches in a row, but with no indication of which lights they operated. In fact, the mapping of switches to lights was so peculiar that she was more often than not surprised by the light that came on when she pressed a particular switch. One might conclude that the author had a serious problem, but she prefers to attribute her difficulty to poor design.

When these kinds of technology design issues surface in health care, they are more than just an annoyance. Poorly designed technology can lead to errors, lower productivity, or even the removal of the system (Alexander & Staggers, 2009). Unfortunately, as more and more kinds of increasingly complex health information technology applications are integrated, the problem becomes even worse (Johnson, 2006). However, nurses are very creative and, if at all possible, will design workarounds that allow them to circumvent troublesome technology. However, workarounds are only a Band-Aid; they are not a long-term solution.

In his classic book The Psychology of Everyday Things, Norman (1988) argued that life would be a lot simpler if people who built the things that others encounter (such as light switches) paid more attention to how they would be used. At least one everyday thing meets Norman’s criteria for good design: the scythe. Even people who have never encountered one will pick up a scythe in the manner needed to use it because the design makes only one way feasible. The scythe’s design fits perfectly with its intended use and a human user. Would it not be great if all technology were so well fit to human use? In fact, this is not such a far-fetched idea. Scientists and engineers are making excellent strides in understanding human–technology interface problems and proposing solutions to them.

By the end of this chapter, the reader will be able to (1) define what is meant by the “human–technology interface”; (2) describe problems with human–technology interfaces currently available in health care; and (3) describe models, strategies, and exemplars for improving interfaces during the analysis, design, and evaluation phases of the development life cycle.

The Human–Technology Interface What is the human–technology interface? Broadly speaking, anytime a human uses technology, some type of hardware or software enables and supports the interaction. It is this hardware and software that defines the interface. The array of light switches described previously was actually an interface (although not a great one) between the lighting technology in the room and the human user.

In today’s healthcare settings, one encounters a wide variety of human–technology interfaces. Those who work in hospitals may use bar-coded identification cards to log their arrival time into a human resources management system. Using the same cards, they might log into their patients’ electronic health record (EHR), access their drugs from a drug administration system, and even administer their drugs using bar-coding technology. Other examples of human–technology interfaces one might encounter include a defibrillator, a patient-controlled analgesia (PCA) pump, any number of physiologic monitoring systems, electronic thermometers, and telephones and pagers.

The human interfaces for each of these technologies are different and can even differ among different brands or versions of the same device. For example, to enter data into an EHR, one might use a keyboard, a light pen, a touch screen, or voice. Healthcare technologies may present information via computer screen, printer, or a personal digital assistant (PDA). Patient data might be displayed in the form of text, pictures (e.g., the results of a brain scan), or even sound (an echocardiogram); and the information may be arrayed or presented differently, based on roles and preferences. Some human–technology interfaces mimic face-to-face human encounters. For example, faculty members are increasingly using videoconferencing technology to communicate with their students. Similarly, telehealth allows nurses to use telecommunication and videoconferencing software to communicate more effectively and more frequently with patients at home by using the technology to monitor patients’ vital signs, supervise their wound care, or demonstrate a procedure. Telehealth technology has fostered other virtual interfaces, such as system-wide intensive care units in which intensivists and specially trained nurses monitor critically ill patients in intensive care units, some of whom may be in rural locations. Sometimes telehealth interfaces allow patients to interact with a virtual clinician (actually a computer program) that asks questions, provides social support, and tailors education to identify patient needs based on the answers to screening questions. These human– technology interfaces have been remarkably successful; sometimes patients even prefer them to live clinicians.

Human–technology interfaces may present information using text, numbers, pictures, icons, or sound. Auditory, visual, or even tactile alarms may alert users to important information. Users may interact with (or control) the technology via keyboards, digital pens, voice activation, or even touch.

A small, but growing number of clinical and educational interfaces rely heavily on tactile input. For example, many students learn to access an intravenous site using virtual technology. Other, more sophisticated virtual reality applications help physicians learn to do endoscopies or practice complex surgical procedures in a safe environment. Still others allow drug researchers to design new medications by combining virtual molecules (here, the tactile response is quite different for molecules that can be joined from those that cannot). In each of these training environments, accurately depicting tactile sensations is critical. For example, feeling the kind and amount of pressure required to penetrate the desired tissues, but not others, is essential to a realistic and effective learning experience.

The growing use of large databases for research has led to the design of novel human–technology interfaces that help researchers visualize and understand patterns in the data that generate new knowledge or lead to new questions. Many of these interfaces now incorporate multidimensional visualizations, in addition to scatter plots, histograms, or cluster representations (Vincent, Hastings- Tolsma, & Effken, 2010). Some designers, such as Quinn (the founder of the Design Rhythmics Sonification Research Laboratory at the University of New Hampshire) and Meeker (2000), use variations in sound to help researchers hear the patterns in large data sets. In Quinn’s (2000) “climate symphony,” different musical instruments, tones, pitches, and phrases are mapped onto variables, such as the amounts and relative concentrations of minerals to help researchers detect patterns in ice core data covering more than 110,000 years. Climate patterns take centuries to emerge and can be difficult to detect. The music allows the entire 110,000 years to be condensed into just a few minutes, making detection of patterns and changes much easier.

The human–technology interface is ubiquitous in health care and takes many forms. A look at the quality of these interfaces follows. Be warned: It is not always a pretty picture.

The Human–Technology Interface Problem In The Human Factor, Vicente (2004) cited the many safety problems in health care identified by the Institute of Medicine’s (1999) report and noted how the technology (defined broadly) used often does not fit well with human characteristics. As a case in point, Vicente described his own studies of nurses’ PCA pump errors. Nurses made the errors, in large part, because of the complexity of the user interface, which required as many as 27 steps to program the device. Vicente and his colleagues developed a PCA in which programming required no more than 12 steps. Nurses who used it in laboratory experiments made fewer errors, programmed drug delivery faster, and reported lower cognitive workloads compared to the commercial device. Further evidence that human–technology interfaces do not work as well as they might is evident in the following events.

Doyle (2005) reported that when a bar-coding medication system interfered with their workflow, nurses devised workarounds, such as removing the armband from the patient and attaching it to the bed, because the bar-code reader failed to interpret bar codes when the bracelet curved tightly around a small arm. Koppel et al. (2005) reported that a widely used computer-based provider order entry (CPOE) system meant to decrease medication errors actually facilitated 22 types of errors because the information needed to order medications was fragmented across as many as 20 screens, available medication dosages differed from those the physicians expected, and allergy alerts were triggered only after an order was written.

Han et al. (2005) reported increased mortality among children admitted to Children’s Hospital in Pittsburgh after CPOE implementation. Three reasons were cited for this unexpected outcome. First, CPOE changed the workflow in the emergency room. Before CPOE, orders were written for critical time-sensitive treatment based on radio communication with the incoming transport team before the child arrived. After CPOE implementation, orders could not be written until the patient arrived and was registered in the system (a policy that was later changed). Second, entering an order required as many as 10 clicks and took as long as 2 minutes;

moreover, computer screens sometimes froze or response time was slow. Third, when the team changed its workflow to accommodate CPOE, face-to-face contact among team members diminished. Despite the problems with study methods identified by some of the informatics community, there certainly were serious human–technology interface problems.

In 2005, a Washington Post article reported that Cedars-Sinai Medical Center in Los Angeles had shut down a $34 million system after 3 months because of the medical staff’s rebellion. Reasons for the rebellion included the additional time it took to complete the structured information forms, failure of the system to recognize misspellings (as nurses had previously done), and intrusive and interruptive automated alerts (Connolly, 2005). Even though physicians actually responded appropriately to the alerts, modifying or canceling 35% of the orders that triggered them, designers had not found the right balance of helpful-to-interruptive alerts. The system simply did not fit the clinicians’ workflow.

Such unintended consequences (Ash, Berg, & Coiera, 2004) or unpredictable outcomes (Aarts, Doorewaard, & Berg, 2004) of healthcare information systems may be attributed, in part, to a flawed implementation process, but there were clearly also human– technology interaction issues. That is, the technology was not well matched to the users and the context of care. In the pediatric case, a system developed for medical–surgical units was implemented in a critical care unit.

Human–technology interface problems are the major cause of as many as 87% of all patient monitoring incidents (Walsh & Beatty, 2002). It is not always that the technology itself is faulty. In fact, the technology may perform flawlessly, but the interface design may lead the human user to make errors (Vicente, 2004).

Improving the Human–Technology Interface Much can be learned from the related fields of cognitive engineering, human factors, and ergonomics about how to make interfaces more compatible with their human users and the context of care. Each of these areas of study is multidisciplinary and integrates knowledge from multiple disciplines (e.g., computer science, engineering, cognitive engineering, psychology, and sociology). Over the years, three axioms have evolved for developing effective human–computer interactions (Staggers, 2003): (1) Users must be an early and continuous focus during interface design; (2) the design process should be iterative, allowing for evaluation and correction of identified problems; and (3) formal evaluation should take place using rigorous experimental or qualitative methods.

Axiom 1: Users Must Be an Early and Continuous Focus During Interface Design

Rubin (1994) uses the term user-centered design to describe the process of designing products (e.g., human–technology interfaces) so that users can carry out the tasks needed to achieve their goals with “minimal effort and maximal efficiency” (p. 10). Thus, in user- centered design, the end user is emphasized.

Vicente (2004) argued that technology should fit human requirements at five levels of analysis (physical, psychological, team, organizational, and political). Physical characteristics of the technology (e.g., size, shape, or location) should conform to the user’s size, grasp, and available space). Information should be presented in ways that are consistent with known human psychological capabilities (e.g., the number of items that can be remembered is seven plus or minus two). In addition, systems should conform to the communication, workflow, and authority structures of work teams; to organizational factors, such as culture and staffing levels; and even to political factors, such as budget constraints, laws, or regulations.

A number of analysis tools and techniques have been developed to help designers better understand the task and user environment for which they are designing. Discussed next are task analysis, cognitive task analysis, and cognitive work analysis (CWA).

Task analysis examines how a task must be accomplished. Generally, analysts describe the task in terms of inputs needed for the task, outputs (what is achieved by the task), and any constraints on actors’ choices on carrying out the task. Analysts then lay out the sequence of temporally ordered actions that must be carried out to complete the task in flowcharts (Vicente, 1999). A worker’s tasks must be analyzed. Task analysis is very useful in defining what users must do and which functions might be distributed between the user and technology (U.S. Department of Health and Human Services, 2013). Cognitive task analysis usually starts by identifying, through interviews or questionnaires, the particular task and its typicality and frequency. Analysts then may review the written materials that describe the job or are used for training and determine, through structured interviews or by observing experts perform the task, which knowledge is involved and how that knowledge might be represented.

Cognitive work analysis was developed specifically for the analysis of complex, high-technology work domains, such as nuclear power plants, intensive care units, and emergency departments where workers need considerable flexibility in responding to external demands (Burns & Hajdukiewicz, 2004; Vicente, 1999). A complete CWA includes five types of analysis: (1) work domain, (2) control tasks, (3) strategies, (4) social–organizational, and (5) worker competencies. The work domain analysis describes the functions of the system and identifies the information that users need to accomplish their task goals. The control task analysis investigates the control structures through which the user interacts with or controls the system. It also identifies which variables and relations among variables discovered in the work domain analysis are relevant for particular situations so that context-sensitive interfaces can present the right information (e.g., prompts or alerts) at the right time. The strategies analysis looks at how work is actually done by users to facilitate the design of appropriate human–computer dialogues. The social–organizational analysis identifies the responsibilities of various users (e.g., doctors, nurses, clerks, or therapists) so that the system can support collaboration, communication, and a viable organizational structure. Finally, the worker competencies analysis identifies design constraints related to the users themselves (Effken, 2002).

Specialized tools are available for the first three types of CWA (Vicente, 1999). Analysts typically borrow tools (e.g., ethnography) from the social sciences for the two remaining types. Hajdukiewicz, Vicente, Doyle, Milgram, and Burns (2001) used CWA to model an operating room environment. Effken (2002) and Effken, Loeb, Johnson, Johnson, and Reyna (2001) used CWA to analyze the information needs for an oxygenation management display for an ICU. Other examples of the application of CWA in health care are described by Burns and Hajdukiewicz (2004) in their chapter on medical systems (pp. 201–238).

Axiom 2: The Design Process Should Be Iterative, Allowing for Evaluation and Correction of Identified

Problems

Today both principles and techniques for developing human–technology interfaces that people can use with minimal stress and maximal efficiency are available. An excellent place to start is with Norman’s (1988, pp. 188–189) principles:

1. Use both knowledge in the world and knowledge in the head. In other words, pay attention not only to the environment or to the user, but to both, and to how they relate. By using both, the problem actually may be simplified.

2. Simplify the structure of tasks. For example, reduce the number of steps or even computer screens needed to accomplish the goal.

3. Make things visible: Bridge the gulfs of execution and evaluation. Users need to be able to see how to use the technology to accomplish a goal (e.g., which buttons does one press and in which order to program this PCA?); if they do, then designers have bridged the gulf of execution. They also need to be able to see the effects of their actions on the technology (e.g., if a nurse practitioner prescribes a drug to treat a certain condition, the actual patient response may not be perfectly clear). This bridges the gulf of evaluation.

4. Get the mappings right. Here, the term mapping is used to describe how environmental facts (e.g., the order of light switches or variables in a physiologic monitoring display) are accurately depicted by the information presentation.

5. Exploit the power of constraints, both natural and artificial. Because of where the eyes are located in the head, humans have to turn their heads to see what is happening behind them; however, that is not true of all animals. As the location of one’s eyes constrains what one can see, so also do physical elements, social factors, and even organizational policy constrain the way tasks are accomplished. By taking these constraints into account when designing technology, it can be made easier for humans use.

6. Design for error. Mistakes happen. Technology should eliminate predictable errors and be sufficiently flexible to allow humans to identify and recover from unpredictable errors.

7. When all else fails, standardize. To get a feel for this principle, think how difficult it is to change from a Macintosh to a Windows environment or from the Windows operating system to Vista.

Kirlik and Maruyama (2004) described a real-world human–technology interface that follows Norman’s principles. The authors observed how a busy expert short-order cook strategically managed to grill many hamburgers at the same time, but each to the customer’s desired level of doneness. The cook put those burgers that were to be well-done on the back and far right portion of the grill, those to be medium well-done in the center of the grill, and those to be rare at the front of the grill, but farther to the left. The cook moved all burgers to the left as grilling proceeded and turned them over during their travel across the grill. Everything the cook needed to know was available in this simple interface. As a human–technology interface, the grill layout was elegant. The interface used knowledge housed both in the environment and in the expert cook’s head; also, things were clearly visible, both in the position of the burgers and in the way they were moved. The process was clearly and effectively standardized, with built-in constraints. What might it take to create such an intuitive human–technology interface in health care?

Several useful books have been written about effective interface design (e.g., Burns & Hajdukiewicz, 2004; Cooper, 1995; Mandel, 1997). In addition, a growing body of research is exploring new ways to present clinical data that might facilitate clinicians’ problem identification and accurate treatment (AHRQ, 2010). Just as in other industries, health care is learning that big data can provide big insights if it can be visualized, accessed, and meaningful (Intel IT Center, 2013). Often designers use graphical objects to show how variables relate. The first to do so were likely Cole and Stewart (1993), who used changes in the lengths of the sides and area of a four- sided object to show the relationship of respiratory rate to tidal volume. Other researchers have demonstrated that histograms and polygon displays are better than numeric displays for detecting changes in patients’ physiologic variables (Gurushanthaiah, Weinger, & Englund, 1995). When Horn, Popow, and Unterasinger (2001) presented physiologic data via a single circular object with 12 sectors (where each sector represented a different variable), nurses reported that it was easy to recognize abnormal conditions, but difficult to comprehend the patient’s overall status. This kind of graphical object approach has been most widely used in anesthesiology, where a number of researchers have shown improved clinician situational awareness or problem detection time by mapping physiologic variables onto display objects that have meaningful shapes, such as using a bellows-like object to represent ventilation (Agutter et al., 2003; Blike, Surgenor, Whallen, & Jensen, 2000; Michels, Gravenstein, & Westenskow, 1997; Zhang et al., 2002).

Effken (2006) compared a prototype display that represented physiologic data in a structured pictorial format with two bar graph displays. The first bar graph display and the prototype both presented data in the order that experts were observed to use them. The second bar graph display presented the data in the way that nurses collected them. In an experiment in which resident physicians and novice nurses used simulated drugs to treat observed oxygenation management problems using each display, residents’ performance was improved with the displays ordered as experts used them, but nurses’ performance was not improved. Instead, nurses performed better when the variables were ordered as they were used to collecting them, demonstrating the importance of understanding user roles and the tasks they need to accomplish.

Data also need to be represented in ways other than visually. Gaver (1993) proposed that because ordinary sounds map onto familiar events, they could be used as icons to facilitate easier technology navigation and use and to provide continuous background information about how a system is functioning. In health care, auditory displays have been used to provide clinicians with information about patients’ vital signs (e.g., in pulse oximetry), such as by altering volume or tone when a significant change occurs (Sanderson, 2006).

Admittedly, auditory displays are probably more useful for quieter areas of the hospital, such as the operating room. Perhaps that is why researchers have most frequently applied the approach in anesthesiology. For example, Loeb and Fitch (2002) reported that anesthesiologists detected critical events more quickly when auditory information about heart rate, blood pressure, and respiratory parameters was added to a visual display. Auditory tones also have been combined as earcons to represent relationships among data elements, such as the relationship of systolic to diastolic blood pressure (Watson & Gill, 2004).

Axiom 3: Formal Evaluation Should Take Place Using Rigorous Experimental or Qualitative Methods

Perhaps one of the highest accolades that any interface can achieve is to say that it is transparent. An interface becomes transparent when it is so easy to use that users no longer think about it, but only about the task at hand. For example, a transparent clinical interface would enable clinicians to focus on patient decisions rather than on how to access or combine patient data from multiple sources. In Figure 12-1, instead of the nurse interacting with the computer, the nurse and the patient interact through the technology interface. The more transparent the interface, the easier the interaction should be.

Figure 12-1 Nurse–patient interaction framework in which the technology supports the interaction Source: Adapted from Staggers, N., & Parks, P. L. (1993). Description and initial applications of the Staggers & Parks nurse–computer interaction framework. Computers in Nursing, 11, 282–290. Reprinted by permission of AMIA.

Usability is a term that denotes the ease with which people can use an interface to achieve a particular goal. Usability of a new human–technology interface needs to be evaluated early and often throughout its development. Typical usability indicators include ease of use, ease of learning, satisfaction with using, efficiency of use, error tolerance, and fit of the system to the task (Staggers, 2003). Some of the more commonly used approaches to usability evaluation are discussed next.

Surveys of Potential or Actual Users

Chernecky, Macklin, and Waller (2006) assessed cancer patients’ preferences for website design. Participants were asked their preferences for a number of design characteristics, such as display color, menu buttons, text, photo size, icon metaphor, and layout, by selecting on a computer screen their preferences for each item from two or three options.

Focus Group

Typically used at the very start of the design process, focus groups can help the designer better understand users’ responses to potential interface designs and to content that might be included in the interface.

Cognitive Walkthrough

In a cognitive walkthrough, evaluators assess a paper mockup, working prototype, or completed interface by observing the steps users are likely to take to use the interface to accomplish typical tasks. This analysis helps designers determine how understandable and easy to learn the interface is likely to be for these users and the typical tasks (Wharton, Rieman, Lewis, & Polson, 1994).

Heuristic Evaluation

A heuristic evaluation has become the most popular of what are called “discount usability evaluation” methods. The objective of a heuristic evaluation is to detect problems early in the design process, when they can be most easily and economically corrected. The methods are termed “discount” because they typically are easy to do, involve fewer than 10 experts (often experts in relevant fields such as human–computer technology or cognitive engineering), and are much less expensive than other methods. They are called “heuristic” because evaluators assess the degree to which the design complies with recognized usability rules of thumb or principles (the heuristics), such as those proposed by Nielsen (1994) and available on his website (http://www.useit.com/papers/heuristic/heuristic_list.html).

For example, McDaniel and colleagues (2002) conducted a usability test of an interactive computer-based program to encourage smoking cessation by low-income women. As part of the initial evaluation, healthcare professionals familiar with the intended users reviewed the design and layout of the program. The usability test revealed several problems with the decision rules used to tailor content to users that were corrected before implementation.

Formal Usability Test

Formal usability tests typically use either experimental or observational studies of actual users using the interface to accomplish real- world tasks. A number of researchers use these methods. For example, Staggers, Kobus, and Brown (2007) conducted a usability study of a prototype electronic medication administration record. Participants were asked to add, modify, or discontinue medications using the system. The time they needed to complete the task, their accuracy in the task, and their satisfaction with the prototype were assessed (the last criterion through a questionnaire). Although satisfaction was high, the evaluation also revealed design flaws that could be corrected before implementation.

Field Study

In a field study, end users evaluate a prototype in the actual work setting just before its general release. For example, Thompson, Lozano, and Christakis (2007) evaluated the use of touch-screen computer kiosks containing child health-promoting information in several low-income, urban community settings through an online questionnaire that could be completed after the kiosk was used. Most users found the kiosk easy to use and the information it provided easy to understand. Researchers also gained a better understanding of the characteristics of the likely users (e.g., 26% had never used the Internet and 48% had less than a high school education) and the information most often accessed (television and media use, and smoke exposure).

Dykes and her colleagues (2006) used a field test to investigate the feasibility of using digital pen and paper technology to record vital signs as a way to bridge an organization from a paper to an electronic health record. In general, satisfaction with the tool increased with use, and the devices conformed well to nurses’ workflow. However, 8% of the vital sign entries were recorded inaccurately because of inaccurate handwriting recognition, entries outside the recording box, or inaccurate data entry (the data entered were not valid values). The number of modifications needed in the tool and the time that would be required to make those changes ruled out using the digital pen and paper as a bridging technology.

Ideally, every healthcare setting would have a usability laboratory of its own to test new software and technology in its own setting before actual implementation. However, this can be expensive, especially for small organizations. Kushniruk and Borycki (2006) developed a low-cost rapid usability engineering method for creating a portable usability laboratory consisting of videocameras and other technology that one can take out of the laboratory into hospitals and other locations to test the technology on site using as close to a real world environment as possible. This is a much more cost-effective and efficient solution and makes it possible to test all technologies before their implementation.

A Framework for Evaluation Ammenwerth, Iller, and Mahler (2006) proposed a fit between individuals, tasks, and technology (FITT) model that suggests that each of these factors be considered in designing and evaluating human–technology interfaces. It is not enough to consider only the user and technology characteristics; the tasks that the technology supports must be considered as well. The FITT model builds on DeLone and McLean’s (1992) information success model, Davis’s (1993) technology acceptance model, and Goodhue and Thompson’s (1995) task technology fit model. A notable strength of the FITT model is that it encourages the evaluator to examine the fit between the various pairs of components: user and technology, task and technology, and user and task.

Johnson and Turley (2006) compared how doctors and nurses describe patient information and found that doctors emphasized diagnosis, treatment, and management, whereas the nurses emphasized functional issues. Although both physicians and nurses share some patient information, how they thought about patients differed. For that reason, an EHR needs to present information (even the same information) to the two groups in different ways.

Hyun, Johnson, Stetson, and Bakken (2009) used a combination of two models (technology acceptance model and task–technology fit model) to design and evaluate an electronic documentation system for nurses. To facilitate the design, they employed multiple methods, including brainstorming of experts, to identify design requirements. To evaluate how well the prototype design fit both task and user, nurses were asked to carry out specific tasks using the prototype in a laboratory setting, and then complete a questionnaire on ease of use, usefulness, and fit of the technology with their documentation tasks. Because the researchers engaged nurses at each step of the design process, the result was a more useful and usable system.

Future of the Human–Technology Interface Increased attention to improving the human–technology interface through human factors approaches has already led to significant improvements in one area of health care: anesthesiology. Anesthesia machines that once had hoses that would fit into any delivery port now have hoses that can only be plugged into the proper port. Anesthesiologists have also been actively working with engineers to improve the computer interface through which they monitor their patients’ status and are among the leaders in investigating the use of audio techniques as an alternative way to help anesthesiologists maintain their situational awareness. As a result of these efforts, anesthesia-related deaths dropped from 2 in 20,000 to 1 in 200,000 in less than 10 years (Vicente, 2004). It is hoped that continued emphasis on human factors (Vicente, 2004) and user-centered design (Rubin, 1994) by informatics professionals and human–computer interactions experts will have equally successful effects on other parts of the healthcare system. The increased amount of informatics research in this area is encouraging, but there is a long way to go.

A systematic review of clinical technology design evaluation studies (Alexander & Staggers, 2009) found 50 nursing studies. Of those, nearly half (24) evaluated effectiveness, fewer (16) evaluated satisfaction, and still fewer (10) evaluated efficiency. The evaluations were not systematic. That is, there was no attempt to evaluate the same system in different environments or with different users. Most evaluations were done in a laboratory, rather than in the setting where the system would be used. The authors argued for a broader range of studies that use an expanded set of outcome measures. For example, instead of looking at user satisfaction, evaluators

should dig deeper into the design factors that led to the satisfaction or dissatisfaction. In addition, performance measures, such as diagnostic accuracy, errors, and correct treatment, should be used.

Rackspace, Brauer, and Barth (2013) reported on a social study of the human cloud formed in part by data collected from wearable technologies; they focused on assessing attitudes and “exploring how cloud computing is enabling this new generation of smart devices” (p. 2). Today, smartphones, glasses, clothing, watches, cameras, and monitors for health or patient tracking, to name but a few devices, are available to this purpose.

As our technologies continue to evolve, we are creating more design issues. The proliferation of smart devices and wearable technology brings new concerns related to human–technology interfaces. According to Madden (2013), wearable technologies are adding another wrinkle into the design process—namely, human behavior. How will someone use this technology? How will individuals behave with it on their person? How will they wear it? How and when will they enable and use it? Will others be able to detect the technologies? That is, will someone be able to wear Google Glass and take pictures or videos of other people’s actions easily and seamlessly move among all of the capabilities of his or her wearable technologies? The human–technology interface must address these issues. There is a long way to go.

Summary There are at least three messages the reader should take away from the discussion in this chapter. First, if there is to be significant improvement in quality and safety outcomes in the United States through the use of information technology, the designs for human– technology interfaces must be radically improved so that the technology better fits human and task requirements. However, that improvement will be possible only if clinicians identify and report problems, rather than simply creating workarounds. That means that each clinician has a responsibility to participate in the design process and to report designs that do not work.

Second, a number of useful tools are currently available for the analysis, design, and evaluation phases of development life cycles. They should be used routinely by informatics professionals to ensure that technology better fits both task and user requirements.

Third, focusing on interface improvement using these tools has had a huge impact on patient safety in the area of anesthesiology and medication administration. With increased attention from informatics professionals and engineers, the same kind of improvement should be possible in other areas regardless of the technologies actually employed there. In the ideal world, one can envision that every human–technology interface will be designed to enhance users’ workflow, will be as easy to use as banking ATMs, and will be fully tested before its implementation in a setting that mirrors the setting where it will be used.

THOUGHT-PROVOKING QUESTIONS

1. You are a member of a team that has been asked to evaluate a prototype personal digital assistant–based application for calculating drug dosages. Based on what you know about usability testing, which kind of test (or tests) might you do and why?

2. Is there a human–technology interface that you have encountered that you think needs improvement? If you were to design a replacement, which analysis techniques would you choose? Why?

3. Which type of functionality and interoperability would you want from your smartphone, watch, clothing, glasses, camera, and monitor? Provide a detailed response.

REFERENCES Aarts, J., Doorewaard, H., & Berg, M. (2004). Understanding implementation: The case of a computerized physician order entry system in a large Dutch

university medical center. Journal of the American Medical Association, 11, 207–216. Agency for Healthcare Research and Quality (AHRQ). (2010). Improving data collection across the health care system.

http://www.ahrq.gov/research/findings/final-reports/iomracereport/reldata5.html Agutter, J., Drews, F., Syroid, N., Westneskow, D., Albert, R., Strayer, … Weinger, M. (2003). Evaluation of graphic cardiovascular display in a high-

fidelity simulator. Anesthesia and Analgesia, 97, 1403–1413. Alexander, G., & Staggers, N. (2009). A systematic review of the designs of clinical technology findings and recommendations for future research.

Advances in Nursing Science, 32(3), 252–279. Ammenwerth, E., Iller, C., & Mahler, C. (2006). IT-adoption and the interaction of task, technology and individuals: A fit framework and a case study.

BMC Medical Informatics and Decision Making, 6, 3. Ash, J. S., Berg, M., & Coiera, E. (2004). Some unintended consequences of information technology in health care: The nature of patient care

information system-related errors. Journal of the American Medical Informatics Association, 11, 104–112. Blike, G. T., Surgenor, S. D., Whallen, K., & Jensen, J. (2000). Specific elements of a new hemodynamics display improves the performance of

anesthesiologists. Journal of Clinical Monitoring & Computing, 16, 485–491. Burns, C. M., & Hajdukiewicz, J. R. (2004). Ecological interface design. Boca Raton, FL: CRC Press. Chernecky, C., Macklin, D., & Waller, J. (2006). Internet design preferences of patients with cancer. Oncology Nursing Forum, 33, 787–792. Cole, W. G., & Stewart, J. G. (1993). Metaphor graphics to support integrated decision making with respiratory data. International Journal of Clinical

Monitoring and Computing, 10, 91–100. Connolly, C. (2005, March 21). Cedars-Sinai doctors cling to pen and paper. Washington Post, p. A01. Cooper, A. (1995). About face: Essentials of window interface design. New York, NY: Hungry Minds, Inc. Davis, F. D. (1993). User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. International Journal

of Man–Machine Studies, 38, 475–487. DeLone, W. H., & McLean, E. (1992). Information systems success: The question for the dependent variable. Information Systems Research, 3(1), 60–

95. Doyle, M. (2005). Impact of the Bar Code Medication Administration (BCMA) system on medication administration errors. Unpublished doctoral

dissertation, University of Arizona, Tucson, AZ. Dykes, P. C., Benoit, A., Chang, F., Gallagher, J., Li, Q., Spurr, C., … Prater, M. (2006). The feasibility of digital pen and paper technology for vital

sign data capture in acute care settings. In AMIA 2006 symposium proceedings (pp. 229–233). Washington, DC: American Medical Informatics

Association. Effken, J. A. (2002). Different lenses, improved outcomes: A new approach to the analysis and design of healthcare information systems. International

Journal of Medical Informatics, 65, 59–74. Effken, J. A. (2006). Improving clinical decision making through ecological interfaces. Ecological Psychology, 18(4), 283–318. Effken, J., Loeb, R., Johnson, K., Johnson, S., & Reyna, V. (2001). Using cognitive work analysis to design clinical displays. In V. L. Patel, R. Rogers,

& R. Haux (Eds.), Proceedings of MedInfo-2001 (pp. 27–31). London, UK: IOS Press. Gaver, W. W. (1993). What in the world do we hear? An ecological approach to auditory event perception. Ecological Psychology, 5, 1–30. Goodhue, D. L., & Thompson, R. L. (1995). Task–technology fit and individual performance. MIS Quarterly, 19(2), 213–236. Gurushanthaiah, K. I., Weinger, M. B., & Englund, C. E. (1995). Visual display format affects the ability of anesthesiologists to detect acute physiologic

changes: A laboratory study employing a clinical display simulator. Anesthesiology, 83, 1184–1193. Hajdukiewicz, J. R., Vicente, K. J., Doyle, D. J., Milgram, P., & Burns, C. M. (2001). Modeling a medical environment: An ontology for integrated

medical informatics design. International Journal of Medical Informatics, 62, 79–99. Han, Y. Y., Carcillo, J. A., Venkataraman, S. T., Clark, R. S. B., Watson, R. S., Nguyen, T., … Orr, R. (2005). Unexpected increased mortality after

implementation of a commercially sold computerized physician order entry system. Pediatrics, 116, 1506–1512. Horn, W., Popow, C., & Unterasinger, L. (2001). Support for fast comprehension of ICU data: Visualization using metaphor graphics. Methods in

Informatics Medicine, 40, 421–424. Hyun, S., Johnson, S. B., Stetson, P. D., & Bakken, S. (2009). Development and evaluation of nursing user interface screens using multiple methods.

Journal of Biomedical Informatics, 42(6), 1004–1012. Institute of Medicine. (1999). To err is human: Building a safer health system. Washington, DC: Author. Intel IT Center. (2013). Big data visualization: Turning big data into big insights.

http://www.intel.com/content/dam/www/public/us/en/documents/white-papers/big-data-visualizationturning-big-data-into-big-insights.pdf Johnson, C. M., & Turley, J. P. (2006). The significance of cognitive modeling in building healthcare interfaces. International Journal of Medical

Informatics, 75(2), 163–172. Johnson, C. W. (2006). Why did that happen? Exploring the proliferation of barely usable software in healthcare systems. Quality & Safety in

Healthcare, 15(suppl 1), 176–181. Kirlik, A., & Maruyama, S. (2004). Human–technology interaction and music perception and performance: Toward the robust design of sociotechnical

systems. Proceedings of the IEEE, 92(4), 616–631. Koppel, R., Metlay, J. P., Cohen, A., Abaluck, B., Localio, A. R., Kimmel, S. E., & Stron, B. (2005). Role of computerized physician order entry

systems in facilitating medication errors. Journal of the American Medical Association, 293(10), 1197–1203. Kushniruk, A. W., & Borycki, E. M. (2006). Low-cost rapid usability engineering: Designing and customizing usable healthcare information systems.

Healthcare Quarterly (Toronto, Ont.), 9(40), 98–100, 102. Loeb, R. G., & Fitch, W. T. (2002). A laboratory evaluation of an auditory display designed to enhance intraoperative monitoring. Anesthesia and

Analgesia, 94, 362–368. Madden, S. (2013). With wearable tech like Google Glass, human behavior is now a design problem. http://gigaom.com/2013/06/15/with-wearable-tech-

like-google-glass-humanbehavior-is-now-a-design-problem/ Mandel, T. (1997). The elements of user interface design. New York, NY: John Wiley & Sons. McDaniel, A., Hutchinson, S., Casper, G. R., Ford, R. T., Stratton, R., & Rembush, M. (2002). Usability testing and outcomes of an interactive computer

program to promote smoking cessation in low income women. In Proceedings AMIA 2002 (pp. 509–513). Washington, DC: American Medical Informatics Association.

Michels, P., Gravenstein, D., & Westenskow, D. R. (1997). An integrated graphic data display improves detection and identification of critical events during anesthesia. Journal of Clinical Monitoring & Computing, 13, 249–259.

Nielsen, J.(1994). Heuristic evaluation. In J. Nielsen & R. L. Mack (Eds.), Usability inspection methods (pp. 25–62). New York, NY: John Wiley & Sons.

Norman, D. A. (1988). The psychology of everyday things. New York, NY: Basic Books. Quinn, M. (2000). The climate symphony: Rhythmic techniques applied to the sonification of ice core data. http://www.bcca.org/ief/dquin00c.htm Quinn, M., & Meeker, L. (2000). Research set to music: The climate symphony and other sonifications of ice core, radar, DNA, seismic and solar wind

data. http://www.drsrl.com/climate_paper.html Rackspace, Brauer, C. & Barth, J. (2013). The human cloud: Wearable technology from novelty to productivity. A social study into the impact of

wearable technology. http://sloanreview.mit.edu/article/managing-the-human-cloud/ Rubin, J. (1994). Handbook of usability testing: How to plan, design, and conduct effective tests. New York, NY: Wiley & Sons. Sanderson, P. (2006). The multimodal world of medical monitoring displays. Applied Ergonomics, 37, 501–512. Staggers, N. (2003). Human factors: Imperative concepts for information systems in critical care. AACN Clinical Issues, 14(3), 310–319. Staggers, N., Kobus, D., & Brown, C. (2007). Nurses’ evaluations of a novel design for an electronic medication administration record. CIN: Computers,

Informatics, Nursing, 25(2), 67–75. Thompson, D. A., Lozano, P., & Christakis, D. A. (2007). Parent use of touchscreen computer kiosks for child health promotion in community settings.

Pediatrics, 119(3), 427–434. U.S. Department of Health and Human Services. (2013). Usability.gov: Task analysis. http://www.usability.gov/how-to-and-tools/methods/task-

analysis.html Vicente, K. J. (1999). Cognitive work analysis: Toward safe, productive, and healthy computer-based work. Mahwah, NJ: Lawrence Erlbaum

Associates. Vicente, K. (2004). The human factor. New York, NY: Routledge. Vincent, D., Hastings-Tolsma, M., & Effken, J. (2010). Data visualization and large nursing datasets. Online Journal of Nursing Informatics, 14(2).

http://ojni.org/14_2/Vincent.pdf Walsh, T., & Beatty, P. C. W. (2002). Human factor error and patient monitoring. Physiological Measurement, 23, R111–R132. Watson, G., & Gill, T. (2004). Earcon for intermittent information in monitoring environments. In Proceedings of the 2004 Conference of the

Computer–Human Interaction Special Interest Group of the Human Factors and Ergonomics Society of Australia (OzCHI2004), Wollonggong, New South Wales, November 22–24, 1994.

Wharton, C., Rieman, J., Lewis, C., & Polson, P. (1994). The cognitive walkthrough: A practitioner’s guide. In J. Nielsen & R. L. Mack (Eds.), Usability inspection methods (pp. 105–139). New York, NY: John Wiley & Sons.

Zhang, Y., Drews, F. A., Westenskow, D. R., Foresti, S., Agutter, J., Burmedez, J. C., … Loeb, R. (2002). Effects of integrated graphical displays on situation awareness in anaesthesiology. Cognition, Technology & Work, 4, 82–90.

Chapter 13

Electronic Security Lisa Reeves Bertin, Dee McGonigle, and Kathleen Mastrian

OBJECTIVES

1. Describe processes for securing electronic information in a computer network. 2. Identify various methods of user authentication and relate authentication to security of a network. 3. Explain methods to anticipate and prevent typical threats to network security.

Key Terms

Antivirus software Authentication Biometrics Confidentiality Firewall Flash drive Hacker Integrity Intrusion detection devices Intrusion detection system Jump drive Malicious code Malicious insider Malware Mask Network Network accessibility Network availability Network security Password Proxy server Radio frequency identity chip Secure information Security breach Shoulder surfing Social engineering Spyware Thumb drive Trojan horse Virus Worm

Introduction In addition to complying with federal HIPAA guidelines regarding the privacy of patient information, healthcare systems need to be vigilant in the way that they secure information and manage network security. Mowry and Oakes (n.d.) discuss the vulnerability of electronic health records to data breaches. They suggest that as many as 77 persons could view a patient’s record during a hospital stay. It is critical for information technology (IT) policies and procedures to ensure appropriate access by clinicians and to protect private information from inappropriate access. However, authentication procedures can be cumbersome and time consuming, thus reducing clinician performance efficiency.

Physicians spend on average 7 minutes per patient encounter, with nearly 2 minutes of that time being devoted to managing logins and application navigation. Likewise, an average major healthcare provider must deal with more than 150 applications—most requiring different user names and passwords—making it difficult for caregivers to navigate and receive contextual information. Healthcare organizations must strike the right balance, in terms of simplifying access to core clinical data sets while maximizing the

time providers can interact with patients without jeopardizing data integrity and security (para. 7). This chapter explores use of information and processes for securing information in a health system computer network.

Securing Network Information Typically, a healthcare organization has computers linked together to facilitate communication and operations within and outside the facility. This is commonly referred to as a network. The linking of computers together and to the outside world creates the possibility of a breach of network security and exposes the information to unauthorized use. With the advent of smart devices, managing all of these risks has become a nightmare for some institutions’ security processes. In the past, stationary devices or computers resided within healthcare facilities. Today, smart devices walk in and out of healthcare organizations with patients, family members, and other visitors, as well as employees—both staff and healthcare providers alike. According to Sullivan (2012), “Even as they promise better health and easier care delivery, wireless medical devices (MDs) carry significant security risks. And the situation is only getting trickier as more and more MDs come with commercial operating systems that are both Internet-connected and susceptible to attack” (para. 1).

The three main areas of secure network information are (1) confidentiality, (2) availability, and (3) integrity. As discussed in the Ethical Applications of Informatics and Legislative Aspects of Nursing Informatics: HITECH and HIPAA chapters, an organization must follow a well-defined policy to ensure that private health information remains appropriately confidential. The confidentiality policy should clearly define which data are confidential and how those data should be handled. Employees also need to understand the procedures for releasing confidential information outside the organization or to others within the organization and know which procedures to follow if confidential information is accidentally or intentionally released without authorization. In addition, the organization’s confidentiality policy should contain consideration for elements as basic as the placement of monitors so that information cannot be read by passersby. Shoulder surfing, or watching over someone’s back as that person is working, is still a major way that confidentiality is compromised.

Availability refers to network information being accessible when needed. This area of the policy tends to be much more technical in nature. An accessibility policy covers issues associated with protecting the key hardware elements of the computer network and the procedures to follow in case of a major electric outage or Internet outage. Food and drinks spilled onto keyboards of computer units, dropping or jarring hardware, and electrical surges or static charges are all examples of ways that the hardware elements of a computer network may be damaged. In the case of an electrical outage or a weather-related disaster, the network administrator must have clear plans for data backup, storage, and retrieval. There must also be clear procedures and alternative methods of ensuring that care delivery remains largely uninterrupted.

Another way organizations protect the availability of their networks is to institute an acceptable use policy. Elements covered in such policy could include which types of activities are acceptable on the corporate network. For example, are employees permitted to download music at work? Restricting downloads is a very common way for organizations to prevent viruses and other malicious code from entering their networks. The policy should also clearly define which activities are not acceptable and identify the consequences for violations.

The last area of information security is integrity. Employees need to have confidence that the information they are reading is true. To accomplish this, organizations need clear policies to clarify how data are actually input, determine who has the authorization to change such data, and track how and when data are changed. All three of these areas use authorization and authentication to enforce the corporate policies. Access to networks can easily be grouped into areas of authorization (e.g., users can be grouped by job title). For example, anyone with the job title of “floor supervisor” might be authorized to change the hours worked by an employee, whereas an employee with the title of “patient care assistant” cannot not make such changes.

Authentication of Users Authentication of employees is also used by organizations in their security policies. The most common ways to authenticate rely on something the user knows, something the user has, or something the user is (Figure 13-1).

Figure 13-1 Ways to authenticate users: A. an ID badge, B. examples of weak and strong passwords, C. a finger on a biometric scanner Sources: Part A: © Photos.com. Part C: © Gary James Calder/ShutterStock, Inc.

Something a user knows is a password. Most organizations today enforce a strong password policy, because free software available on the Internet can break a password from the dictionary very quickly. Strong password policies include using combinations of letters, numbers, and special characters, such as plus signs and ampersands. Policies typically include the enforcement of changing passwords every 30 or 60 days. Passwords should never be written down in an obvious place, such as a sticky note attached to the monitor or under the keyboard.

The second area of authentication is something the user has, such as an identification (ID) card. ID cards can be magnetic, similar to a credit card, or have a radio frequency identity chip embedded into the card.

The last area of authentication is biometrics. Devices that recognize thumb prints, retina patterns, or facial patterns are available. Depending on the level of security needed, organizations commonly use a combination of these types of authentication.

Threats to Security The largest benefit of a computer network is the ability to share information. However, organizations need to protect that information and ensure that only authorized individuals have access to the network and the data appropriate to their role. A 2003 nationwide survey by the Computing Technology Industry Association found that human error was the most likely cause of problems with security breaches. The survey indicated that only 8% of such breaches were caused by purely technical errors, whereas more than 63% were caused by some type of human error (Gross, 2003). According to Degaspari (2010), “Given the volume of electronic patient data involved, it’s perhaps not surprising that breaches are occurring. According to the Department of Health and Human Services’ Office of Civil Rights (OCR), 146 data breaches affecting 500 or more individuals occurred between December 22, 2009, and July 28, 2010. The types of breaches encompass theft, loss, hacking, and improper disposal; and include both electronic data and paper records” (para. 4). How, then, should organizations approach security, knowing that human beings are the most likely cause of a security breach?

The first line of defense is strictly physical. The power of a locked door, an operating system that locks down after 5 minutes of inactivity, and regular security training programs are extremely effective in this regard. Proper workspace security discipline is a critical aspect of maintaining security. Employees need to be properly trained to be aware of computer monitor visibility, shoulder surfing, and policy regarding the removal of computer hardware. A major issue facing organizations is removable storage devices (Figure 13-2). CD/DVD burners, jump drives, flash drives, and thumb drives (which use USB port access) are all potential security risks. These devices can be slipped into a pocket and, therefore, are easily removed from the organization. One way to address this physical security risk is to limit the authorization to write files to a device. Organizations are also turning off the CD/DVD burners and USB ports on company desktops.

The most common threats a corporate network faces are hackers, malicious code (spyware, viruses, worms, Trojan horses), and malicious insiders. Acceptable use policies help to address these problems. For example, employees may be restricted from downloading files from the Internet. Downloaded files, including e-mail attachments, are the most common way viruses and other malicious codes enter a computer network. Network security policies typically prohibit employees from using personal CDs/DVDs and USB drives, thereby preventing the transfer of malicious code from a personal computer to the network.

Figure 13-2 A removable storage device Sources: © Alex Kotlov/ShutterStock, Inc.

Spyware is normally controlled by limiting functions of the browser used to surf the Internet. For example, the browser privacy options can control how cookies are used. A cookie is a very small file written to the hard drive of a computer whose user is surfing the Internet. This file contains information about the user. For example, many shopping sites write cookies to the user’s hard drive containing the user’s name and preferences. When that user returns to the site, the site will greet her by name and list products in which she is possibly interested. Weather websites send cookies to users’ hard drives with their ZIP code so that when each user returns to that site, the local weather forecast is immediately displayed. On the negative side, cookies can follow the user’s travels on the Internet. Marketing companies use spying cookies to track popular websites that could provide a return on advertising expenditures. Spying cookies related to marketing typically do not track keystrokes in an attempt to steal user IDs and passwords.

Instead, they simply track which websites are popular and are used to develop advertising and marketing strategies. Spyware that does steal user IDs and passwords contains malicious code that is normally hidden in a seemingly innocent file download. This threat to security explains why healthcare organizations typically do not allow employees to download files. The rule of thumb to protect the network and one’s own computer system is to download only files from a reputable site that provides complete contact information. Organizations may also use such devices as firewalls (covered in the next section) and intrusion detection devices to protect from hackers.

Another huge threat to corporate security is social engineering, or the manipulation of a relationship based on one’s position in an organization. For example, someone attempting to access a network might pretend to be an employee from the corporate IT office, who simply asks for an employee’s digital ID and password. The outsider can then gain access to the corporate network. Once this access has been obtained, all corporate information is at risk. A second example of social engineering is a hacker impersonating a federal government agent. After talking an employee into revealing network information, the hacker has an open door to enter the corporate network.

The number one security threat to a corporate network is the malicious insider. This person can be a disgruntled employee or a recently fired employee whose rights of access to the corporate network have not yet been removed. In the case of a recently fired employee, his or her network access should be suspended immediately upon notice of termination. To avoid the potentially hazardous issues created by malicious insiders, healthcare organizations need some type of policy to monitor employee activity to ensure that employees carry out only those duties that are part of their normal job. Separation of privileges is a common security tool; no one employee should be able to complete a task that could cause a critical event without the knowledge of another employee. For example, the employee who processes the checks and prints them should not be the same person who signs those checks. Similarly, the employee who alters pay rates and hours worked should be required to submit a weekly report to a supervisor before the changes take effect. Software that can track and monitor employee activity is also available. This software can log which files an employee accesses, whether changes were made to files, and whether the files were copied. Depending on the number of employees, organizations may also employ a full-time electronic auditor who does nothing but monitor activity logs.

Security Tools A wide range of tools are available to an organization to protect the organizational network and information. These tools can be either a software solution, such as antivirus software, or a hardware tool, such as a proxy server. Such tools are effective only if they are used along with employee awareness training.

For example, e-mail scanning is a commonly used software tool. All incoming e-mail messages are scanned to ensure they do not contain a virus or some other malicious code. This software can find only viruses that are currently known, so it is important that the virus software be set to search for and download updates automatically. Organizations can further protect themselves by training employees to never open an e-mail attachment unless they are expecting the attachment and know the sender. Even IT managers have fallen victim to e-mail viruses and sent infected e-mails to everyone in their address book. Employees should be taught to protect their organization from new viruses that may not yet be included in their scanning software by never opening an e-mail attachment unless the sender is known and the attachment is expected. E-mail scanning software and antivirus software should never be turned off, and updates should be installed at least weekly—or, ideally, daily. Software is also available to scan instant messages and to delete automatically any spam e-mail.

Many antivirus and adware software packages are available for fees ranging from free to more than $25 per month. The main factors to consider when purchasing antivirus software are its effectiveness (i.e., the number of viruses it has missed), the ease of installation and use, the effectiveness of the updates, and the help and user support available. Numerous websites compare and contrast the most recent antivirus software packages. Be aware, however, that some of these sites also sell antivirus software, so they may present biased information.

Firewalls are another tool used by organizations to protect their corporate networks when they are attached to the Internet. A firewall can be either hardware or software or a combination of both that examines all incoming messages or traffic to the network. The firewall can be set up to allow only messages from known senders into the corporate network. It can also be set up to look at outgoing information from the corporate network. If the message contains some type of corporate secret, the firewall may prevent the message from leaving. In essence, firewalls serve as electronic security guards at the gate of the corporate network. Box 13-1 contains links to short videos explaining Internet security and firewalls.

Proxy servers also protect the organizational network. Proxy servers prevent users from directly accessing the Internet. Instead, users must first request passage from the proxy server. The server looks at the request and makes sure the request is from a legitimate user and that the destination of the request is permissible. For example, organizations can block requests to view a website with the word “sex” in the title or the actual uniform resource locator of a known pornography site. The proxy server can also lend the requesting user a mask to use while he or she is surfing the Web. In this way, the corporation protects the identity of its employees. The proxy server keeps track of which employees are using which masks and directs the traffic appropriately.

With hacking becoming more common, healthcare organizations must have some type of protection to avoid this invasion. Intrusion detection systems (both hardware and software) allow an organization to monitor who is using the network and which files that user has accessed. Detection systems can be set up to monitor a single computer or an entire network. Corporations must diligently monitor for unauthorized access of their networks. Anytime someone uses a secured network, a digital footprint of all of the user’s travels is left, and this path can be easily tracked by electronic auditing software. The Research Brief provides an overview of the HIMSS 2012 Security Survey results that report on progress made by institutions in securing private information.

BOX 13-1 SOURCES OF INFORMATION ON INTERNET SECURITY AND FIREWALLS Check out YouTube for the following videos on the Internet and firewalls:

Warriors on the Net (Full Version)”: http://www.youtube.com/watch?v=RhvKm0RdUY0

“What Is a firewall?”: http://www.youtube.com/watch?v=0_EVfWpL6L4 “Firewalls: An Introduction”: http://www.youtube.com/watch?v=kIAu7mvjBUU&feature=related

Research Brief

Overview of HIMSS 2012 Security Survey In the 5th Annual Security Survey, participants included 303 persons who had some responsibility for information security in their organization. Roles and titles varied among respondents, such as chief information officer (CIO), senior IT executive, chief security officer (CSO), or chief technology officer, but all reported responsibility for privacy and information security. Key survey results from the executive summary are reproduced here: Maturity of Environment: Respondents characterized their environment as being at a middle rate of maturity, with an average score of 4.64 on a

scale of 1 to 7, where 1 is “not at all mature” and 7 is “highly mature.” Security Budget: Approximately half of respondents reported that their organization spends 3% or less of its IT budget on information security.

However, while this was consistent with what was reported in the previous year, many respondents indicated that their budget actually increased in the past year.

Formal Security Position: Nearly 2/3 of the hospital-based respondents reported that they had either a chief security officer (CSO), chief information security officer (CSIO) or other full-time person in charge of the security of patient data. Less than 1% of respondents reported that this function was outsourced.

Employee/Patient Data Access: Healthcare organizations are increasingly making patient data available to patients, surrogates, and designated others.

Security in a Networked Environment: Respondents working for hospitals were much more likely than their counterparts at physician practices to report that they shared information with other healthcare organizations. Future use of health information exchanges was expected to grow among all survey respondents.

Use of Security Technologies: While use of tools like firewalls and user access controls has remained widespread in the past several years, growth of tools such as biometric technologies and public key infrastructure has remained limited. Growth of electronic signature tools has been substantial in the past five years.

Medical Identity Theft: The number of organizations reporting a case of medical identity theft in the past five years has decreased, from 20 percent in 2008 to 11 percent in 2012. Hospitals (18 percent) were more likely to report an instance of medical identity theft than were physician practices (six percent).

Results continue to demonstrate that physician practices are not as advanced in many of the areas for security data, when compared to hospitals. Respondents working for physician practices are less likely to have formal processes in place to monitor their security environment. This includes performing regular risk analyses, validating and testing their data breach response plan and auditing their IT security plan. Respondents working for physician practices were also less likely than their peers to report the use of a full complement of data security tools such as wireless security protocols and single signon (SSO). Despite this, physician practices were less likely than their hospital counterparts to experience cases of medical identity theft or security breaches. Fewer incidents may be attributed to the fact that physician practices typically manage fewer patients than do hospitals and also have fewer staff, which has been identified as a key threat to secure patient information (HIMSS, 2012, p. 23).

Source: Permission for use of the data is granted in the publication. Individuals are encouraged to cite this report and any accompanying graphics in printed matter, publications, or any other medium, as long as the information is attributed to the 5th Annual HIMSS Security Survey, sponsored by Experian.

Off-Site Use of Portable Devices Off-site uses of portable devices, such as laptops, personal data assistants (PDAs), home computing systems, smartphones, smart devices, and portable data storage devices, can help to streamline the delivery of health care. For example, home health nurses may need to access electronic protected health information (EPHI) via a wireless laptop connection during a home visit, or a physician might use a PDA to get specific patient information related to a prescription refill in response to a patient request. These mobile devices are invaluable to healthcare efficiency and responsiveness to patient need in such cases. At the very least, however, agencies should require data encryption when EPHI is being transmitted over unsecured networks or transported on a mobile device as a way of protecting sensitive information. Hotspots provided by companies, such as McDonald’s restaurants, and by airports are not secured networks. Virtual private networks must be used to ensure that all data transmitted on unsecured networks are encrypted. The user must log into the virtual private network to reach the organization’s network.

Only data essential for the job should be maintained on the mobile device; other nonclinical information, such as Social Security numbers, should never be carried outside the secure network. Some institutions make use of thin clients, which are basic interface portals that do not keep secure information stored on them. Essentially, users must log in to the network to get the data they need. Use of thin clients may be problematic in patient care situations where the user cannot access the network easily. For example, some rural areas of the United States do not have wireless coverage. In these instances, private health information may need to be stored in a clinician’s laptop or PDA. This is comparable to home health nurses carrying paper charts in their cars to make home visits, and it entails the same responsibilities accompanying such use of private information outside the institution’s walls.

What happens if one of these devices is lost or stolen? The agency is ultimately responsible for the integrity of the data contained on these devices and is required by HIPAA regulations (U.S. Department of Health and Human Services, 2006) to have policies in place covering such items as appropriate remote use, removal of devices from their usual physical location, and protection of these devices from loss or theft. Simple rules, such as covering laptops left in a car and locking car doors during transport of mobile devices containing EPHI, can help to deter theft. If a device is lost or stolen, the agency must have clear procedures in place to help ensure that sensitive data are not released or used inappropriately. Software packages that provide for physical tracking of the static and mobile computer inventory including laptops and PDAs are being used more widely and can assist in the recovery of lost or stolen devices. In addition, some software that allows for remote data deletion in the event of theft or loss of a mobile device can be invaluable to the agency in preventing the release of EPHI.

If a member of an agency is caught accessing EPHI inappropriately or steals a mobile device, the sanctions should be swift and public. Sanctions may range from a warning or suspension with retraining to termination or prosecution depending on the severity of

the security breach. The sanctions must send a clear message to all that protecting EPHI is serious business. The U.S. Department of Health and Human Services (2006) has identified potential risks and proposed risk management strategies

for accessing, storing, and transmitting EPHI. Visit the following website for detailed tabular information (pp. 4–6) on potential risks and risk management strategies: http://its.syr.edu/infosec/docs/standards/remoteaccess-standard.pdf

To protect our patients and their data, nurses must consider the impact of wireless mobile devices. Data can be stolen by an employee very easily through the use of e-mail or file transfers. Malware that infiltrates a network can collect easily accessible data. One of the evolving issues is lost or stolen devices that can provide a gateway into a healthcare organization’s network and records. When the device is owned by the employee, other issues arise as to how the device is used and secured.

The increase in cloud computing has also challenged our personal and professional security and privacy. According to Jansen and Grance (2011), cloud computing “promises to have farreaching effects on the systems and networks of federal agencies and other organizations. Many of the features that make cloud computing attractive, however, can also be at odds with traditional security models and controls” (p. vi).

Summary Technology changes so quickly that even the most diligent user will likely encounter a situation that could constitute a threat to his or her network. Organizations must provide their users with the proper training to help them avoid known threats and—more importantly —be able to discern a possible new threat. Consider that 10 years ago wireless networks were the exception to the rule, where today access to wireless networks is almost taken for granted. How will computer networks be accessed 10 years from now? The most important concept to remember from this chapter is that the only completely safe network is one that is turned off. Network accessibility and network availability are necessary evils that pose security risks. The information must be available to be accessed, yet remain secured from hackers, unauthorized users, and any other potential security breaches. As the cloud expands, so do the concerns over security and privacy. In an ideal world, everyone would understand the potential threats to network security and would diligently monitor and implement tools to prevent unauthorized access of their networks, data, and information.

THOUGHT-PROVOKING QUESTIONS

1. Sue is a chronic obstructive pulmonary disorder clinic nurse enrolled in a master’s education program. She is interested in writing a paper on the factors that are associated with poor compliance with medical regimens and associated repeat hospitalization of chronic obstructive pulmonary disorder patients. She downloads patient information from the clinic database to a thumb drive that she later accesses on her home computer. Sue understands rules about privacy of information and believes that because she is a nurse and needs this information for a graduate school assignment, she is entitled to the information. Is Sue correct in her thinking? Describe why she is or is not correct.

2. The nursing education department of a large hospital system has been centralized; as a consequence, the nurse educators are no longer assigned to one hospital but must now travel among all of the hospitals. They use their smartphones to interact and share data and information. What are the first steps you would take to secure these transactions? Describe why each step is necessary.

3. Research cloud computing in relation to health care. What are the major security and privacy challenges? Please choose three and describe them in detail.

References Degaspari, J. (2010). Staying ahead of the curve on data security. Healthcare Informatics, 27(10), 32–36. http://www.healthcare-

informatics.com/ME2/dirmod.asp? sid=9B6FFC446FF7486981EA3C0C3CCE4943&nm=Articles%2FNews&type=Publishing&mod=Publications%3A%3AArticle&mid=8F3A7027421841978F18BE895F87F791&tier=4&id=35F1496AE0B144D3A9716D5D9C2D03CF Gross, G. (2003). Human error causes most security breaches. InfoWorld.

http://www.infoworld.com/article/03/03/18/HNhumanerror_1.html HIMSS. (2012). 5th annual HIMSS security survey. http://himss.files.cms-

plus.com/HIMSSorg/content/files/2012_HIMSS_SecuritySurvey.pdf Jansen, W., & Grance, T. (2011). National Institute of Standards and Technology (NIST): Guidelines on security and privacy in

public cloud computing. https://cloudsecurityalliance.org/wp-content/uploads/2011/07/NIST-Draft-SP-800-144_cloud-computing.pdf Mowry, M., & Oakes, R. (n.d.). Not too tight, not too loose. Healthcare Informatics, Healthcare IT Leadership, Vision & Strategy.

http://www.healthcare-informatics.com/ME2/dirmod.asp? nm=&type=Publishing&mod=Publications%3A%3AArticle&mid=8F3A7027421841978F18BE895F87F791&tier=4&id=B7823E299AC64041AC3F253CE19DF298

Sullivan, T. (2012). Government health IT: DHS lists top 5 mobile medical device security risks. http://www.govhealthit.com/news/dhs-lists-top-5-mobile-device-security-risks

U.S. Department of Health and Human Services. (2006). HIPAA security guidance. Retrieved from Security Guidance for Remote Use website: http://its.syr.edu/infosec/docs/standards/remoteaccess-standard.pdf

Chapter 14

Nursing Informatics: Improving Workflow and Meaningful Use Denise Hammel-Jones, Dee McGonigle, and Kathleen Mastrian

OBJECTIVES

1. Provide an overview of the purpose of conducting workflow analysis and design. 2. Deliver specific instructions on workflow analysis and redesign techniques. 3. Cite measures of efficiency and effectiveness that can be applied to redesign efforts. 4. Explore meaningful use from the nursing perspective.

Key Terms

American Recovery and Reinvestment Act (ARRA) Bar-code medication administration (BCMA) Clinical transformation Computerized provider order entry (CPOE) Electronic health record (EHR) Events Health information exchange (HIE) Health information technology (HIT) Information systems Interactions Lean Meaningful use (MU) Medical home models Metrics Process analysis Process owners Quality Six Sigma Tasks Work process Workflow Workflow analysis

Introduction The healthcare environment has grown more complex and continues to evolve every day. Unfortunately, the complexities that help clinicians to deliver better care and improve patient outcomes also take a toll on the clinicians themselves. This toll is exemplified through hours spent learning new technology, loss in productivity as the user adjusts and adapts to new technology, and unintended workflow consequences from the use of technology.

Despite the perceived negative downstream effects to end users and patients as a result of technology, this very same technology can improve efficiency and yield a leaner healthcare environment. The intent of this chapter is to outline the driving forces that create the need to redesign workflow as well as to elucidate what the nurse needs to know about how to conduct workflow redesign, measure the impact of workflow changes, and assess the impact of meaningful use.

Workflow Analysis Purpose According to the American Association for Justice (2013), “Preventable medical errors kill and seriously injure hundreds of thousands of Americans every year” (para. 1). Not only is there an impact on patients from these errors, but there is also a significant financial impact on healthcare organizations. Clearly, we must minimize these errors, and one of the most important tools for this purpose is the use of electronic records and information systems to provide point-of-care decision support and automation. The key point is that many of these errors are preventable and we must find ways to prevent them.

Technology can provide a mechanism to improve care delivery and create a safer patient environment, provided it is implemented appropriately and considers the surrounding workflow. In an important article by Campbell, Guappone, Sittig, Dykstra, and Ash (2009), the authors suggested that technology implemented without consideration of workflow can provide greater patient safety concerns than no technology at all. Computerized provider order entry (CPOE) causes us to focus more specifically on workflow considerations. These workflow implications are referred to as the unintended consequences of CPOE implementation; they are just some of the effects of poorly implemented technology. The Healthcare Information Management Systems Society (HIMSS, 2010) ME-PI Toolkit addresses workflow redesign and considers why it is so critical to successful technology implementations.

Technology is recognized to have a potentially positive effect on patient outcomes. Nevertheless, even with the promise of improving how care is delivered, adoption of technology has been slow. The cost of technology solutions such as CPOE, bar-code medication administration (BCMA), and electronic health records (EHR) remain staggeringly high. The cost of technology coupled with the lengthy timelines required to develop and implement such technology has put this endeavor out of reach for many healthcare organizations. In addition, upgrades or enhancements to the technology are often necessary either mid-implementation or shortly after a launch, leaving little time to focus efforts on the optimization of the technology within the current workflow. Furthermore, the existence of technology does not in itself guarantee that it will be used in a manner that promotes better outcomes for patients.

Given the sluggish adoption of technology, in 2009 the U.S. government took an unprecedented step when it formally recognized the importance of health information technology (HIT) for patient care outcomes. As a result of the provisions of American Recovery and Reinvestment Act (ARRA), healthcare organizations can qualify for financial incentives based on the level of meaningful use achieved. Meaningful use (MU) refers to the rules and regulations established by the ARRA. The three stages of meaningful use are part of an EHR incentive program. During stage 1, the focus is on data capturing and sharing (Centers for Medicare & Medicaid Services [CMS], 2013a, 2013b). Stage 2 focuses on advanced clinical processes, and stage 3 seeks improved outcomes (CMS, 2013b). Stage 1 was initiated during 2011–2012, stage 2 will begin in 2014, and stage 3 will be launched in 2016 (Sherman, 2013). Follow developments related to meaningful use at this site: http://www.cms.gov/Regulations-and- Guidance/Legislation/EHRIncentivePrograms/Meaningful_Use.html.

For an organization that seeks to use the 25 meaningful use measures to qualify for the incentives, the data to support these measures must be gathered and reported on electronically—necessitating the use of technology in all patient care areas. Additionally, a fundamental aspect of meaningful use is the assurance that a significant number of healthcare providers have adopted technology. Nurses and healthcare professionals who use EHRs in their practice setting, for example, will collect higher-quality data. Many of the quality reporting measures rely on nursing documentation. Those healthcare personnel who do not use EHRs should soon see their organizations moving in that direction. Meaningful use measures will push healthcare organizations to reexamine the use of clinical technologies within their organization and approach implementations in a new way.

Not only is there a potential for patient safety and quality issues to arise from technology implementations that do not address workflow, but a financial impact to the organization is possible as well. All organizations, regardless of their industry, must operate efficiently to maintain profits and continue to provide services to their customers. For hospitals, which normally have significantly smaller profit margins than other organizations, the need to maintain efficient and effective care is essential for survival. Given that hospital profit margins are diminishing, never has there been a more crucial time to examine the costs of errors and poorly designed workflows and the financial burden they present to an organization, than now. Moreover, what are the costs to an organization that fails to address the integration of technology? This is an area where few supporting data exist to substantiate the claim that technology without workflow considerations can, in fact, impact the bottom line.

Today, many healthcare organizations are experiencing the effects of poorly implemented clinical technology solutions. These effects may be manifested in the form of redundant documentation, non–value-added steps, and additional time spent at the computer rather than in direct care delivery. A recent study by the University of Maryland indicated that nurses spend the equivalent of 1 FTE time per year at a computer instead of in providing direct patient care. Technology ought not to be implemented for the sake of automation unless it promises to deliver gains in patient outcomes and proper workflow. In fact, the cost to organizations for duplicate/redundant documentation by nursing can range from $6,500 to $13,000 per nurse, per year (Clancy, Delaney, Morrison, & Gunn, 2006).

Examining the workflow surrounding the use of technology enables better use of the technology and more efficient work. It also promotes safer patient care delivery. The need to focus on workflow and technology is attracting increasing recognition, although there remains a dearth of literature that addresses the importance of this area. As more organizations work to achieve a level of technology adoption that will enable them to receive ARRA financial incentives, we will likely see more attention paid to the area of workflow design and, therefore, a greater body of research and evidence (AHRQ, n.d., Qualis Health, 2011; Yuan, Finley, Long, Mills, & Johnson, 2013).

Workflow and Technology Workflow is a term used to describe the action or execution of a series of tasks in a prescribed sequence. Another definition of workflow is a progression of steps (tasks, events, interactions) that constitute a work process, involve two or more persons, and create or add value to the organization’s activities. In a sequential workflow, each step depends on the occurrence of the previous step; in a parallel workflow, two or more steps can occur concurrently. The term workflow is sometimes used interchangeably with process or process flows, particularly in the context of implementations. Observation and documentation of workflow to better understand what is happening in the current environment and how it can be altered is referred to as process or workflow analysis. A typical output of workflow analysis is a visual depiction of the process, called a process map. The process map ranges from simplistic to fairly complex and provides an excellent tool to identify specific steps. It also can provide a vehicle for communication and a tool upon which to build educational materials as well as policies and procedures.

One school of thought suggests that technology should be designed to meet the needs of clinical workflow (Yuan et al., 2013). This model implies that system analysts have a high degree of control over screen layout and data capture. It also implies that technology is

malleable enough to allow for the flexibility to adapt to a variety of workflow scenarios. Lessons learned from more than three decades of clinical technology implementations suggest that clinical technologies still have a long way to go on the road to maturity to allow this to be possible. The second and probably most prevalent thought process is that workflow should be adapted to the use of technology. Today, this is by far the most commonly used model given the progress of clinical technology.

A concept that has gained popularity in recent years relative to workflow redesign is clinical transformation. Clinical transformation is the complete alteration of the clinical environment and, therefore, this term should be used cautiously to describe redesign efforts. Earl, Sampler, and Sghort (1995) define transformation as “a radical change approach that produces a more responsive organization that is more capable of performing in unstable and changing environments that organizations continue to be faced with” (p. 31). Many workflow redesign efforts are focused on relatively small changes and not the widespread change that accompanies transformational activities. Moreover, clinical transformation would imply that the manner in which work is carried out and the outcomes achieved are completely different from the prior state—which is not always true when the change simply involves implementing technology. Technology can be used to launch or in conjunction with a clinical transformation initiative, although the implementation of technology alone is not perceived as transformational.

Before undertaking transformative initiatives, the following guidelines should be understood:

Leadership must take the lead and create a case for transformation. Establish a vision for the end point. Allow those persons with specific expertise to provide the details. Think about the most optimal experience for both the patient and the clinician. Do not replicate the current state. Focus on those initiatives that offer the greatest value to the organization. Recognize that small gains have no real impact on transformation.

Optimization

Most of what has been and will be discussed in this chapter is related to workflow analysis in conjunction with technology implementations. Nevertheless, not all workflow analysis and redesign occurs prior to the implementation of technology. Some analysis and redesign efforts may occur weeks, months, or even years following the implementation. When workflow analysis occurs postimplementation, it is often referred to as optimization. Optimization is the process of moving conditions past their current state and into more efficient and effective method of performing tasks. Merriam-Webster Online Dictionary (2010) considers optimization to be the act, process, or methodology of making something (as a design, system, or decision) as fully perfect, functional, or effective as possible. Some organizations will routinely engage in optimization efforts following an implementation, whereas other organizations may undertake this activity in response to clinician concerns or marked change in operational performance.

Furthermore, workflow analysis can be conducted either as a stand-alone effort or as part of an operational improvement event. When the process is addressed alone, the effort is termed process improvement. Nursing informatics professionals should always be included in these activities to represent the needs of clinicians and to serve as a liaison for technological solutions to process problems. Additionally, informaticists will likely become increasingly operationally focused and will need to transform their role accordingly to address workflow in an overall capacity as well as respective to technology. As mentioned earlier, hospitals tend to operate with smaller profit margins than other industries and these profits will likely continue to diminish, forcing organizations to work smarter, not harder—and to use technology to accomplish this goal.

If optimization efforts are undertaken, the need to revisit workflow design should not be considered a flaw in the implementation approach. Even a well-designed future-state workflow during a technology implementation must be reexamined postimplementation to ensure that what was projected about the future state remains valid and to incorporate any additional workflow elements into the process redesign.

Exploring the topic of workflow analysis with regard to clinical technology implementation will yield considerably fewer literature results than searching for other topical areas of implementation. More research is needed in the area of the financial implications of workflow inefficiencies and their impact on patient care. Time studies require an investment of resources and may be subject to patient privacy issues as well as the challenges of capturing time measurements on processes that are not exactly replicable. Another confounding factor affecting the quality and quantity of workflow research is the lack of standardized terminology for this area. A comprehensive literature search was conducted and published through the Agency for Healthcare Quality and Research (AHRQ) in 2008 as an evidence-based handbook for nurses; this literature search yielded findings indicating that a lack of standardized terminology in the area of workflow and publications on this topic have made it a difficult topic to support through research findings.

What all organizations ultimately strive for is efficient and effective delivery of patient care. The terms efficient and effective are widely known in quality areas or Six Sigma and Lean departments, but are not necessarily known or used in informatics. Effective delivery of care or workflow suggests that the process or end product is in the most desirable state. An efficient delivery of care or workflow would mean that little waste—that is, unnecessary motion, transportation, overprocessing, or defects—was incurred. Health systems such as Virginia Mason University Medical Center, among others, have experienced significant quality and cost gains from the widespread implementation of Lean development throughout their organization.

CASE STUDY In my experience consulting, I have seen several examples of organizations that engage in the printing of paper reports that replicate information that has been entered and is available with the electronic health record. These reports are often reviewed, signed, and acted on, instead of using the electronic information. Despite the knowledge that the information contained in these reports was outdated the moment the report was printed and that the very nature of using the report for workflow is an inefficient practice, this method of clinical workflow remains prevalent in many hospitals across the United States.

There is an underlying fear that drives the decisions to mold a paper-based workflow around clinical technology. There is also a lack of the

appropriate amount of integration that would otherwise allow this information to be available in an electronic form.

Workflow Analysis and Informatics Practice The American Nurses Association (ANA), in Nursing Informatics: Scope and Standards of Practice (2008), defines nine functional areas of practice for the informatics nurse specialist (INS). The functional area of analysis identifies the specific functional qualities related to workflow analysis. Particularly, Nursing Informatics: Scope and Standards of Practice indicates that the INS should develop techniques necessary to assess and improve human–computer interaction. Workflow analysis, however, is not relevant solely to analysis, but rather is part of every functional area the INS engages in. The functional areas covered by consultants, researchers, and other areas need to understand workflow and appreciate how lack of efficient workflow affects patient care.

A critical aspect of the informatics role is workflow design. Nursing informatics is uniquely positioned to engage in the analysis and redesign of processes and tasks surrounding the use of technology. The ANA cites workflow redesign as one of the fundamental skills sets that make up the discipline of this specialty. Moreover, workflow analysis should be part of every technology implementation, and the role of the informaticist within this team is to direct others in the execution of this task or to perform the task directly.

Unfortunately, many nurses find themselves in an informatics capacity without sufficient preparation for a process analysis role. One area of practice that is particularly susceptible to inadequate preparation is the ability to facilitate process analysis. Workflow analysis requires careful attention to detail and the ability to moderate group discussions, organize concepts, and generate solutions. These skills can be acquired through a formal academic informatics program or through courses that teach the discipline of Six Sigma or Lean, by example. Regardless of where these skills are acquired, it is important to understand that they are now and will continue to remain a vital aspect of the informatics role.

Some organizations have felt strongly enough about the need for workflow analysis that departments have been created to address this very need. Whether the department carries the name of clinical excellence, organizational effectiveness, or Six Sigma/Lean, it is critical to recognize the value this group can offer technology implementations and clinicians.

As we examine how workflow analysis is conducted, note that while the nursing informaticist is an essential member of the team to participate in or enable workflow analysis, a team dedicated to this effort is necessary for its success.

Building the Design Team

The workflow redesign team is an interdisciplinary team consisting of “process owners.” Process owners are those persons who directly engage in the workflow to be analyzed and redesigned. These individuals can speak about the intricacy of process, including process variations from the norm. When constructing the team, it is important to include individuals who are able to contribute information about the exact current-state workflow and offer suggestions for future-state improvement. Members of the workflow redesign team should also have the authority to make decisions about how the process should be redesigned. This authority is sometimes issued by managers, or it could come from participation of the managers directly. Such a careful blend of decision makers and “process owners” can be difficult to assemble but is critical for forming the team and enabling them for success. Often, individuals at the manager level will want to participate exclusively in the redesign process. While having management participate provides the advantage of having decision makers and management-level buy-in, these individuals may also make erroneous assumptions about how the process should be versus how the process is truly occurring. Conversely, including only process owners who do not possess the authority to make decisions can slow down the work of the team while decisions are made outside the group sessions.

Team focus needs to be addressed at the outset of the team’s assembly. Early on, the team should decide which workflow will be examined to avoid confusion or spending time unnecessarily on workflow that does not ultimately matter to the outcome. In the early stages of workflow redesign, the team should define the beginning and end of a process and a few high-level steps of the process. Avoid focusing on process steps in great detail in the beginning, as the conversation can get sidetracked or team members may get bogged down by focusing on details and not move along at a good pace. Six Sigma expert George Eckes uses the phrase “Stay as high as you can as long as you can”—a good catch phrase to remember to keep the team focused and at a high level. The pace at which any implementation team progresses ultimately affects the overall timeline of a project; therefore, focus and speed are skills that the informatics expert should develop and use throughout every initiative, but particularly when addressing workflow redesign.

The workflow redesign team will develop a detailed process map after agreement is reached on the current-state process’s beginning and end points, and a high-level map depicting the major process steps is finalized. Because workflow crosses many different care providers, it may be useful to construct the process map using a swimlane technique (Figure 14-1). A swim-lane technique uses categories such as functional workgroups and roles to visually depict groups of work and to indicate who performs the work. The resulting map shows how workflow and data transition to clinicians and can demonstrate areas of potential process and information breakdowns.

It may take several sessions of analysis to complete a process map, as details are uncovered and workarounds discussed. There is a tendency for individuals who participate in process redesign sessions to describe workflow as they believe it to be occurring, rather than not how it really is. The informatics expert and/or the process team facilitator should determine what is really happening, however, and capture that information accurately. Regardless of whether a swim-lane or simplistic process map design is used, the goal is to capture enough details to accurately portray the process as it is happening today.

Other techniques (aside from process mapping) may be used to help the team understand the workflow as it exists in the current state. The future-state workflow planning will be only as good as the reliability of the current state; thus it is crucial to undertake whatever other actions are needed to better understand what is happening in the current state. Observation, interviews, and process or waste walks are also helpful in understanding the current state.

Value Added Versus Non–Value Added

Beyond analysis of tasks, current-state mapping provides the opportunity for the process redesign team to distinguish between value- added and non–value-added activities. A value-added activity or step is one that ultimately brings the process closer to completion or changes the product or service for the better. An example of a value-added step would be placing a name tag on a specimen sample. The name tag is necessary for the laboratory personnel to identify the specimen and, therefore, its placement is an essential or value- added step in the process. Some steps in a process do not necessarily add value but are necessary for regulatory or compliance reasons. These steps are still considered necessary and need to be included in the future process. A non–value-added step, in contrast, does not alter the outcome of a process or product. Activities such as handling, moving, and holding are not considered value-added steps and should be evaluated during workflow analysis. Manipulating papers, moving through computer screens, and walking or transporting items are all considered non–value-added activities.

The five whys represent one technique to drive the team toward identifying value-added versus non–value-added steps. The process redesign facilitator will query the group about why a specific task is done or done in a particular way through a series of questions asking “why?” The goal is to uncover tasks that came about due to workarounds or for other unsubstantiated reasons. Tasks that are considered non–value added and are not necessary for the purpose of compliance or regulatory reasons should be eliminated from the future-state process. The team’s purpose in redesigning workflow is to eliminate steps in a process that do not add value to the end state or that create waste by their very nature.

Figure 14-1 Source: Greencastle Associates Consulting and Atlantic Health. Reprinted by permission.

Waste

A key underpinning of the Lean philosophy is the removal of waste activities from workflow. Waste is classified as unnecessary activities or an excess of products to perform tasks. The seven categories listed here are the mostly widely recognized forms of waste:

Overproduction: pace is faster than necessary to support the process Waiting Transport Inappropriate processing: overprocessing Unnecessary inventory: excess stock Unnecessary motion: bending, lifting, moving, and so on Defects: reproduction

Variation

The nature of the work situation for the nurse is one of frequent interruptions causing the workflow to be disrupted and increasing the chance of error (Yuan et al., 2013). Variation in workflow is considered the enemy of all good processes and, therefore, should be

eliminated when possible. Variation occurs when workers perform the same function in different ways. It usually arises because of flaws in the way a process was originally designed, lack of knowledge about the process, or inability to execute a process as originally designed due to disruption or disturbances in the workflow. Examining the process as it exists today will help with identifying variation. A brief statement about variation that cannot be eliminated: Processes that involve highly customized products or services are generally not conducive to standardization and the elimination of variation inherent to the process.

Some argue that delivery of care is subject to variation owing to its very nature and the individual needs of patients. There is little doubt that each patient’s care should be tailored to meet his or her specific needs. Nevertheless, delivery of care involves some common processes that can be standardized and improved upon without jeopardizing care.

Transitioning to the Future State

Following redesign efforts, regardless of whether they occurred during or after an implementation or as a stand-alone process improvement event, steps must be taken to ensure that change takes hold and the new workflow continues after the support team has disbanded. Management support and involvement during the transition phase is essential, as management will be necessary to enforce new workflow procedures and further define/refine roles and responsibilities. Documentation of the future-state workflow should have occurred during the redesign effort but is not completely finished until after the redesign is complete and the workflow has become operational. Policies and procedures are addressed and rewritten to encompass the changes to workflows and role assignments. Help desk, system analyst, nursing education, and other support personnel need to be educated about the workflow specifics as part of the postimprovement effort. It is considered good practice to involve the operational staff in the future process discussions and planning so as to incorporate specifics of these areas and ensure the buy-in of the staff.

When workflow changes begin to fail and workarounds develop, they signal that something is flawed about the way in which the new process was constructed and needs to be evaluated further. The workflow redesign team is then brought together to review and, if necessary, redesign the process.

The future state is constructed with the best possible knowledge of how the process will ideally work. To move from the current state to the future state, gap analysis is necessary. Gap analysis zeros in on the major areas most affected by the change—namely, technology. What often happens in redesign efforts is an exact or near-exact replication of the current state using automation. The gap analysis discussion should generate ideas from the group about how best to utilize the technology to transform practice. A prudent step is to consider having legal and risk representatives at the table when initiating future-state discussions to identify the parameters within which the group should work; nevertheless, the group should not assume the existing parameters are its only boundaries.

Future-state process maps become the basis of educational materials for end users, communication tools for the project team, and the foundation of new policies and procedures. Simplified process maps provide an excellent schematic for communicating change to others.

Informatics as a Change Agent Technology implementations represent a significant change for clinicians, as does the workflow redesign that accompanies adoption of technology. Often the degree of change and its impact are underappreciated and unaccounted for by leadership and staff alike. A typical response to change is anger, frustration, and a refusal to accept the proposed change. All of these responses should be expected and need to be accounted for; thus a plan to address the emotional side of change is developed early on. Every workflow redesign effort should begin with a change management plan. Engagement of the end user is a critical aspect of change management and, therefore, technology adoption. Without end-user involvement, change is resisted and efforts are subject to failure. Users may be engaged and brought into the prospective change through question-and-answer forums, technology demonstrations, and frequent communications regarding change, and as department-specific representatives in working meetings.

Many change theories have been developed. No matter which change theory is adopted by the informatics specialist, however, communication, planning, and support are key factors in any change management strategy. Informaticists should become knowledgeable about at least one change theory and use this knowledge as the basis for change management planning as part of every effort. John Kotter (1996), one of the most widely recognized change theorists, suggested the following conditions must be addressed to deal with change in an organization:

Education and communication Participation and involvement Facilitation and support Negotiation and agreement Manipulation and co-optation Explicit and implicit coercion

In the HIMSS (2009) Nursing Informatics Impact survey, nursing informaticists were identified as the most significant resource in a project team that influences adoption and change management. Nurses bring to such teams their ability to interact with various clinicians, their knowledge of clinical practice, and their ability to empathize with the clinicians as they experience the impact of workflow change. These innate skills differentiate the nursing informaticist from other members of the implementation team and are highly desirable in the informatics community.

Nevertheless, no matter which change management techniques are employed by the informatics specialist and the project team, adoption of technology and workflow may be slow to evolve. Change is often a slow process that requires continual positive reinforcement and involvement of supporting resources. Failure to achieve strong adoption results early on is not necessarily a failure of the methods utilized, but rather may be due to other factors not entirely within the control of the informaticist.

Perhaps a complete alteration in behavior is not possible, but modifications to behaviors needed to support a desired outcome can

be realized. This situation is analogous to the individual who stops smoking; the desire for the cigarette remains, but the behavior has been modified to no longer sustain smoking. To manage change in an organization, nurses must modify behavior to produce the intended outcome.

Change takes hold when strong leadership support exists. This support manifests itself as a visible presence to staff, clear and concise communications, an unwavering position, and an open door policy to field concerns about change. Too often, leadership gives verbal endorsement of change and then fails to follow through with these actions or withdraws support when the going gets tough. Inevitability, if leadership wavers, so too will staff.

Measuring the Results Metrics provide understanding about the performance of a process or function. Typically within clinical technology projects, we identify and collect specific metrics about the performance of the technology or metrics that capture the level of participation or adoption. Equally important is the need for process performance metrics. Process metrics are collected at the initial stage of project or problem identification. Current-state metrics are then benchmarked against internal indicators. When there are no internal indicators to benchmark against, a suitable course of action is to benchmark against an external source such as a similar business practice within a different industry. Consider examining the hotel room change-over strategy or the customer service approach of Walt Disney Company or Ritz Carlton hotels, for example, to determine suitable metrics for a particular project or focus area.

TABLE 14-1 EXAMPLES OF METRICS Turnaround times Change-over time Patient satisfaction Cycle times Set-up time Employee satisfaction Throughput System availability

The right workflow complement will provide the organization with the data it needs to understand operational and clinical performance. This area is highlighted through the need for healthcare organizations to capture meaningful use measures. Good metrics should tell the story of accomplishment. The presence of technology alone does not guarantee an organization’s ability to capture and report on these measures without also addressing the surrounding workflow. Metrics should focus on the variables of time, quality, and costs. Table 14-1 provides examples of relevant metrics.

The ARRA highlights the need for healthcare organizations to collect information that represents the impact of technology on patient outcomes. Furthermore, data are necessary to demonstrate how a process is performing in its current state. In spite of the ARRA mandates, the need to collect data to demonstrate improvement in workflow—though it remains strong—is all too often absent in implementation or redesign efforts. A team cannot demonstrate improvements to an existing process without collecting information about how the process is performing today. Current-state measures also help the process team validate that the correct area for improvement was identified. Once a process improvement effort is over and the new solution has been implemented, postimprovement measures should be gathered to demonstrate progress.

In some organizations, the informatics professional reports to the director of operations, the chief information officer, or the chief operations officer. In this relationship, the need to demonstrate operational measures is even stronger. Operational measures such as turnaround times, throughput, and equipment or technology availability are some of the measures captured.

Future Directions Workflow analysis is not an optional part of clinical implementations, but rather a necessity for safe patient care supported by technology. The ultimate goal of workflow analysis is not to “pave the cow path,” but rather to create a future-state solution that maximizes the use of technology and eliminates non–value-added activities. Although many tools to accomplish workflow redesign are available, the best method is the one that complements the organization and supports the work of clinicians. Redesigning how people do work will evidentially create change; thus the nursing informaticist will need to apply change management principles for the new way of doing things to take hold.

Workflow analysis has been described in this chapter within the context of the most widely accepted tools that are fundamentally linked to the concepts of Six Sigma/Lean. Other methods of workflow analysis exist and may become commonly used to assess clinical workflow. An example of an alternative workflow analysis tool is the use of radio frequency badges to detect movement within a defined clinical area. Clinician and patient movements may be tracked using these devices, and corresponding actions may be documented, painting a picture of the workflow for a specific area (Vankipuram, 2010).

Another example of a workflow analysis tool involves the use of modeling software. An application such as ProModel provides images of the clinical work area where clinician workflows can be plotted out and reconfigured to best suit the needs of a specific area. Simulation applications enable decision makers to visualize realistic scenarios and draw conclusions about how to leverage resources, implement technology, and improve performance. Other vendors that offer simulation applications include Maya and Autodesk.

Healthcare organizations need to consider how other industries have analyzed and addressed workflow to their streamline business practices and improve quality outputs to glean best practices that might be incorporated into the healthcare industry’s own clinical and business approaches. First, however, each healthcare organization must step outside itself and recognize that not all aspects of patient care are unique; consequently, many aspects of care can be subjected to standardization. Many models of workflow redesign from

manufacturing and the service sector can be extrapolated to health care. The healthcare industry is facing difficult economic times and can benefit from performance improvement strategies used in other industries.

Although workflow analysis principles have been described within the context of acute and ambulatory care in this chapter, the need to perform process analysis on a macro level will expand as more organizations move forward with health information exchanges (HIE) and medical home models. Health information exchanges require the nursing informaticist to visualize how patients move through the entire continuum of care and not just a specific patient care area.

Technology initiatives will become increasingly complex in the future. In turn, nursing informaticists will need greater preparation in the area of process analysis and improvement techniques to meet the growing challenges that technology brings and the operational performance demands of fiscally impaired healthcare organizations.

Summary Meaningful use (MU) reflects the rules and regulations arising from ARRA. Nursing must lead the charge in this area, because nurses play an important part in organizations’ ability to meet the MU criteria based on nursing documentation. EHR adoptions “represent a small step rather than a giant leap forward” (Murphy, 2013, para. 1). Workflows integrating technology provide the healthcare professional with the data necessary to make informed decisions. This quality data must be collected and captured to meet MU criteria. Nurses must be involved in “meaningful data collection and reporting. Documentation by nurses can tell what’s going on with the patient beyond physical exams, test results, and procedures” (Daley, 2013, para. 5).

Workflow redesign is a critical aspect of technology implementation. When done well, it yields technology that is more likely to achieve the intended patient outcomes and safety benefits. Nursing informatics professionals are taking on a greater role with respect to workflow design, and this aspect of practice will grow in light of MU-driven objectives. Other initiatives that impact hospital performance will also drive informatics professionals to influence how technology is used in the context of workflow to improve the bottom line for their organizations. In an ideal world, nurse informaticists who are experts at workflow analysis will be core members of every technology implementation team.

THOUGHT-PROVOKING QUESTIONS

1. What do you perceive as the current obstacles to redesigning workflow within your clinical settings? 2. Thinking about your last implementation, were you able to challenge the policies and practices that constitute today’s workflow or were you able

to create a workflow solution that eliminated non–value-added steps? 3. Is the workflow surrounding technology usage providing the healthcare organization with the data it needs to make decisions and eventually

meet meaningful use criteria? 4. How does the current educational preparation need to change to address the skills necessary to perform workflow analysis and redesign clinical

processes?

References Agency for Healthcare Research and Quality (AHRQ). (2008). Patient safety and quality: An evidence-based handbook for nurses.

http://www.ahrq.gov/qual/nurseshdbk/ Agency for Healthcare Research and Quality (AHRQ). (n.d.). Workflow assessment for health IT toolkit. http://healthit.ahrq.gov/health-it-tools-and-

resources/workflowassessment-health-it-toolkit American Association for Justice. (2013). Preventable medical errors: The sixth biggest killer in America.

http://www.justice.org/cps/rde/justice/hs.xsl/8677.htm American Nurses Association (2008). Nursing informatics: Scope and standards of practice. Silver Springs, MD: Author. Campbell, E., Guappone, K., Sittig, D., Dykstra, R., & Ash, J. (2009). Computerized provider order entry adoption: Implications for clinical workflow.

Journal of General Internal Medicine, 24(1), 21–26. Centers for Medicare & Medicaid Services (CMS). (2013a). CMS.gov: Meaningful use. http://www.cms.gov/Regulations-and-

Guidance/Legislation/EHRIncentivePrograms/Meaningful_Use.html Centers for Medicare & Medicaid Services (CMS). (2013b). What is meaningful use?

http://www.cms.gov/eHealth/downloads/Webinar_eHealth_July2_IntroEHRProgram.pdf Clancy, T., Delaney, C., Morrison, B., & Gunn, J. (2006). The benefits of standardized nursing languages in complex adaptive systems such as hospitals.

Journal of Nursing Administration, 36(9), 426–434. Daley, K. (2013). Making HIT meaningful for nursing and patients. http://www.theamericannurse.org/index.php/2011/08/01/making-hit-meaningful-for-

nursing-and-patients/ Earl, M., Sampler, J., & Sghort, J. (1995). Strategies for business process reengineering: Evidence from field studies. Journal of Management

Information Systems, 12(1), 31–56. Healthcare Information Management Systems Society (HIMSS). (2009). Nursing informatics impact study.

http://www.himss.org/files/HIMSSorg/content/files/HIMSS2009NursingInformaticsImpactSurveyFullResults.pdf Healthcare Information Management Systems Society (HIMSS). (2010). ME-PI toolkit: Process management, workflow & mapping: Tools, tips and

case studies to support the understanding, optimizing and monitoring of processes. Retrieved November 2010 from http://www.himss.org/ASP/topics_FocusDynamic.asp?faid=322

Kotter, J. P. (1996). Leading change (pp. 33–147). Cambridge, MA: Harvard Business Press. Merriam-Webster Online Dictionary. (2010). Optimization. http://www.merriam-webster.com/dictionary/optimization Murphy, K. (2013). Nursing approach to meaningful use, EHR adoption: CIO series. http://ehrintelligence.com/2013/02/21/nursing-approach-to-

meaningful-use-ehr-adoption-cioseries/ Qualis Health. (2011). Workflow analysis. http://www.qualishealthmedicare.org/healthcare-providers/improvement-fundamentals/workflow-analysis Sherman, R. (2013). What nurse leaders need to know about meaningful use. http://www.emergingrnleader.com/nurseleaderdevelopment-2/ Vankipuram, K. (2010). Toward automated workflow analysis and visulization in clinical environment. Journal of Biomedical Informatics.

doi:10.1016/jbi.2010.05.015 Yuan, M., Finley, G., Long, J., Mills, C. & Johnson, R. (2013). Evaluation of user interface and workflow design of a bedside nursing clinical decision

support system. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3628119/

Section IV

Nursing Informatics Practice Applications: Care Delivery

Chapter 15 The Electronic Health Record and Clinical Informatics Chapter 16 Informatics Tools to Promote Patient Safety and Quality Outcomes Chapter 17 Supporting Consumer Information and Education Needs Chapter 18 Using Informatics to Promote Community/Population Health Chapter 19 Telenursing and Remote Access Telehealth

Nursing information systems must support nurses as they fulfill their roles in delivering quality patient care. Such systems must be responsive to nurses’ needs, allowing them to manage their data and information as needed and providing access to necessary references, literature sources, and other networked departments. Nurses have always practiced in a field where they have needed to use their ingenuity, resourcefulness, creativity, initiative, and skills. To improve patient care and advance the science of nursing, clinicians as knowledge workers must apply these same abilities and skills to become astute users of available information systems.

In this section, the reader learns about clinical practice tools, electronic health records, and clinical information systems; informatics tools to enhance patient safety, provide consumer information, and meet education needs; population and community health tools; and telehealth and telenursing. Information systems, electronic documentation, and electronic health records are changing the way nurses and physicians practice. Nursing informatics systems are also changing how patients enter and receive data and information. Some institutions, for example, are permitting patients to access their own records electronically via the Internet or a dedicated patient portal. Confidentiality and privacy issues loom with these new electronic systems. HIPAA regulations (covered in the Perspectives on Nursing Informatics section) and professional ethics principles (covered in the Building Blocks of Nursing Informatics section) must remain at the forefront when clinicians interact electronically with intimate patient data and information.

The material within this book is placed within the context of the Foundation of Knowledge model (Figure IV-1) to meet the needs of healthcare delivery systems, organizations, patients, and nurses. Readers should continue to assess where they are in this model. The Foundation of Knowledge model reflects the fact that knowledge is powerful; for that reason, nurses must focus on information as a key resource. This section addresses the information systems with which clinicians interact in their healthcare environments as affected by legislation, professional codes of ethics, consumerism, and reconceptualization of practice paradigms, such as in telenursing. All of the various nursing roles—practice, administration, education, research, and informatics—involve the science of nursing.

Figure IV-1 Foundation of Knowledge Model Source: Designed by Alicia Mastrian

Chapter 15

The electronic Health Record and clinical informatics Emily B. Barey, Kathleen Mastrian, and Dee McGonigle

OBJECTIVES

1. Describe the common components of an electronic health record. 2. Assess the benefits of implementing an electronic health record. 3. Explore the ownership of an electronic health record. 4. Evaluate the flexibility of the electronic health record in meeting the needs of clinicians and patients.

Key Terms

Administrative processes American Recovery and Reinvestment Act of 2009 (ARRA) Connectivity Decision support Electronic communication Electronic health record Health information Health Information Technology for Economic and Clinical Health Act of 2009 (HITECH) Interoperability Meaningful use Order entry management Patient support Population health management Reporting Results management

Introduction The significance of electronic health records (EHRs) to nursing cannot be underestimated. Although EHRs on the surface suggest a simple automation of clinical documentation, in fact their implications are broad, ranging from the ways in which care is delivered, to the types of interactions nurses have with patients in conjunction with the use of technology, to the research surrounding EHRs that will inform nursing practice for tomorrow. A basic knowledge of EHRs and nursing informatics is now considered by many to be an entry-level nursing competency.

At the Technology Informatics Guiding Education Reform (TIGER, 2006) summit on evidence and informatics transforming nursing, participants stated that “the nation is working full-speed to realize the 10-year goal of Electronic Health Records for its citizens” (p. 1). Nurses must become active participants in this effort to capture healthcare information, generate knowledge, and enhance patient care. “This is a critical juncture for nurses, who comprise 55% of the healthcare workforce, number more than 3 million, and who must become more aware and involved at every level of the Informatics Revolution” (p. 1). Although EHR standards are evolving and barriers to adoption remain, the collective work has a positive momentum that will benefit clinicians and patients alike.

This drive to adopt EHRs has been underscored with the passage of the Health Information Technology for Economic and Clinical Health Act of 2009 (HITECH). It is essential that this competency be developed if nurses are to participate fully in the changing world of healthcare information technology.

This chapter has four goals. First, it describes the common components of an EHR. Second, it reviews the benefits of implementing an EHR. Third, it provides an overview of successful ownership of an EHR, including nursing’s role in promoting the safe adoption of EHRs in day-to-day practice. Fourth, it discusses the flexibility of an EHR in meeting the needs of both clinicians and patients, including an introduction to interoperability.

Setting the Stage The U.S. healthcare system faces the enormous challenge of improving the quality of care while simultaneously controlling costs. EHRs have been proposed as one solution to achieve this goal (Institute of Medicine [IOM], 2001). In January 2004, President George W. Bush raised the profile of EHRs in his State of the Union address by outlining a plan to ensure that most Americans have an EHR by 2014. He stated that “by computerizing health records we can avoid dangerous medical mistakes, reduce costs, and improve care” (Bush, 2004). This proclamation generated an increased demand for understanding EHRs and promoting their adoption, but relatively few healthcare organizations were motivated to pursue more rapid adoption of EHRs. The Healthcare Information and Management Systems Society (HIMSS) has been tracking EHR adoption since 2005 through its “Stage 7” award and reports that most

U.S. healthcare organizations (77%) are in Stage 3, reflecting only implementation of the basic components of laboratory, radiology, and pharmacy ancillaries; a clinical data repository, including a controlled medical vocabulary; and simple nursing documentation and clinical decision support (HIMSS, 2013). Higher stages of the electronic medical record adoption model include more sophisticated use of decision support tools and medication administration tools, with Stage 7—the highest level—consisting of EHRs that have data sharing and warehousing capabilities and that are completely interfaced with emergency and outpatient facilities (HIMSS Analytics, 2013).

In President Barack Obama’s first term in office, Congress passed the American Recovery and Reinvestment Act of 2009 (ARRA). This legislation included the HITECH Act, which specifically sought to incentivize health organizations and providers to become meaningful users of EHRs. These incentives will come in the form of increased reimbursement rates from the Centers for Medicare and Medicaid Services (CMS); ultimately, the HITECH Act will result in payment of a penalty by any healthcare organization that has not adopted EHRs by January 2015. The final rule was published by the Department of Health and Human Services (HHS) in July 2010 for the first phase of implementation. Stage 1 meaningful use criteria focus on data capture and sharing (DHHS, 2010). Stage 2 criteria, which are slated for implementation by 2014, advance several clinical processes and promote health information exchange (HIE) and more patient control over personal data. Stage 3, which has a target implementation date of 2016, focuses on improved outcomes for individuals and populations, and introduction of patient self-management tools (HealthIT.gov, 2013).

Components of Electronic Health Records Overview Before enactment of the ARRA, several variants of EHRs existed, each with its own terminology and each developed with a different audience in mind. The sources of these records included, for example, the federal government (Certification Commission for Healthcare Information Technology, 2007), the IOM (2003), the HIMSS (2007), and the National Institutes of Health (2006; Robert Wood Johnson Foundation [RWJF], 2006). Under ARRA, there is now an explicit requirement for providers and hospitals to use a certified EHR that meets a set of standard functional definitions to be eligible for the increased reimbursement incentive. HHS has granted two organizations the authority to accredit EHRs: the Drummond Group and the Certification Commission for Healthcare Information Technology. These bodies are authorized to test and certify EHR vendors against the standards and test procedures developed by the National Institute of Standards and Technology (NIST) and endorsed by the Office of the National Coordinator for Health Information Technology for EHRs.

The NIST test procedure includes 45 certification criteria, ranging from the basic ability to record patient demographics, document vital signs, and maintain an up-todate problem list, to more complex functions, such as electronic exchange of clinical information and patient summary records (Jansen & Grance, 2011; NIST, 2010). Box 15-1 lists the 45 certification criteria outlined by NIST.

Despite the points articulated in the ARRA, the IOM definition also remains a valid reference point. This definition is useful because it has distilled all the possible features of an EHR into eight essential components with an emphasis on functions that promote patient safety—a universal denominator that everyone in health care can accept. The eight components are (1) health information and data, (2) results management, (3) order entry management, (4) decision support, (5) electronic communication and connectivity, (6) patient support, (7) administrative processes, and (8) reporting and population health management (IOM, 2003). Each of these components is described in more detail here. With the exception of EHR infrastructure functions, such as security and privacy management, controlled medical vocabularies, and interoperability standards, the 45 NIST standards easily map into the IOM categories (Jansen & Grance, 2011).

BOX 15-1 EHR CERTIFICATION CRITERIA

Criteria # Certification Criteria §170.302 (a) Drug–drug, drug–allergy interaction checks §170.302 (b) Drug formulary checks §170.302 (c) Maintain up-to-date problem list §170.302 (d) Maintain active medication list §170.302 (e) Maintain active medication allergy list §170.302 (f)(1) Vital signs §170.302 (f)(2) Calculate body mass index §170.302 (f)(3) Plot and display growth charts §170.302 (g) Smoking status §170.302 (h) Incorporate laboratory test results

§170.302 (i) Generate patient lists §170.302 (j) Medication reconciliation §170.302 (k) Submission to immunization registries §170.302 (l) Public health surveillance §170.302 (m) Patient-specific education resources §170.302 (n) Automated measure calculation §170.302 (o) Access control §170.302 (p) Emergency access §170.302 (q) Automatic log-off §170.302 (r) Audit log §170.302 (s) Integrity §170.302 (t) Authentication §170.302 (u) General encryption §170.302 (v) Encryption when exchanging electronic health information §170.302 (w) Accounting of disclosures (optional) §170.304 (a) Computerized provider order entry §170.304 (b) Electronic prescribing §170.304 (c) Record demographics §170.304 (d) Patient reminders §170.304 (e) Clinical decision support §170.304 (f) Electronic copy of health information §170.304 (g) Timely access §170.304 (h) Clinical summaries §170.304 (i) Exchange clinical information and patient summary record §170.304 (j) Calculate and submit clinical quality measures §170.306 (a) Computerized provider order entry §170.306 (b) Record demographics §170.306 (c) Clinical decision support §170.306 (d)(1) Electronic copy of health information §170.306 (d)(2) Electronic copy of health information

Note: For discharge summary §170.306 (e) Electronic copy of discharge instructions §170.306 (f) Exchange clinical information and patient summary record §170.306 (g) Reportable lab results §170.306 (h) Advance directives §170.306 (i) Calculate and submit clinical quality measures

Data from National Institute of Standards and Technology (NIST). (2010). Meaningful use test measures: Approved test procedures. Retrieved October 2010 from http://healthcare.nist.gov/use_testing/finalized_requirements.html

Health Information and Data

Health information and data comprises the patient data required to make sound clinical decisions, including demographics, medical and nursing diagnoses, medication lists, allergies, and test results (IOM, 2003).

Results Management

Results management is the ability to manage results of all types electronically, including laboratory and radiology procedure reports, both current and historical (IOM, 2003).

Order Entry Management

Order entry management is the ability of a clinician to enter medication and other care orders, including laboratory, microbiology, pathology, radiology, nursing, supply orders, ancillary services, and consultations, directly into a computer (IOM, 2003).

Decision Support

Decision support entails the use of computer reminders and alerts to improve the diagnosis and care of a patient, including screening for correct drug selection and dosing, screening for medication interactions with other medications, preventive health reminders in such areas as vaccinations, health risk screening and detection, and clinical guidelines for patient disease treatment (IOM, 2003).

Electronic Communication and Connectivity

Electronic communication and connectivity include the online communication among healthcare team members, their care partners, and patients including e-mail, Web messaging, and an integrated health record within and across settings, institutions, and telemedicine (IOM, 2003).

Patient Support

Patient support encompasses patient education and self-monitoring tools, including interactive computer-based patient education, home telemonitoring, and telehealth systems (IOM, 2003).

Administrative Processes

Administrative processes are activities carried out by the electronic scheduling, billing, and claims management systems, including electronic scheduling for inpatient and outpatient visits and procedures, electronic insurance eligibility validation, claim authorization and prior approval, identification of possible research study participants, and drug recall support (IOM, 2003).

Reporting and Population Health Management

Reporting and population health management are the data collection tools to support public and private reporting requirements, including data represented in a standardized terminology and machine-readable format (IOM, 2003).

NIST has not provided an exhaustive list of all possible features and functions of an EHR. Consequently, different vendor EHR systems combine different components in their offerings, and often a single set of EHR components may not meet the needs of all clinicians and patient populations. For example, a pediatric setting may demand functions for immunization management, growth tracking, and more robust order entry features to include weight-based dosing (Spooner & Council on Clinical Information Technology, 2007). These types of features may not be provided by all EHR systems, and it is important to consider EHR certification to be a minimum standard.

Advantages of Electronic Health Records There are mixed reviews of the advantages offered by EHRs. Much has been written about the potential promise of reduced costs, improved quality, and better outcomes, but very little of this promise’s realization has been substantiated except anecdotally (Sidorov, 2006). Possible methods to estimate EHR benefits include using vendorsupplied data that have been retrieved from their customers’ systems, synthesizing and applying studies of overall EHR value, creating logical engineering models of EHR value, summarizing focused studies of elements of EHR value, and conducting and applying information from site visits (HealthIT.gov, 2012; Thompson, Osheroff, Classen, & Sittig, 2007). However, the time and effort involved in completing this work are further increased by the fact that historically there has been no standard by which to measure adoption or expected benefits (HealthIT.gov, 2012; RWJF, 2006; Thompson et al., 2007).

With the advent of ARRA, there are now 25 meaningful use objectives for eligible providers and 24 such objectives for eligible hospitals (CMS, 2010a). The Nursing Informatics: Improving Workflow and Meaningful Use chapter provides an expanded discussion of meaningful use. In addition, the final rule calls for providers to report on three required clinical quality measures and three additional quality measures of their choice from a list of 44 possible measures (CMS, 2010b). Eligible hospitals must report on 15 clinical quality measures. Although these objectives and measures will provide a universal benchmark moving forward for EHR benefits, for most healthcare organizations these outcomes alone will not provide sufficient return on investment to justify the capital investment made to implement an EHR, and additional benefits will continue to be sought.

The four most common benefits cited for EHRs are (1) increased delivery of guidelines-based care, (2) enhanced capacity to perform surveillance and monitoring for disease conditions, (3) reduction in medication errors, and (4) decreased use of care (Chaudhry et al., 2006; HealthIT.gov, 2012). These findings were echoed by two similar literature reviews. The first review focused on the use of informatics systems for managing patients with chronic illness. It found that the processes of care most positively impacted were guidelines adherence, visit frequency (i.e., a decrease in emergency department visits), provider documentation, patient treatment adherence, and screening and testing (Dorr et al., 2007).

The second review was a cost–benefit analysis of health information technology completed by the Agency for Healthcare Research and Quality (AHRQ) that studied the value of an EHR in the ambulatory care and pediatric settings, including its overall economic value. The AHRQ study highlighted the common findings already described, but also noted that most of the data available for review came from six leading healthcare organizations in the United States, underscoring the challenge of generalizing these results to the broader healthcare industry (Shekelle, Morton, & Keeler, 2006). As noted previously by the HIMSS Stage 7 Awards, the challenge to generalize results persists in the hospital arena, with fewer than 1% of U.S. hospitals or eight leading organizations providing most of the experience with comprehensive EHRs (HIMSS, 2010a).

Finally, all three literature reviews cited here indicated that there are a limited number of hypothesis-testing studies of EHRs and even fewer that have reported cost data.

The descriptive studies do have value, however, and should not be hastily dismissed. Although not as rigorous in their design, they do describe the advantages of EHRs well and often include useful implementation recommendations learned from practical experience. As identified in these types of reviews, EHR advantages include simple benefits, such as no longer having to interpret poor handwriting and handwritten orders, reduced turnaround time for laboratory results in an emergency department, and decreased time to administration of the first dose of antibiotics in an inpatient nursing unit (HealthIT.gov, 2012; Husk & Waxman, 2004; Smith et al.,

2004). In the ambulatory care setting, improved management of cardiacrelated risk factors in patients with diabetes and effective patient notification of medication recalls have been demonstrated to be benefits of the EHR (Jain et al., 2005; Reed & Bernard, 2005). Two other unique advantages that have great potential are the ability to use the EHR and decision support functions to identify patients who qualify for research studies or who qualify for prescription drug benefits offered by pharmaceutical companies at safety-net clinics and hospitals (Embi et al., 2005; Poprock, 2005).

The HIMSS Davies Award may be the best resource for combined quantitative and qualitative results of successful EHR implementation. The Davies Award recognizes healthcare organizations that have achieved both excellence in implementation and value from health information technology (HIMSS, 2010b). One winner demonstrated a significant avoidance of medication errors because of bar-code scanning alerts, a $3 million decrease in medical records expenses as a result of going paperless, and a 5% reduction of duplicate laboratory orders by using computerized provider order entry alerting (HIMSS, 2010c). Another winner noted a 13% decrease in adverse drug reactions through the use of computerized physician order entry; it also achieved a decrease in methicillin-resistant Staphylococcus aureus (MRSA) nosocomial infections from 9.8 per 10,000 discharges to 6.4 per 10,000 discharges in less than a year using an EHR flagging function, which made clinicians immediately aware that contact precautions were required for MRSA–positive patients (HIMSS, 2009). At both organizations, there was qualitative and quantitative evidence of high rates of end user adoption and satisfaction with use of the EHR.

A 2011 study of the effects of EHR adoption on nurse perceptions of quality of care, communication, and patient safety documented that nurses report better care outcomes and fewer concerns with care coordination and patient safety in hospitals with a basic EHR (Kutney-Lee & Kelly, 2011). In this study, nurses perceived that in hospitals with a functioning EHR, there was better communication among staff, especially during patient transfers, and fewer medication errors.

Without an EHR system, any of these benefits would be very difficult and costly to accomplish. Thus, despite limited standards and published studies, there is enough evidence to warrant pursuing widespread implementation of the EHR (Halamka, 2006; HealthIt.gov, 2012), and certainly enough evidence to warrant further study of the use and benefits of EHRs. Box 15-2 describes some of the clinical information system (CIS) functions of an EHR.

BOX 15-2 THE EHR AS A CLINICAL INFORMATION SYSTEM Denise Tyler A clinical information system (CIS) is a technology-based system applied at the point of care and designed to support care by providing instant access to information for clinicians. Early CISs implemented prior to the advent of EHRs were limited in scope and provided such information as interpretation of laboratory results or a medication formulary and drug interaction information. With the implementation of EHRs, the goal of many organizations is to expand the scope of the early CISs to become comprehensive systems that provide clinical decision support, an electronic patient record, and in some instances professional development and training tools. Benefits of such a comprehensive system include easy access to patient data at the point of care; structured and legible information that can be searched easily and lends itself to data mining and analysis; and improved patient safety, especially the prevention of adverse drug reactions and the identification of health risk factors, such as falls.

TRACKING CLINICAL OUTCOMES The ability to measure outcomes can be enhanced or impeded by the way an information system is designed and used. Although many practitioners can paint a very good picture of the patient by using a narrative (free text), employing this mode of expression in a clinical system without the use of a coded entry makes it difficult to analyze the care given or the patient’s response. Free-text reporting also leads to inconsistencies of reporting from clinician to clinician and patient information that is fragmented or disorganized. This can limit the usefulness of patient data to other clinicians and interfere with the ability to create reports from the data for quality assurance and measurement purposes. Moreover, not all clinicians are equally skilled at the free-text form of communication, yielding inconsistent quality of documentation. Integrating standardized nursing terminologies into computerized nursing documentation systems enhances the ability to use the data for reporting and further research.

According to the IOM (2012), “Payers, healthcare delivery organizations and medical product companies should contribute data to research and analytic consortia to support expanded use of care data to generate new insights” (para. 2). McLaughlin and Halilovic (2006) described the use of clinical analytics to promote medical care outcomes research. The use of a CIS in conjunction with standardized codes for patient clinical issues helps to support the rigorous analysis of clinical data. Outcomes data produced as part of these analyses may include length of stay, mortality, readmissions, and complications. Future goals include the ability to compare data and outcomes across various institutions as a means of developing clinical guidelines or best practices guidelines. With the implementation of a comprehensive CIS, similar analyses of nursing outcomes could also be performed and shared. Likewise, such a system could aid nurse administrators in cross-unit comparisons and staffing decisions, especially when coupled with acuity systems data. In addition, clinical analytics can support required data reporting functions, especially those required by accreditation bodies.

SUPPORTING EVIDENCE-BASED PRACTICE Evidence-based practice (EBP) can be thought of as the integration of clinical expertise and best practices based on systematic research to enhance decision making and improve patient care. References supporting EBP, such as clinical guidelines, are available for review at the click of a mouse or the press of a few keystrokes. The CIS’s prompting capabilities can also reinforce the practice of looking for evidence to support nursing interventions rather than relying on how things have been done historically. This approach enhances processing and understanding of the information and allows the nurse to apply the information to other areas, increasing the knowledge obtained about why certain conditions or responses result in prompts for additional questions or actions.

To incorporate EBP into the practice of clinical nursing, the information needs to be embedded in the computerized documentation system so that it is part of the workflow. The most typical way of embedding this timely information is through clinical practice guidelines. The resulting interventions and clinical outcomes need to be measurable and reportable for further research. The supporting documentation for the EBP needs to be easily retrievable and meaningful. Links, reminders, and prompts can all be used as vehicles for transmission of this information. The format needs to allow for rapid scanning, with the ability to expand the amount of information when more detail is required or desired. Balancing a consistency in formatting with creativity can be difficult but is worth the effort to stimulate an atmosphere for learning.

EBP is supported by translational research, an exciting movement that has enormous potential for the sharing and use of EBP. The use of translational research to support EBP may help to close the gap between what is known (research) and what is done (practice).

THE CIS AS A STAFF DEVELOPMENT TOOL Joy Hilty, a registered nurse from Kaweah Delta, came up with a creative way to provide staff development or education without taking staff away from the bedside to a classroom setting. She created pop-up boxes on the opening charting screens for all staff who chart on the computer. These

pop-ups vary in color and content and include a short piece of clinical information, along with a question. Staff can earn vacations from these pop- ups for as long as 14 days by e-mailing the correct answer to the question. This medium has provided information, stimulation, and a definite benefit: the vacation from the pop-up boxes. The pop-up box education format has also encouraged staff to share their answers, thereby creating interaction, knowledge dissemination, and reinforcement of the education provided.

Embedding EBP into nursing documentation can also increase the compliance with Joint Commission core measures, such as providing information on influenza and pneumococcal vaccinations to at-risk patients. In the author’s experience at Kaweah Delta, educating staff via classes, flyers, and storyboards was not successful in improving compliance with the documentation of immunization status or offering education on these vaccinations to at-risk patients. Embedding the prompts, information, and related questions in the nursing documentation with a link to the protocol and educational material, however, improved the compliance to 96% for pneumococcal vaccinations and to 95% for influenza vaccinations (Hettinger, 2007).

As more information is stored electronically, nurse informaticists must translate the technology so that the input and retrieval of information are developed in a manner that is easy for clinicians to learn and use. A highly usable product should decrease errors and improve information entry and retrieval. Nurse informaticists must be able to work with staff and expert users to design systems that meet the needs of the staff who will actually use the systems. The work is not done after the system is installed; the system must continue to be developed and improved, because as staff use the system, they will be able to suggest changes to improve it. This ongoing revision should result in a system that is mature and meets the needs of the users.

In an ideal world, all clinical documentation will be shared through a national database, in a standard language, to enable evaluation of nursing care, increase the body of evidence, and improve patient outcomes. With minimal effort, the information will be translated into new research that can be analyzed and linked to new evidence that will be intuitively applied to the CIS. Alerts will be meaningful and will be patient and provider specific. The steps required of the clinician to find current, reliable information will be almost transparent, and the information will be presented in a personalized manner based on user preferences stored in the CIS.

REFERENCES Hettinger, M. (2007, March). Core measure reporting: Performance improvement. Visalia, CA: Kaweah Delta Health Care District.

McLaughlin, T., & Halilovic, M. (2006). Clinical analytics, rigorous coding bring objectivity to quality assertions. Medical Staff Update Online, 30(6). http://med.stanford.edu/shs/update/archives/JUNE2006/analytics.htm

Ownership of Electronic Health Records The implementation of an EHR has the potential to affect every member of a healthcare organization. The process of becoming a successful owner of an EHR has multiple steps and requires integrating the EHR into the organization’s day-to-day operations and long-term vision, as well as into the clinician’s day-to-day practice. All members of the healthcare organization—from the executive level to the clinician at the point of care—must feel a sense of ownership to make the implementation successful for themselves, their colleagues, and their patients. Successful ownership of an EHR may be defined in part by the level of clinician adoption of the tool, and this section reviews key steps and strategies for the selection, implementation and evaluation, and optimization of an EHR in pursuit of that goal.

Historically, many systems were developed locally by the information technology department of a healthcare organization. It was not unusual for software developers to be employed by the organization to create needed systems and interfaces between them. As commercial offerings were introduced and matured, it became less and less common to see homegrown or locally developed systems.

As this history suggests, the first step of ownership is typically a vendor selection process for a commercially available EHR. During this step, it is important to survey the organization’s level of interest, identify possible barriers to participation, document desired functions of an EHR, and assess the willingness to fund the implementation (Holbrook, Keshavjee, Troyan, Pray, & Ford, 2003). Although clinicians should drive the project, the assessment should also include the needs and readiness of the executive leadership, information technology, and project management teams. It is essential that leadership understands that this type of project is as much about redesigning clinical work as it is about technically automating it and that they agree to assume accountability for its success (Goddard, 2000). In addition, this preacquisition phase should concentrate on understanding the current state of the health information technology industry to identify appropriate questions and the next steps in the selection process (American Organization of Nurse Executives, 2006). These first steps begin to identify any organizational risks related to successful implementation and pave the way for initiating a change management process to educate the organization about the future state of delivering health care with an EHR system.

The second step of the selection process is to select a system based on the organization’s current and predicted needs. It is common during this phase to see a demonstration of several vendors’ EHR products. Based on the completed needs assessment, the organization should establish key evaluation criteria to compare the different vendors and products. These criteria should include both subjective and objective items that cover such topics as common clinical workflows, decision support, reporting, usability, technical build, and maintenance of the system. Providing the vendor with these guidelines will ensure that the process meets the organization’s needs; however, it is also essential to let the vendor demonstrate a proposed future state from its own perspective. This activity is critical to ensuring that the vendor’s vision and the organization’s vision are well aligned (Konschak & Shiple, n.d.). It also helps spark dialogue about the possible future state of clinical work at the organization and the change required in obtaining it. Such demonstrations not only enable the organization to compare and contrast the features and functions of different systems, but also are a good way to engage the organization’s members in being a part of this strategic decision.

Implementation planning should occur concurrently with the selection process, particularly the assessment of the scope of the work, initial sequencing of the EHR components to be implemented, and resources required. However, this step begins in earnest once a vendor and a product have been selected. In addition to further refining the implementation plan, this is the time to identify key metrics by which to measure the EHR’s success. An organization may realize numerous benefits from implementing an EHR. It should choose metrics that match its overall strategy and goals in the coming years and may include expected improvements in financial, quality, and clinical outcomes. Commonly used metrics focus on reductions in the number of duplicate laboratory tests through duplicate orders alerting, reductions in the number of adverse drug events through the use of bar-code medication administration, meaningful use objectives and measures, and the EHR advantages mentioned earlier in this chapter. To ensure that the desired benefits

are realized, it is important to avoid choosing so many that they become meaningless or unobtainable, to carefully and practically define those that are chosen, to measure before and after the implementation, and to assign accountability to a member of the organization to ensure the work is completed.

End-user adoption of the EHR is also essential to realizing its benefits. Clinicians must be engaged to use the EHR successfully in their practice and daily workflows so that data may be captured to drive the decision support that underlies so many of the advantages and metrics described. To promote adoption, a change management plan must be developed in conjunction with the EHR implementation plan. The most effective change management plans offer end users several exposures to the system and relevant workflows in advance of its use and continue through the go-live and postlive time periods. Successful prelive strategies include end- user involvement as subject-matter experts to validate the EHR workflow design and content build, hosting end-user usability testing sessions, shadowing end users in their current daily work in parallel with the new system, and formal training activities. The goal of these prelive activities is not only to ensure that the EHR implementation will meet end user needs, but also to assess the impact of the new EHR on current workflow and process. The larger the impact, the more change management is required above and beyond system training. For example, simulation laboratory experiences may be offered to more thoroughly dress rehearse a significant workflow change, executive leadership may need to convey their support and expectations of clinicians about a new way of working, and generally more anticipatory guidance is required to communicate to those impacted by the changes.

Training may be delivered in a variety of media. Often a combination of approaches works best, including classroom time, electronic learning, independent exercises, and peer-to-peer, at-the-elbow support. Training must be workflow based and reflect real clinical processes. It must also be planned and budgeted for through the postlive period to ensure that competency with the system is assessed at the go-live point and that any necessary retraining or reinforcements are made in the 30 to 60 days postlive. This not only promotes reliability and safe use of the system as it was designed but also can have a positive impact on end users’ morale: Users will feel that they are being supported beyond the initial go-live period and have an opportunity to move from basic skills to advanced proficiency with the system.

Finally, the implementation plan should account for the long-term optimization of the EHR. This step is commonly overlooked and often results in benefits falling short of expectations because the resources are not available to realize them permanently. It also often means the difference between end users of EHRs merely surviving the change versus becoming savvy about how to adopt the EHR as another powerful clinical tool, such as the stethoscope (HealthIT.gov, 2012). Optimization activities of the EHR should be considered a routine part of the organization’s operations, should be resourced accordingly, and should emphasize the continued involvement of clinician users to identify ways that the EHR can enable the organization to achieve its overall mission. Many organizations start an implementation of EHRs with the goal of transforming their care delivery and operations. An endeavor that differs from simply automating a previously manual or fragmented process, transformation often includes steps to improve the process so as to realize better patient care outcomes or added efficiency. Although some transformation is experienced with the initial use of the system, most of this work is done postimplementation and relies on widespread clinician adoption of the EHR. As such, it makes optimization a critical component to successful ownership of an EHR.

Box 15-3 reviews the barriers to and methods for successful acceptance of EHRs.

BOX 15-3 RESISTANCE TO IMPLEMENTATION Julie A. Kenney and Ida Androwich For an implementation to be successful, a few things need to happen. The informatics nurse specialist (INS) will need to understand and use change management theory to ensure that the implementation of the new EHR system will be successful. It is a well-known fact that nurses can make or break a system implementation. A nursing staff that is involved early in the implementation process has been found to be a major determinant in a successful implementation. Assessing nursing attitudes and concerns early in the process can aid the INS in determining the best way to proceed with staff education and implementation rollout. Nurses may believe that the implementation that should be making their job easier will actually make it more challenging (Trossman, 2005). Nurses who feel that the system has been forced onto them are very likely to be highly resistant to the change. This is why it is imperative that nurses be involved in the design, development, and implementation of the EHR. Nurses who have been involved in the implementation process will ensure that the product meets the needs of the staff, which will result in high end-user satisfaction (McLane, 2005).

Another challenge facing those wishing to implement an EHR is the issue that writing is nearly automatic for most, but using a computer is not. This potential problem can be overcome by ensuring that data entry and system navigation make for a system that is user friendly (Walsh, 2004). Voice data entry is an easy way to enter data into the system and may be a way for those who are not comfortable with computers to still use the system effectively (Walsh, 2004). Another way to encourage staff to accept the new EHR is to ensure that they have received adequate training prior to the implementation as well as to provide continued support and education after the implementation.

The implementation of a new EHR system requires the staff to make significant changes to how they work and how they handle patient information. The INS who is familiar with change management and the NI process should have an integral role in the redesign of workflow processes to ensure a smooth transition from a paper record to an electronic record. Many excellent EHR systems fail after their installation due to poor implementation planning. It is imperative that nurses are employed in the information systems (IS) department (Trossman, 2005).

REFERENCES McLane, S. (2005). Designing an EMR planning process based on staff attitudes toward and opinions about computers in healthcare. CIN: Computers, Informatics, Nursing, 23(2), 85–92.

Trossman, S. (2005). Bold new world: Technology should ease nurses’ jobs, not create a greater work load. American Journal of Nursing, 105(5), 75–77.

Walsh, S. (2004). The clinician’s perspective on electronic health records and how they can affect patient care. BMJ: British Medical Journal, 328(7449), 1184–1187.

Flexibility and Expandability

Health care is as unique as the patients themselves. It is delivered in a variety of settings, for a variety of reasons, over the course of a patient’s lifetime. In addition, patients rarely receive all their care from one healthcare organization; indeed, choice is a cornerstone of the American healthcare system. An EHR must be flexible and expandable to meet the needs of patients and caregivers in all these settings, despite the challenges.

At a very basic level, there is as yet no EHR system available that can provide all functions for all specialties to such a degree that all clinicians would successfully adopt it. Consider oncology as an example. Most systems do not yet provide the advanced ordering features required for the complex treatment planning undertaken in this field. An oncologist could use a general system, but he or she would not find as many benefits without additional features for chemotherapy ordering, lifetime cumulative dose tracking, or the ability to adjust a treatment day schedule and recalculate a schedule for the remaining days of the plan.

Further, most healthcare organizations do not yet have the capacity to implement and maintain systems in all care areas. As one physician stated, “implementing an EMR is a complex and difficult multidisciplinary effort that will stretch an organization’s skills and capacity for change” (Chin, 2004, p. 47).

These two conditions are improving every day at both vendor and healthcare organizations alike. Improvements in both areas were recently fueled by ARRA incentives (see Box 15-4).

BOX 15-4 CLOUDY EHRS A paradigm shift from healthcare facility–owned, machine-based computing to off-site, vendor-owned cloud computing, web browser-based log-in accessible data, software, and hardware could link systems together and reduce costs. Hospitals with shrinking budgets and extreme IT needs are exploring the successes in this area achieved in other industries, such as Amazon’s S3. As providers strive to implement potent EHRs, they are looking for cloud-based models that offer the necessary functionality without having to assume the burden associated with all of the hardware, software, application, and storage issues. However, in the face of the HITECH Act and its associated penalties, how can we overcome the challenges to realize the benefits of this approach? Cloud computing has both advantages and disadvantages, and while they explore this new paradigm, healthcare providers must relinquish control as they continue to strive to maintain security. The vendors that are responsible for developing and maintaining this new environment are also facing challenges originating from both legislatures and healthcare providers. As the vendors and healthcare providers work together to improve the implementation and adoption of the cloud-based EHR, the sky is the limit!

ARRA has also set the expectation that despite the large number of settings in which a patient may receive care, a minimum set of data from those records must flow or “interoperate” between each setting and the unique EHR systems used in those settings. Today, interoperability exists through what is called a continuity of care document. This data set includes patient demographics, medication, allergy, and problem lists, among other things, and the formatting and exchange of the continuity of care document is required to be supported by EHR vendors and healthcare organizations seeking ARRA meaningful use incentives.

Despite this positive step forward, financial and patient privacy hurdles remain to be overcome to achieve an expansive EHR. Most health care is delivered by small community practices and hospitals, many of which do not have the financial or technical resources to implement EHRs. HHS recently loosened regulations so that physicians may now be able to receive healthcare information technology software, hardware, and implementation services from hospitals to alleviate the financial burden placed on individual providers and to foster more widespread adoption of the EHR.

Finally, patient privacy is a pivotal issue in determining how far and how easy it will be to share data across healthcare organizations. In addition to the Health Insurance Portability and Accountability Act privacy rules, many states have regulations in place related to patient confidentiality. The recent experience of the state of Minnesota foreshadows what all states will soon be facing. In 2007, Governor Tim Pawlenty announced the creation of the Minnesota Health Information Exchange (State of Minnesota, 2007). Although the intentions of the exchange were to promote patient safety and increase healthcare efficiency across the state, it raised significant concerns about security and privacy. New questions arose about the definition of when and how patient consent is required to exchange data electronically, and older paper-based processes needed to be updated to support real-time electronic exchange (Minnesota Department of Health, 2007). For health exchanges such as these to reach their full potential, members of the public must be able to trust that their privacy will be protected, or else the healthcare industry risks that patients may not share a full medical history, or worse yet, may not seek care, effectively making the exchanges useless.

The Future Despite the challenges, the future of EHRs is an exciting one for patients and clinicians alike. Benefits may be realized by implementing stand-alone EHRs as described here, but the most significant transformation will come as interoperability is realized between systems. As the former national information technology coordinator in the HHS, David Brailer, noted about the potential of interoperability:

For the first time, clinicians everywhere can have a longitudinal medical record with full information about each patient. Consumers will have better information about their health status since personal health records and similar access strategies can be feasible in an interoperable world. Consumers can move more easily between and among clinicians without fear of their information being lost. Payers can benefit from the economic efficiencies, fewer errors, and reduced duplication that arises from interoperability. Healthcare information exchange and interoperability (HIEI) also underlies meaningful public health reporting, bioterrorism surveillance, quality monitoring, and advances in clinical trials. In short, there is little that most people want from health care for which HIEI isn’t a prerequisite. (Brailer, 2005, p. W 5-20)

The future also holds tremendous potential for EHR features and functions that will include not only more sophisticated decision support and clinical reporting capacity, but also improved biomedical device integration, ease of use and intuitiveness, and access through more hardware platforms.

Implementations of EHRs will become more commonplace in the near future, with ARRA putting pressure on healthcare

organizations to move more quickly toward adoption of such records. More organizations adopting EHRs will facilitate broader dissemination of implementation best practices, with the hope of further shortening the time required to take advantage of advanced EHR features.

Summary It is an important time for health care and technology. EHRs have come to the forefront and will remain central to shaping the future of health care. In an ideal world, all nurses, from entry-level personnel to executives, will have a basic competency in nursing informatics that will enable them to participate fully in shaping the future use of technology in the practice at a national level and wherever care is delivered. Such initiatives as TIGER are imperative for adoption and ultimately more visibility of nursing in the later phases of the ARRA meaningful use standards, which are still being defined.

THOUGHT-PROVOKING QUESTIONS

1. What are the implications for nursing education as the EHR becomes the standard for caring for patients? 2. What are the ethical considerations related to interoperability and a shared EHR? 3. You are asked about a diagnosis with which you are unfamiliar. Where would you start looking for information? How would you determine the

validity of the information?

References American Organization of Nurse Executives. (2006, September). Defining the role of the nurse executive in technology acquisition and implementation.

Washington, DC: Author. Retrieved October 2007 from http://www.aone.org/aone/pdf/Guiding%20Principles%20for%20Acquisition%20and%20Implementation%20of%20Information%20Technology.pdf

Brailer, D. J. (2005, January). Interoperability: The key to the future healthcare system. Health Affairs—Web Exclusive, W 5-19–W 5-21. http://content.healthaffairs.org/cgi/reprint/hlthaff.w5.19v1

Bush, G. W. (2004). State of the Union address. http://www.whitehouse.gov/news/releases/2004/01/20040120-7.html Centers for Medicare and Medicaid Services (CMS). (2010a). EHR incentive programs: Meaningful use.

https://www.cms.gov/EHRIncentivePrograms/35_Meaningful_Use.asp Centers for Medicare and Medicaid Services (CMS). (2010b). Quality measures: Electronic specifications.

http://www.cms.gov/QualityMeasures/03_ElectronicSpecifications.asp#TopOfPage Certification Commission for Healthcare Information Technology. (2007).

Certification Commission announces new work group members. Retrieved July 2007 from http://www.cchit.org/about/news/releases/Certification- Commission-Announces-New-Work-Group-Members.asp Chaudhry, B., Wang, J., Wu, S., Maglione, M., Mojica, W., Roth, E., … Shekelle, P. (2006).

Systematic review: Impact of health information technology on quality, efficiency, and costs of medical care. Annals of Internal Medicine, 144(10), E- 12–E-22.

Chin, H. L. (2004). The reality of EMR implementation: Lessons from the field. Permanente Journal, 8(4), 43–48. Department of Health and Human Services (DHHS). (2010). Medicare and Medicaid programs: Electronic health record incentive program. Retrieved

July 2010 from http://www.ofr.gov/OFRUpload/OFRData/2010-17202_PI.pdf Dorr, D., Bonner, L. M., Cohen, A. N., Shoai, R. S., Perrin, R., Chaney, E., & Young, A. (2007). Informatics systems to promote improved care for

chronic illness: A literature review. Journal of the American Medical Informatics Association, 14(2), 156–163. Embi, P. J., Jain, A., Clark, J., Bizjack, S., Hornung, R., & Harris, C. M. (2005). Effect of a clinical trial alert system on physician participation in trial

recruitment. Archives of Internal Medicine, 165, 2272–2277. Goddard, B. L. (2000). Termination of a contract to implement an enterprise electronic medical record system. Journal of American Medical Informatics

Association, 7, 564–568. Halamka, J. D. (2006, May). Health information technology: Shall we wait for the evidence? [Letter to the editor]. Annals of Internal Medicine, 144(10),

775–776. Healthcare Information and Management Systems Society (HIMSS). (2007). Electronic health record. http://www.himss.org/ASP/topics_ehr.asp Healthcare Information and Management Systems Society (HIMSS). (2009). HIMSS Davies Organizational Award application: MultiCare. Retrieved

October 2010 from http://www.himss.org/davies/docs/2009_RecipientApplications/MultiCareConnectHIMSSDaviesManuscript.pdf Healthcare Information and Management Systems Society (HIMSS). (2010a). Davies Award. http://www.himss.org/davies/ Healthcare Information and Management Systems Society (HIMSS). (2010b). HIMSS Davies Award. http://apps.himss.org/davies/ Healthcare Information and Management Systems Society (HIMSS). (2010c). Recipient list.

http://www.himssanalytics.org/hc_providers/stage7Hospitals.asp Healthcare Information and Management Systems Society (HIMSS). (2013). HIMSS 2013 iHIT Study: Executive summary.

http://www.himss.org/library/clinical-informatics/2013-ihitstudy-executive-summary Healthcare Information and Management Systems Society (HIMSS) Analytics. (2013). Electronic medical record adoption model (EMRAM).

http://www.himssanalytics.org/emram/index.aspx HealthIT.gov. (2012). Benefits of EHRs: Why adopt EHRs? http://www.healthit.gov/providers-professionals/why-adopt-ehrs HealthIT.gov. (2013). How to attain meaningful use. http://www.healthit.gov/providers-professionals/how-attain-meaningful-use Holbrook, A., Keshavjee, K., Troyan, S., Pray, M., & Ford, P. T. (2003). Applying methodology to electronic medical record selection. International

Journal of Medical Informatics, 71, 43–50. Husk, G., & Waxman, D. A. (2004). Using data from hospital information systems to improve emergency care. Academic Emergency Medicine, 11(11),

1237–1244. Institute of Medicine (IOM). (2001). Crossing the quality chasm: A new health system for the 21st century. Washington, DC: National Academies Press. Institute of Medicine (IOM). (2003). Key capabilities of an electronic health record system: Letter report. Washington, DC: National Academies Press. Institute of Medicine (IOM). (2012). Best care at lower cost. http://www.iom.edu/~/media/Files/Report%20Files/2012/Best-

Care/Best%20Care%20at%20Lower%20Cost_Recs.pdf Jain, A., Atreja, A., Harris, C. M., Lehmann, M., Burns, J., & Young, J. (2005). Responding to the rofecoxib withdrawal crisis: A new model for

notifying patients at risk and their healthcare providers. Annals of Internal Medicine, 142(3), 182–186.

Jansen, W., & Grance, T. (2011). National Institute of Standards and Technology (NIST): Guidelines on security and privacy in public cloud computing. https://cloudsecurityalliance.org/wp-content/uploads/2011/07/NIST-Draft-SP-800-144_cloud-computing.pdf

Konschak, C., & Shiple, D. (n.d.). System selection: Aligning vision and technology. http://www.divurgent.com/images/White%20Paper.Vendor%20Selection.vfinal.pdf

Kutney-Lee, A., & Kelly, D. (2011). The effect of hospital electronic health record adoption on nurse-assessed quality of care and patient safety. Journal of Nursing Administration, 41(11), 466–472. doi: 10.1097/NNA.0b013e3182346e4b

Minnesota Department of Health. (2007, June). Minnesota Health Records Act—HF 1078 fact sheet. Minneapolis, MN: Author. http://www.health.state.mn.us/e-health/hras/hras050113facts.pdf

National Institute of Standards and Technology (NIST). (2010). Meaningful use test measures: Approved test procedures. http://healthcare.nist.gov/use_testing/finalized_requirements.html

National Institutes of Health. (2006, April). Electronic health records overview. McLean, VA: Mitre Corporation. Poprock, B. (2005, September). Using Epic’s alternative medications reminder to reduce prescription costs and encourage assistance programs for

indigent patients. Presented at the Epic Systems Corporation user group meeting, Madison, WI. Reed, H. L., & Bernard, E. (2005). Reductions in diabetic cardiovascular risk by community primary care providers. International Journal of

Circumpolar Health, 64(1), 26–37. Robert Wood Johnson Foundation (RWJF). (2006). Health information technology in the United States: The information base for progress.

http://www.rwjf.org/programareas/resources/product.jsp?id=15895&pid=1142&gsa=1 Shekelle, P. G., Morton, S. C., & Keeler, E. B. (2006). Costs and benefits of health information technology. Evidence report/technology assessment, No.

132 [Prepared by the Southern California Evidence-based Practice Center under Contract No. 290-02-0003]. Agency for Healthcare Research and Quality Publication No. 06-E006. Rockville, MD: Agency for Healthcare Research and Quality.

Sidorov, J. (2006). It ain’t necessarily so: The electronic health record and the unlikely prospect of reducing healthcare costs. Health Affairs, 25(4), 1079–1085.

Smith, T., Semerdjian, N., King, P., DeMartin, B., Levi, S., Reynolds, K., … Dowd, J. (2004). Nicholas E. Davies Award of Excellence: Transforming healthcare with a patient-centric electronic health record system. Evanston, IL: Evanston Northwestern Healthcare. http://www.himss.org/content/files/davies2004_evanston.pdf

Spooner, S. A., & Council on Clinical Information Technology. (2007). Special requirements of electronic health record systems in pediatrics. Pediatrics, 119, 631–637.

State of Minnesota, Office of the Governor. (2007). New public–private partnership to improve patient care, safety and efficiency. Retrieved October 2007 from http://www.governor.state.mn.us/mediacenter/pressreleases/2007/PROD008303.html

Technology Informatics Guiding Education Reform (TIGER). (2006). Evidence and informatics transforming nursing. http://www.amia.org/sites/amia.org/files/Malin-AMIA-Tiger-Team-Testimony.pdf

Thompson, D. I., Osheroff, J., Classen, D., & Sittig, D. F. (2007). A review of methods to estimate the benefits of electronic medical records in hospitals and the need for a national database. Journal of Healthcare Information Management, 21(1), 62–68.

Chapter 16

Informatics Tools to Promote Patient Safety and Clinical Outcomes Kathleen Mastrian and Dee McGonigle

OBJECTIVES

1. Explore the characteristics of a safety culture. 2. Examine strategies for developing a safety culture. 3. Recognize how human factors contribute to errors. 4. Appreciate the impact of informatics technology on patient safety.

Key Terms

Alarm fatigue Bar-code medication administration Clinical decision support Computerized physician order entry Failure modes and effects analysis High-hazard drug Human factors engineering Radiofrequency identifier Root-cause analysis Safety culture Smart pump Smart room Wearable devices Workaround

Introduction Nursing professionals have an ethical duty to ensure patient safety. Increasing demands on professionals in complex and fast-paced healthcare environments, however, may lead them to cut corners or develop workarounds that deviate from accepted and expected practice protocols. These deviations are not carried out deliberately to put patients at risk, but rather are often practiced in the interest of saving time or because the organizational culture is such that risky behaviors are commonplace. Occasionally, these inappropriate actions or omissions of appropriate actions result in harm or significant risk of harm to patients. Consider the following case scenario:

A 19-year-old obese woman who had recently undergone C-section delivery of a baby presented in the emergency department (ED) with dyspnea. Believing the patient had developed a pulmonary embolism, the physician prescribed an IV heparin bolus dose of 5,000 units followed by a heparin infusion at 1,000 units/hour. After administering the bolus dose, a nurse started the heparin infusion but misprogrammed the pump to run at 1,000 mL/hour, not 1,000 units/hour (20 mL/hour). By the time the error was discovered, the patient had received more than 17,000 units (5,000 unit loading dose and about 12,000 units from the infusion) in less than an hour since arrival in the ED. A smart pump with dosing limits for heparin had been used. Thus, the programming error should have been recognized before the infusion was started. However, the nurse had elected to bypass the dose-checking technology and had used the pump in its standard mode. It was quite fortunate that the patient did not experience adverse bleeding as her aPTT values were as prolonged as 240 seconds when initially measured and 148 seconds two hours later. (Institute for Safe Medication Practices, 2007, para. 2)

The smart pump used in this scenario was equipped with dose calculation software that compares the programmed infusion rate to a drug database to check for dosing within safe limits. This technology is particularly important when high-alert or high-hazard drugs are being administered. In this case, however, the available dose-checking technology had been turned off and the pump was operated

in standard mode. A subsequent analysis of the error event revealed that many nurses in the institution were bypassing the safety technology afforded by the smart pump to save time.

This chapter focuses on some of the recommended organizational strategies used to promote a culture of safety and some of the specific informatics technologies designed to reduce errors and promote patient safety.

What Is a Culture of Safety? The 2000 Institute of Medicine report, To Err Is Human, is widely credited for launching the current focus on patient safety in health care. This report was followed in 2001 by the Institute of Medicine’s Crossing the Quality Chasm report, which brought to national attention healthcare quality and safety. This national attention resulted in a $50 million grant by Congress to the Agency for Healthcare Research and Quality (AHRQ) to launch initiatives focused on safety research for patients. Other initiatives prompted by these seminal reports were the Joint Commission’s National Patient Safety Goals (2002); the National Quality Forum’s adverse events and “never events” list (2002); the creation of the Office of National Coordinator for Health IT to computerize health care (2004); the formation of the World Health Organization’s Alliance for Patient Safety (2004); the Institute for Healthcare Improvement’s (IHI) 100,000 Lives campaign (2005) and 5 Million Lives campaign (2008); Congressional authorization of patient safety organizations created by the Patient Safety and Quality Improvement Act to promote blameless error reporting and shared learning (2005); the “no pay for errors” initiative launched by Medicare (2008); and the $19 billion Congressional appropriation to support electronic health records (EHRs) and patient safety (Wachter, 2010).

The AHRQ (2012) safety culture primer suggested that organizations should strive to achieve high reliability by being committed to improving healthcare quality and preventing medical errors and to demonstrate an overall commitment to patient safety. That is, everyone and every level in an organization must embrace the safety culture. Key features of a safety culture identified by the AHRQ are as follows:

Acknowledgment of the high-risk nature of an organization’s activities and the determination to achieve consistently safe operations

A blame-free environment where individuals are able to report errors or near misses without fear of reprimand or punishment Encouragement of collaboration across ranks and disciplines to seek solutions to patient safety problems Organizational commitment of resources to address safety concerns (AHRQ, 2012, para. 1)

An important part of the safety culture is cultivating a blame-free environment. Errors and near misses must always be reported so that they can be thoroughly analyzed. All organizations can learn from mistakes and change their organizational processes or culture to ensure patient safety. The Patient Safety and Quality Improvement Act of 2005 mandates the creation of a national database of medical errors and funded several organizations to analyze these data with the goal of developing shared learning to prevent medical errors. Organizations themselves can engage in root-cause analysis or failure modes and effects analysis to examine medical errors closely and to determine the system processes that need to be changed to prevent similar future errors (Harrison & Daly, 2009). A tool for implementing root-cause analysis developed by the National Center for Patient Safety is detailed at this website: http://www.patientsafety.gov/CogAids/RCA/index.html#page=page-1. Similarly, the IHI has a website dedicated to failure modes and effects analysis. “Failure Modes and Effects Analysis (FMEA) is a systematic, proactive method for evaluating a process to identify where and how it might fail, and to assess the relative impact of different failures in order to identify the parts of the process that are most in need of change” (IHI, 2011b, para. 1). This powerful tool and shared experiences from other organizations can be viewed at http://www.ihi.org/knowledge/Pages/Tools/FailureModesandEffectsAnalysisTool.aspx.

If one embraces a blame-free environment to encourage error reporting, then where does individual accountability fit in? According to the ARHQ, one way to balance these competing cultural values (blameless versus accountability) is to establish a “just culture” where system or process issues that lead to unsafe behaviors and errors are addressed by changing practices or workflow processes, and a clear message is communicated that reckless behaviors are not tolerated. The “just culture” approach accounts for three types of behaviors leading to patient safety compromises: (1) human error (unintentional mistakes); (2) risky behaviors (workarounds); and (3) reckless behavior (total disregard for established policies and procedures).

Strategies for Developing a Safety Culture Strategies for achieving a safety culture have been addressed frequently in the literature. The focus here is limited to those strategies described by two key organizations, the AHRQ and the IHI. The AHRQ (2012) provides access to data from two validated surveys, the Patient Safety Culture Survey and the Safety Attitudes Questionnaire. The AHRQ suggests that teamwork training, executive walk- arounds, and unit-based safety teams have improved safety culture perceptions but have not led to a significant reduction in error rates. The IHI (2011a) stresses that organizational leaders must drive the culture change by making a visible commitment to safety and by enabling staff to share safety information openly. Some of the strategies suggested by the IHI include appointing a safety champion for every unit, creating an adverse event response team, and reenacting or simulating adverse events to better understand the organizational or procedural processes that failed.

A systems engineering approach to patient safety, in which technology manufacturers partner with organizations to identify risks to patient safety and promote safe technology integration, is advocated by Ebben, Gieras, and Gosbee (2008). They note that human factors engineering is “The discipline of applying what is known about human capabilities and limitations to the design of products, processes, systems, and work environments.” Its application to system design improves “ease of use, system performance and reliability, and user satisfaction, while reducing operational errors, operator stress, training requirements, user fatigue, and product liability” (p. 327). For example, Ebben et al. describe the feel of an oxygen control knob that rotated smoothly between settings, suggesting to the user that oxygen flows at all points on the knob, when in fact oxygen flowed only at specifically designated liter flow settings. Human factors engineering testing would most likely reveal this design flaw, and the setting knob could be improved to

include discrete audio or tactile feedback (click into place) to the user to indicate a point on the dial where oxygen flows. Ebben et al. emphasize that testing human use factors provides more objective safety data than the subjective responses gained from user preference testing. “Understanding how the equipment shapes human performance is as important as evaluating reliability or other technical criteria” (p. 329). Organizations that are purchasing medical technology devices should avail themselves of shared safety data on equipment maintained by several key organizations, including the Joint Commission, the Food and Drug Administration, and the Medical Product Safety Network (MedSun; www.fda.gov/MedicalDevices/Safety/MedSunMedicalProductSafetyNetwork/default.htm).

Once the technology is integrated into the organization, biomedical engineers can become valuable partners in promoting patient safety through appropriate use of these technologies. For example, in one organization, the biomedical engineers helped to revamp processes associated with the new technology alarm systems after they discovered several key issues: slow response times to legitimate alarms and multiple false alarms (promoting alarm fatigue) created by alarm parameters that were too sensitive. Strategies for addressing these issues included improving the nurse call system by adding Voice over Internet Protocol telephones that wirelessly receive alarms directly from technology equipment carried by all nurses, thus reducing response times to alarms; feeding alarm data into a reporting database for further analysis; and encouraging nurses to round with physicians to provide input into alarm parameters that were too sensitive and were generating multiple false alarms (Williams, 2009). The Research Brief describes a study of intelligent agent (IA) technology to improve the specificity of physiologic alarms.

Clearly, there is more work to be done to create safety cultures in complex healthcare organizations and to reduce the incidence of errors. Many organizations are looking to informatics technology to help manage these complex safety issues by using smart technologies that provide knowledge access to users, provide automated safety checks, and improve communication processes.

Informatics Technologies for Patient Safety Healthcare technologies are frequently designed to improve patient safety, streamline work processes, and improve the quality and outcomes of healthcare delivery. Technology is not always the answer to patient safety, as the Joint Commission (2008) cautions: “the overall safety and effectiveness of technology in health care ultimately depends on its human users, and … any form of technology can have a negative impact on the quality and safety of care if it is designed or implemented improperly or is misinterpreted” (para. 2). Although technology may certainly help to prevent or reduce errors, one must always remember that technology is not a substitution for safety vigilance by the healthcare team in a safety culture.

Bates and Gawande (2003) urged the adoption of information technology (IT) processes to improve safety. They suggest that information technologies improve communication, reduce errors and adverse events, increase the rapidity of response to adverse events, make knowledge more accessible to clinicians, assist with decisions, and provide feedback on performance. They also describe the benefits of technology-based forcing functions that direct or restrict actions or orders implemented by computer technologies. For example, physicians may be forced to write complete and accurate medication orders, restricted from ordering an inappropriate dosing route for a medication, or prompted to write corollary orders that should be included in the care regimen as part of the standard of care.

The Wired for Health Care Quality Act of 2005 began a series of funding streams to promote health IT, promote sharing of best practices in health IT, and help organizations implement health IT (Harrison & Daly, 2009). Many early adopters opted to focus technology and safety initiatives on medication ordering and administration processes. Medication errors are the most frequent and the most visible errors because the medication administration cycle has many poorly designed work processes with several opportunities for human error. Thus computerized physician order entry (CPOE), automated dispensing machines, smart pump technologies for intravenous drug administration, and bar-code medication administration (BCMA) frequently preceded the adoption of the EHR in many institutions because of the costs associated with implementing these technologies. In an ideal world, the EHR would be adopted concurrently as part of an interoperable health IT system. In the early EHR systems, clinicians were prompted by electronic alerts reminding them of important interventions that should be part of the standard of care, but these alerts tended to be generalized and not patient specific—for example, “Did you check the allergy profile?” or “Has the patient received a pneumonia immunization?” These early alert and care reminders are now evolving into more sophisticated clinical decision support (CDS) systems to promote accurate medical diagnoses and suggest appropriate medical and nursing interventions based on patient data.

Research Brief The investigators in one study used simple reactive intelligent agent (IA) technology to develop and test decision algorithms for improving the sensitivity and specificity of physiologic alarms. The IA technology was tested in a 14-bed cardiothoracic unit over 28 days and was implemented in parallel to the usual physiologic patient monitor that provided measures such as systolic blood pressure, mean arterial pressure, central venous pressure, and cardiac index. Alarm data generated by both systems were compared and classified as to whether the alarm represented a true medical event requiring clinician intervention or a false-positive alarm. A total of 293,049 alarms were generated by the usual physiologic monitoring system, and 1,012 alarms were generated by the IA system after raw physiologic data were filtered using rule-based IA technology. The IA filtering system shows promise for improving the specificity of physiologic alarms and decreasing the number of false-positive alarms generated by artifacts, thus reducing the incidence of alert fatigue in clinicians.

Source: The full article appears in Blum, J., Kruger, G., Sanders, K., Gutierrez, J., & Rosenberg, A. (2009). Specificity improvement for network distributed physiologic alarms based on a simple deterministic reactive intelligent agent in the critical care environment. Journal of Clinical Monitoring and Computing, 23(1), 21–30. Retrieved from ProQuest Nursing & Allied Health Source (Document ID: 1848695561).

The National Patient Safety Foundation (2013) listed the top patient safety issues as wrong-site surgery, hospital-acquired infections, falls, hospital readmissions, diagnostic errors, and medication errors. Many of these issues can be prevented or detected in their early stages using informatics technologies. Other technologies designed to promote patient safety include wireless technologies for patient monitoring, clinician alerts, point-of-care applications, and radiofrequency identification (RFID) applications. Each of these is reviewed here, and the chapter concludes with a section discussing future technologies for patient safety.

Technologies to Support the Medication Administration Cycle

The steps in the medication administration cycle (assessment of need, ordering, dispensing, distribution, administration, and evaluation) have been relatively stable for many years. Each of the steps depends on vigilant humans to ensure patient safety, resulting in the five rights of medication administration: (1) the right patient, (2) the right time and frequency of administration, (3) the right dose, (4) the right route, and (5) the right drug. Human error can be related to many aspects of this cycle. Distractions, unclear thinking, lack of knowledge, short staffing, and fatigue are a few of the factors that cause humans to deviate from accepted safety practices and commit medication errors. Integration of technology into the medication administration cycle promises to reduce the potential for human errors in the cycle by performing electronic checks and providing alerts to draw attention to potential errors.

CPOE is an electronic prescribing system designed to support physicians and nurse practitioners in writing complete and appropriate medication and care orders for patients. When CPOE is part of an EHR with a CDS system, the medication order is electronically checked against specific data in the patient record to prevent errors, such as ordering a drug that might interact with a drug the patient is already taking, ordering a dose that is too large for the patient’s weight, or ordering a drug that is contraindicated by the patient’s allergy profile or renal function. Because it is impossible for, and unreasonable to expect, a clinician to remember each of the more than 600 drugs that require a dose adjustment in the case of renal dysfunction, for example, safe dosing parameters are provided by the CPOE (Bates & Gawande, 2003). In a stand-alone CPOE system without a CDS system, the medication orders are simply checked by the computer against the drug database to ensure that the dose and route specified in the order are appropriate for the medication chosen. Specific benefits of CPOE include the following:

Prompts that warn against the possibility of drug interaction, allergy, or overdose Accurate, current information that helps physicians keep up with new drugs as they are introduced into the market Drug-specific information that eliminates confusion among drug names that look and sound alike Improved communication between physicians and pharmacists Reduced healthcare costs caused by improved efficiencies (LeapFrog Group, 2008)

CPOE solves the safety issues associated with poor handwriting and unclear or incomplete medication orders. Orders can be entered in seconds and from remote sites, eliminating the use of verbal orders that are especially subject to interpretation errors. Orders are then transmitted electronically to the pharmacy, reducing the potential for the transcription errors commonly encountered in the paper-based system, such as lost or misplaced orders, delayed dosing, or unreadable faxes. Thus CPOE changes workflows for all clinical staff and physicians as well as health team communication patterns (Manor, 2010). As with any technology integration, introduction of CPOE is associated with a resistance to change and a learning curve to gain proficiency, and users must learn to trust the system. Manor urges careful planning and training during implementation with plenty of staff support. She also reports on the need for a paper-based backup system in the case of network or electrical outages or system maintenance.

The verification and dispensing functions of the pharmacy can also be assisted by technology. The pharmacist begins by verifying the allergy status of the patient and the medication reconciliation information to ensure that the new medication is compatible with other medication in the care regimen. This verification function is computer based, and the medication order is electronically checked via the knowledge database. If the order is verified as safe and appropriate, the pharmacist proceeds to the dispensing process. Barcode medication labeling at a unit dose level was mandated by the Food and Drug Administration in 2004, with targeted compliance to be achieved by 2006. A bar code is a series of alternating bars and spaces that represents a unique code that can be read by a special barcode reader. Bar-code technology spans both the medication dispensing and administration steps in the medication administration cycle. In the pharmacy, the bar code helps to ensure that the right drug and the right dose are dispensed by the pharmacy. Medications that are labeled with bar codes can also be dispensed by robots capable of reading the codes or by automated dispensing machines. In this way, bar-code technology helps with the processes of procurement, inventory, storage, preparation, and dispensing (Cohen, 2002).

The processes of drug storage, dispensing, controlling, and tracking are easily carried out via automated dispensing machines (also known as automated dispensing cabinets, unit-based cabinets, automated dispensing devices, and automated distribution cabinets). These devices have benefits for both the user and the organization, specifically in the areas of access security (especially with narcotics administration tracking), safety, supply chain, and charge functions (Institute for Safe Medication Practices, 2008).

Radiofrequency identifier (RFID) technology is rapidly gaining a foothold in healthcare technology and may soon be used in the medication administration cycle. Although more expensive than bar coding for packaging, the RFID tags are reprogrammable (Wicks, Visich, & Li, 2006) and issues associated with bar-code printing imperfections and bar-code scanner resolution can be mitigated (Snyder, Carter, Jenkins, & Frantz, 2010). As discussed later in this chapter, RFID technologies may also be an important component of a medication compliance system for patients.

BCMA systems help to ensure adherence to the five rights of medication administration. Whether BCMA is part of the larger EHR or a free-standing electronic medication administration system (eMAR), bar-code technology provides a system of checks and balances to ensure medication safety. The nurse begins by scanning his or her name badge, thereby logging in as the person responsible for medication administration. Next, the bar code on the patient’s identification bracelet is scanned, prompting the electronic system to pull up the medication orders. Next, the bar code on each of the medications to be administered is scanned. This technology check ensures that the five rights of medication administration are met. If there is a discrepancy between the order and the medication that was scanned or a contraindication for administration, an alert is generated by the system. For example, in an EHR system with CDS, the nurse may be prompted to check the most recent laboratory results for electrolytes before administering a potassium supplement. In a free-standing eMAR without CDS or EHR links, if the medication orders have recently been changed, the nurse is alerted to the change. When an alert is generated, the nurse must chart the action taken in response to that alert. For example, an early dose might need to be given if the patient is leaving the unit for a diagnostic test.

Despite the promising advances in patient safety afforded by this technology, it is not fail safe (Cochran, Jones, Brockman, Skinner, & Hicks, 2007). Medications that are labeled individually by the in-house pharmacist increase the potential for human error if the medication is given an incorrect bar code, such as one signifying a wrong dose or even the wrong medication. In addition, the bar- code printers themselves may generate unreadable labels, leading to staff workarounds in the interest of saving time. Cochran et al.

make the following recommendations to reduce BCMA errors:

Purchase unit-of-use medications with manufacturer bar codes whenever possible. Double-check all hospital-generated bar-code labels, including those for compounded injectable medications, before the product

leaves the pharmacy. Carefully review all BCMA override reports. Address system workarounds through process change and staff education. Minimize false-positive warnings to reduce the likelihood that staff will ignore warnings for real errors. Ensure that an urgent need exists for all “stat” orders, as pharmacy review and advantages of bar code administration are

usually circumvented in such cases. Establish institutional policies and procedures that can be easily implemented when products fail to scan. Processes in pharmacy

will likely be different than processes at the point of care (p. 300). Smart pump technologies are designed for safe administration of high-hazard drugs and to reduce adverse drug events during intravenous medication administration. Smart pumps have software that is programmed to reflect the facility’s infusion parameters and a drug library that compares normal dosing rates with those programmed into the pump. Discrepancies generate an alarm alerting the clinician to a safety issue. A soft alarm can typically be overridden by a clinician at the bedside, but a hard alarm requires the clinician to reprogram the pump so that the dosing falls within the facility’s intravenous administration guidelines for the drug to be infused. All alarms generated by the smart pump are tracked along with the clinician’s responses to them (Dulak, 2005; UAB, 2013). Smart pumps can be seamlessly integrated into BCMA systems, and data can be fed directly into the EHR. The IHI recommends the following steps to ensure safe implementation of smart pump technology:

Prior to deploying these pumps, standardize concentrations within the hospital. Asking the nurse to choose among several concentrations increases the risk of selection error.

Prior to deploying these pumps, standardize dosing units for a given drug (for example, agree to always dose nitroglycerin in terms of mcg/min or mcg/kg/min, but not both). Asking the nurse to choose among several dosing units increases the risk of selection error.

Prior to deploying these pumps, standardize drug nomenclature (for example, agree to always use the term KCl, but not potassium chloride, K, pot chloride, or others). Asking the nurse to remember and choose among several possible drug names increases the risk of selection error.

Perform a failure modes and effects analysis on the deployment of these devices. Ensure that the concentrations, dose units, and nomenclature used in the pump are consistent with that used on the medication

administration record (MAR), the pharmacy computer system, and the EHR. Meet with all relevant clinicians to come to agreement on the proper upper and lower hard and soft dose limits. Monitor overrides of alerts to assess whether the alerts have been properly configured or whether additional quality intervention

is required. Be sure the “smart” feature is utilized in all parts of the hospital. If the pump is set up volumetrically in the operating room but

the “smart” feature is used in the ICU, an error may occur if the pump is not properly reprogrammed. Be sure there are upper and lower dose limits for bolus doses, when applicable. Engage the services of a human factors engineer to identify new opportunities for failure when the pumps are deployed. Identify a procedure for the staff to follow in the event a drug that is not in the library must be given or when its concentration is

not standard. Deploy the pump in all areas of the hospital. If a different pump is used on one floor and the patient is later transferred, this will

create new opportunities for failure. Also, there may be incorrect assumptions about the technology available to a given floor or patient.

Consider using “smart” technology for syringe pumps as well as large-volume infusion devices (IHI, 2012, para. 7).

CDS can enhance the medication administration cycle by promoting safety and improving patient outcomes. Clinical decision making is guided by targeted information delivery ensuring that the five rights of CDS are implemented: the right information provided to the right person in the right format through the right channel at the right time in workflow. For example, during medication selection, a CDS helps a clinician select an appropriate medication based on client data, such as clinical condition, weight, renal function, concurrent medications, and cost. This system ensures that the order is complete by performing checks for drug interactions, duplications, or allergy contraindications and ensures the right dose and right route are specified. During the verification and dispensing phase of the medication administration cycle, the CDS provides double checks for interactions, allergies, and appropriate dose orders. Consideration is also given to potential infusion pump programming issues, incompatibilities during infusion, and proper notation and dispensing when portions of a dose must be wasted. During the administration phase, the CDS assists with patient identification and current assessment parameters (i.e., blood pressure, glucose level) that may contraindicate the use of the medication at that point in time. In addition, checks for interactions with foods or other medications and timing and monitoring guidelines are provided to the clinician administering the medication. The CDS has patient education guidelines and printable handouts to assist clinicians in educating patients about their medications. The monitoring functions of the CDS provide a structured data reporting system to track side effects and adverse events across the population (Healthcare Information and Management Systems Society [HIMSS], 2009a).

Several promising technologies may become available in the future to assist patients with medication compliance after discharge. For example, eMedonline collects patient medication compliance data by scanning package bar codes or RFID medication tags and using personal digital assistant or smartphone technology to send compliance data to the server. Clinicians review the medication compliance data and provide education and feedback to patients to increase their compliance with proper medication administration (eMedonline, n.d.). The SIMpill Medication Adherence System uses Web-based technology to monitor patient compliance and provide reminders about taking medications or refilling prescriptions by sending text messages to the patient or caregivers (SIMpill, 2008). Caps of pill bottles may contain RFID tags that monitor and collect data on when the bottle is opened, or that contain flashing time

reminders when a dose is due (Blankenhorn, 2010). Smart inhalers track asthma medication compliance using a microprocessor that records and stores medication compliance. They may also include visual and audio reminders to use the inhaler (Nexus 6, 2010). These are just a sampling of the newer technologies for medication adherence; more are expected to emerge in the future.

Additional Technologies for Patient Safety

CDS systems have safety uses beyond the medication administration cycle. The robust data collection and data management functions help to ensure quality approaches to patient health challenges based on research evidence and clinical guidelines. A CDS may also ensure cost-effectiveness by alerting clinicians to duplicate testing orders, or by suggesting the most cost-effective diagnostic test based on specific patient data (HIMSS, 2009b). Consider this description of the features of a CDS based on screen captures performed by a CDS system:

The patient is a 75-year-old male with coronary artery disease (CAD), diabetes mellitus (DM), and elevated creatine kinase (CK). Assessment prompts and reminders on the screen for this patient include: no recent LDL test; BP is above goal; patient is due for Pneumovax and influenza vaccines; patient is a current smoker, not thinking of quitting, last counseled with date; patient is overweight; patient is due for eye and ear checks. The patient management prompts include:

Lipid Management: “No Recent LDL Management” is printed red with a series of check boxes presenting choices to the clinician:

Order lipid panel now Order lipid panel with direct LDL now Print instructions for fasting lipid panel (link) Print orders for outside lab request for lipid panel testing (link)

BP management: BP is above goal average over last 2 visits; goal is 130/80 Choices on checkboxes: Start another antihypertensive (“help me choose”) link Series of links listing current medications with opportunities to adjust each

Order Chem 7 now or order Chem 7 in (drop-down menu for timing of order) Suggestions for referrals include: Refer to nutritionist

Refer to cardiac rehabilitation (“help me choose” link) Refer to BP specialist (“help me choose” link) Prompts for patient education handouts include: Print “control high blood pressure” link Print DASH diet instructions link Print exercise prescription (White, Shiffman, Middleton, & Cabán, 2008)

The prompts and instructions provided to the clinician by the system in this example are detailed and easy to navigate. As the example suggests, implementation of a CDS has the potential to optimize care by ensuring that all of the details of a patient’s health issues are presented to the clinician for management, thereby promoting individualized approaches to the total health of the patient based on best available evidence and clinical guidelines (HealthIT.gov, n.d.).

RFID technologies have both supply chain and patient care applications to patient safety. An RFID system contains a tag affixed to an object or to a person that functions as a radiofrequency transponder and provides a unique identification code, a reader that receives and decodes the information contained on the tag, and an antenna that transmits the information between the tag and the reader. When RFID tags are embedded in patient identification bracelets, they can help with patient tracking during procedures and testing or function as part of the EHR communicating pertinent information to clinicians at the bedside. RFIDs may be part of the medication administration process, replacing barcode technologies. They can be used to track medical supplies and equipment, thereby reducing staff time in locating such items. They may also be embedded into surgical supplies to automate supply-counting procedures, thereby reducing the likelihood that sponges or tools will be erroneously left in a patient. RFIDs may also reduce the likelihood of wrong- patient, wrong-site surgical procedures (Revere, Black, & Zalila, 2010). RFIDs used in the medication supply chain protect patients by reducing the potential that a counterfeit medication might be inadvertently introduced into the supply, and by providing for efficient medication recalls. Potential terrorist manipulation of the medication supply is also thwarted by RFID supply chain tracking technology. Blood and blood products can be efficiently tracked by RFIDs because specialized tags can detect temperature fluctuations and, therefore, ensure that the blood or blood product was stored at the optimal temperature for safe administration (Wicks et al., 2006).

Smart rooms are being tested for wider use in healthcare facilities. As a caregiver enters the room, the RFID tag on his or her name badge announces to the patient on a monitor (typically mounted on the wall in the patient’s line of sight) exactly who has entered the room and triggers “need to know” data based on caregiver status to be displayed on the monitor in the room. For example, when a dietary aide enters the room, only dietary information is displayed; when a physician or nurse enters, all of the pertinent medical data from the EHR is available. Clinicians can review patient data in real time and chart care at the bedside using touch-screen technology, thereby increasing productivity (Cronin, 2010). Some smart room technologies include workflow algorithms to alert clinicians as they enter the room about procedures that need to be implemented for the patient and can track individual clinician efficiency and effectiveness by aggregating data over time (Sharbaugh & Boroch, 2010).

New technologies to improve patient monitoring include wearable devices and wireless area networks, variously called “body area networks” or “patient area networks.” The technologies provide the ability to wear a small unobtrusive monitor that collects and transmits physiologic data via a cell phone to a server for clinician review. Although most of these technologies are designed for monitoring patients with chronic diseases, they also have safety implications because they help to identify early warning physiologic signs of impeding serious health events (California Healthcare Foundation, 2007). A wireless chip on a disposable Band-Aid with a 5- to 7-day battery promises to be able to monitor the patient’s heart rate and electrocardiogram, blood glucose, blood pH, and blood pressure, allowing for the collection of important clinical data outside the hospital (Miller, 2008). Wearable stress-sensing monitors detect electrical changes in the skin that may signal increased stress in autistic children who are unable to communicate an impending

crisis; caregivers are alerted to the potential crisis via wireless transmission and can intervene to reduce the stress and prevent the crisis (Murph, 2010). Several new technologies promise to aid in early detection of falls in the elderly, including a wearable pendant that triggers a personal emergency response system (Aging in Place Technology Watch, 2012) and smart slippers with pressure sensors in the soles that transmit movement data wirelessly to a remote monitoring site (Mobihealthnews, 2009).

Robotics technologies are also being increasingly tested for safety and efficiency uses. Robotics has been used in minimally invasive surgery for some time; however, newer devices are including haptic (tactile) feedback to the surgeon, thereby increasing the sense of reality during the procedure and reducing the potential for unsafe manipulation (June, 2010). A robot designed to assist with patient lifting promises increased safety for both patients and clinicians (Melanson, 2010). Finally, laser-guided robots are performing such routine functions as emptying and disposing of trash, cleaning rooms, delivering supplies and meals, and dispensing drugs (Savoy, 2010).

Role of the Nurse Informaticist The human side of patient safety is paramount. As technologies that can help to reduce errors and increase safety are integrated into caregiving activities, healthcare professionals must also improve their ability to use and manage these technologies. Therefore, not only must the technology be scrutinized and tested routinely, but the users must also be maintained and nurtured so that they are able to use the tools to the patient’s benefit, avoiding harm and keeping the patient safe. Even the best CDS systems can contribute to mistakes by providing meaningless or harmful information. Nurse informaticists and the IT team in the facility must ensure that all systems are properly configured and maintained. They should routinely monitor and check these systems while making sure that their human potential—that is, the users—is capable of using the systems accurately to avoid errors. A technology and its user can never be left to their own devices.

Human inputting activities must focus on patient safety to raise the appropriate issues and sound out solutions. Nurse informaticists must be involved in all stages of the system development life cycle, while maintaining a focus on safety. Safety concerns and remedies need to be analyzed, synthesized, and integrated throughout the system development life cycle to have a robust tool that provides meaningful information and enhances patient care while preventing errors and promoting patient safety. According to Effken and Carty (2002), “Creating a safe patient environment is a very complex issue that will require the combined knowledge and skill of clinical informaticists, informatics faculty, researchers, and system designers” (para. 16). The Research Brief describes the results of a survey on the impact of nurse informaticists on patient safety.

Research Brief

In 2009, HIMSS conducted an Informatics Nurse Impact Survey sponsored by McKesson (HIMSS, 2009). This Webbased survey yielded 432 acceptable responses over a 2-month period from December 2008 to February 2009.

One of the areas assessed was “value and impact of informatics nurse,” on a scale of 1 to 7, with 7 being the highest rating:

Respondents believe that informatics nurses involved in system analysis, design, selection, implementation and optimization of IT have the greatest impact on patient safety (6.21), workflow (6.17) and user/clinician acceptance (6.15). The area with the least impact was integration with other systems (6.03). These findings suggest the informatics nurse is a driver of quality of care and enhanced patient safety within their organization. (p. 2)

This demonstrates the belief that nurse informaticists can greatly improve patient safety. The nurse executives who responded rated the positive impact of nurse informaticists on patient safety at 6.36 out of 7.

In their conclusion, the researchers stated that:

The role of informatics nurses is not limited to IT; this research also suggests that informatics nurses play an instrumental role with regard to patient safety, change management and usability of systems as evidenced by their impact on quality outcomes, workflow, and user acceptance. These additional areas highlight the value of informatics nurses—their expertise truly translates to the adoption of more effective, higher quality clinical applications in healthcare organizations. (p. 11)

Source: The full article appears in HIMSS. (2009). Informatics nurse impact survey sponsored by McKesson. http://www.himss.org/files/HIMSSorg/content/files/HIMSS2009NursingInformaticsImpactSurveyFullResults.pdf

Summary Patient safety is an important and ubiquitous issue in health care. This chapter explored the characteristics of a safety culture and technologies designed to promote patient safety. The need to evaluate errors carefully to determine why and how they occurred and how workflow processes might be changed to prevent future errors of the same type was emphasized. Technology is changing rapidly, and the culture of sharing related to technology implementation, error reporting, and troubleshooting should prompt continuous process improvements. The key for organizations is to invest in their users and choose wisely so that the technologies they are adopting will be interoperable and easily upgradable as technologies and safety practices evolve.

Organizations must make a commitment to a safety culture in which everyone at every level is committed to patient safety at every moment. In an ideal world, everyone would first stop and think “Is this safe?” before every action, workarounds would not occur, and everyone would embrace rather than resist the technologies and workflow processes designed to promote patient safety. Table 16-1 provides a list of websites to watch for updates on patient safety technologies.

TABLE 16-16 PATIENT SAFETY WEBSITES

TITLE URL

AHRQ Patient Safety Network http://www.psnet.ahrq.gov/primerHome.aspx

National Patient Safety Foundation http://www.npsf.org/

National Center for Patient Safety http://www.patientsafety.va.gov/

Institute for Healthcare Improvement http://www.ihi.org/explore/patientsafety/Pages/default.aspx

Center for Patient Safety http://www.centerforpatientsafety.org/

QSEN Institute (Quality and Safety Education for Nurses) http://qsen.org/

THOUGHT-PROVOKING QUESTIONS

1. What are the current patient safety characteristics of your organizational culture? Identify at least three aspects of your culture that need to be changed with regard to patient safety, and suggest strategies for change.

2. Describe a current technology that you use in patient care that would benefit from human factors engineering concepts. What are some ways this technology should be improved?

3. Identify a workaround that you have used and analyze why you chose this risk-taking behavior over behavior that conforms to a safety culture.

References Agency for Healthcare Research and Quality (AHRQ). (2012). Patient safety primer: Safety culture. http://psnet.ahrq.gov/primer.aspx?primerID=5 Aging in Place Technology Watch. (2012). New more accurate fall detector helps seniors age in place safely.

http://www.ageinplacetech.com/pressrelease/new-more-accurate-fall-detectorhelps-seniors-age-place-safely Bates, D., & Gawande, A. (2003). Improving safety with information technology. New England Journal of Medicine, 348, 2526–2534. Blankenhorn, D. (2010). Can better tools overcome the medical compliance crazy? http://www.zdnet.com/blog/healthcare/can-better-tools-overcome-

the-medical-compliance-crazy/3925 California Healthcare Foundation. (2007). Healthcare unplugged: The evolving role of wireless technology. Retrieved November 2010 from

http://www.chcf.org/~/media/Files/PDF/H/PDF%20HealthCareUnpluggedTheRoleOfWireless.pdf Cochran, C., Jones, K., Brockman, J., Skinner, A., & Hicks, R. (2007). Errors prevented by and associated with bar-code administration systems. The

Joint Commission Journal on Quality and Patient Safety, 33(5), 293–301. http://www.scribd.com/doc/12844778/Errors-Prevented-by-and- Associated-With-BCMA

Cohen, M. (2002). Bar code labeling for drug products. Proceedings of the Food and Drug Administration’s July 26, 2002, public meeting. http://www.ismp.org/pressroom/viewpoints/FdaBarCoding.asp

Cronin, M. (2010). SmartRooms from IBM connect hospital staff to patient data. http://www.cerner.com/uploadedFiles/Content/Solutions/_White_Papers/Medical_Devices/NCH_Smart_Room_Whitepaper.pdf

Dulak, S. (2005). Technology today: Smart IV pumps. http://www.modernmedicine.com/modernmedicine/article/articleDetail.jsp?id=254828 Ebben, S., Gieras, I., & Gosbee, L. (2008). Harnessing hospital purchase power to design safe care delivery. Biomedical Instrumentation & Technology,

42(4), 326–331. Retrieved from ProQuest Nursing & Allied Health Source (Document ID: 1548954831). Effken, J., & Carty, B. (2002). The era of patient safety: Implications for nursing informatics curricula. Journal of the American Medical Informatics

Association, 9(6 suppl 1). http://www.ncbi.nlm.nih.gov/pmc/articles/PMC419434/ eMedonline. (n.d.). How eMedonline works. Retrieved November 2010 from http://www.emedonline.com/about.asp?topic=howitworks Harrison, J., & Daly, M. (2009). Leveraging health information technology to improve patient safety. Public Administration and Management, 14(1),

218–237. Retrieved from ABI/INFORM Global (Document ID: 1685699891). Healthcare Information and Management Systems Society (HIMSS). (2009a). Approaching CDS in medication management. Retrieved November 2010

from http://healthit.ahrq.gov/images/mar09_cds_book_chapter/CDS_MedMgmnt_ch_1_sec_3_applying_CDS.htm Healthcare Information and Management Systems Society (HIMSS). (2009b). Clinical decision support (CDS) fact sheet. Retrieved November 2010

from http://www.himss.org/content/files/CDSFactSheet3-17-09.pdf HealthIT.gov. (n.d.). CDS Implementation. http://www.healthit.gov/policy-researchersimplementers/cds-implementation Institute of Medicine (IOM). (2000). To err is human: Building a safer health system. L. Kohn, Corrigan, & M. Donaldson (Eds.). Washington, DC:

Committee on Quality of Health Care in America, National Academies Press. http://psnet.ahrq.gov/resource.aspx?resourceID=1579 Institute of Medicine (IOM). (2001). Crossing the quality chasm: A new health system for the 21st century. Committee on Quality of Health Care in

America, Institute of Medicine. Washington, DC: National Academies Press. http://psnet.ahrq.gov/resource.aspx?resourceID=1564 Institute for Healthcare Improvement (IHI). (2011a). Develop a culture of safety.

http://www.ihi.org/knowledge/Pages/Changes/DevelopaCultureofSafety.aspx Institute for Healthcare Improvement (IHI). (2011b). Failure modes and effects analysis tool.

http://www.ihi.org/knowledge/Pages/Tools/FailureModesandEffectsAnalysisTool.aspx Institute for Healthcare Improvement (IHI). (2012). Reduce adverse drug events (ADES) involving intravenous medications: Implement smart infusion

pumps. http://www.ihi.org/knowledge/Pages/Changes/ReduceAdverseDrugEventsInvolvingIntravenousMedications.aspx Institute for Safe Medication Practices. (2007, April 19). Smart pumps are not smart on their own.

http://ismp.org/Newsletters/acutecare/articles/20070419.asp Institute for Safe Medication Practices. (2008). Guidance on the interdisciplinary safe use of automated dispensing cabinets.

http://www.ismp.org/tools/guidelines/ADC_Guidelines_Final.pdf Joint Commission. (2008). Sentinel event alert #42: Safely implementing health information and converging technologies.

http://www.jcrinc.com/Sentinel-Event-Alert-42/ June, L. (2010). Sofie surgical robot gives haptic feedback for a more humane touch. http://www.engadget.com/2010/10/11/sofie-surgical-robot-gives-

haptic-feedback-for-a-more/ LeapFrog Group. (2008). Fact sheet: Computerized physician order entry. http://www.leapfroggroup.org/media/file/FactSheet_CPOE.pdf Manor, P. (2010). CPOE: Strategies for success. Nursing Management, 41(5), 18. Retrieved from ABI/INFORM Global (Document ID: 2044925551). Melanson, D. (2010). Yurina health care robot promises to help lift, terrify patients. http://www.engadget.com/2010/08/13/yurina-health-care-robot-

promises-to-help-lift-terrify-patients/ Miller, P. (2008). Wireless chip on a Band-Aid to monitor patients from home. http://www.engadget.com/2008/02/05/wireless-chip-on-a-band-aid-to-

monitor-patients-from-home/ Mobihealthnews. (2009). AT&T develops “smart slippers” for fall prevention. http://mobihealthnews.com/5675/att-develops-smart-slippers-for-fall-

prevention/ Murph, D. (2010). Affectiva’s Q Sensor wristband monitors and logs stress levels, might bring back the snap bracelet.

http://www.engadget.com/2010/11/02/affectivas-q-sensor-wristbandmonitors-and-logs-stress-levels/ National Patient Safety Foundation. (2013). Definitions and hot topics. http://www.npsf.org/for-healthcare-professionals/resource-center/definitions-and-

hot-topics/ Nexus 6. (2010). What are smart inhalers? http://www.smartinhaler.com/ Revere, L., Black, K., & Zalila, F. (2010). RFIDs can improve the patient care supply chain. Hospital Topics, 88(1), 26–31. Retrieved from Health

Module (Document ID: 2119405721). Savoy, V. (2010). Robots to invade Scottish hospital, pose as “workers.” http://www.engadget.com/2010/06/21/robots-to-invade-scottish-hospital-pose-

as-workers/ Sharbaugh, D., & Boroch, M. (2010). Hospital smart rooms are ready for rollout. http://www.buildings.com/tabid/3413/ArticleID/9675/Default.aspx SIMpill. (2008). The SIMpill medication adherence solution. http://www.simpill.com/thesimplesolution.html Snyder, M., Carter, A., Jenkins, K., & Frantz, C. (2010). Patient misidentifications caused by errors in standard barcode technology. Clinical Chemistry,

56(10), 1554–1561. University of Alabama at Birmingham (UAB). (2013). UAB Medicine: The connected hospital. http://www.uabmedicine.org/news/news-nursing-the-

connected-hospital Wachter, R. (2010). Patient safety at ten: Unmistakable progress, troubling gaps. Health Affairs, 29(1), 165–173. Retrieved from ABI/INFORM Global

(Document ID: 194983018). White, J., Shiffman, R., Middleton, B., & Cabán, T. (2008). A national web conference on using clinical decision support to make informed patient care

decisions. Slide 63: Partners CDS services: CAD/DM smart, Form Slides 63–65. Presented at Agency for Healthcare Research and Quality, September 19, 2008. Retrieved November 2010 from http://healthit.ahrq.gov/images/sep08cdswebconference/textmostly/

Wicks, A., Visich, J., & Li, S. (2006). Radio frequency identification applications in healthcare. International Journal of Healthcare Technology and Management, 7(6), 522–540.

Williams, J. (2009). Biomeds’ increased involvement improves processes, patient safety. Biomedical Instrumentation & Technology, 43(2), 121–3. Retrieved from ProQuest Nursing & Allied Health Source (Document ID: 1692747971).

Chapter 17

Supporting Consumer Information and Education Needs Kathleen Mastrian and Dee McGonigle

OBJECTIVES

1. Define health literacy and e-health. 2. Explore various technology-based approaches to consumer health education. 3. Identify barriers to use of technology and issues associated with healthrelated consumer information. 4. Imagine future approaches to technology-supported consumer health information.

Key Terms

Blog Digital divide Domain name E-brochure E-health eHealth Initiative Empowerment Gray gap Health literacy HONcode Interactive technologies Know–do gap Static medium Trust-e Voice recognition Web quest Weblog

Introduction Imagine that you have decided to take up running as your preferred form of exercise in a quest to get in shape. You start slowly by running a half mile and walking a half mile. You gradually build up your endurance and find yourself running nearly every day for longer distances and longer periods of time. But then you notice a nagging pain first in your right hip; over a few weeks, it gradually spreads to the center of your right buttocks and then down your right leg. You try rest and heat, but nothing seems to help. You visit your doctor, and she indicates that you have developed piriformis syndrome and prescribes a series of stretching exercises, ice to the involved area, and rest. You are intrigued by the diagnosis. Upon your return home, you log on to the Internet and begin a search for information about piriformis syndrome. When you type the words into your favorite search engine, you get 484,000 results in response to your query.

Your use of the Internet to seek health information mirrors the behavior of many consumers, who are increasingly relying on the Internet for health-related information. The challenge for consumers and healthcare professionals alike is the proliferation of information on the Internet and the need to learn how to recognize when information is accurate and meaningful to the situation at hand.

This chapter explores consumer information and education needs and considers how technology may help to meet those needs, yet at the same time create everincreasing demands for health-related information. It begins with a discussion of health literacy, e-health, and health education and information needs and explores various approaches by healthcare providers to using technology to promote health literacy. Also examined is the use of games, Web quests, and simulations as means of increasing health literacy among the school-age population. Issues associated with the credibility of Web-based information and barriers to access and use are discussed. Finally, future trends related to technology-supported consumer information are explored.

Consumer Demand for Information This is the Knowledge Age; many people want to be in the know. People demand news and information, and they want immediate results and unlimited access. This is increasingly true with health information. More and more people, in a trend known as consumer empowerment, are interested in taking control of their health and are not satisfied being dependent on a healthcare provider to supply them with information.

The Pew Internet and American Life Project survey report of 2013 (Fox, 2013) indicates that 8 in 10 (comparable to the numbers in previous surveys conducted in 2006 and 2011) Americans who are online have searched for health information. The most frequent health topic searches (69%) are related to a specific disease or medical problem that the searcher or a member of the family is experiencing. Other frequent topics of health-related searches are weight, diet, and exercise (60%) and health indicators such as blood parameters or sleep patterns (33%). The 2011 survey (n = 3,001) reports that consumers also searched for information on food (29%) and drug safety (24%) (Fox, 2011). A 2006 Pew Internet survey tracked 8 million searches for health information by American adults in a single day and suggested that most begin their searches with a search engine. The impacts of the searches were reported as both positive and negative. These impacts ranged from affecting decisions about their care (58%), changing their approaches to overall health maintenance (55%), and providing material for posing questions to healthcare providers (54%), to feeling overwhelmed (25%) or confused (18%) by materials they found online about their health (Fox, 2006).

It is important to note that these surveys are limited to those individuals who are online and do not reflect the health information needs or demands of those persons who are not online. Digital divide is the term used to describe the gap between those who have and those who do not have access to online information. Nurses and healthcare providers need to be aware of the various components of the digital divide to ensure that patients and clients are receiving the health information they need in a format that they are interested in and can comprehend. Notably, persons with chronic diseases are less likely to have Internet connectivity. Fox and Purcell (2010) explain the disparity in that having a chronic disease is associated with increased age, level of education, ethnicity, and income—all factors also associated with the digital divide. Persons living with a chronic disease who have Internet access are likely to use the Internet for blogging and online discussion forums, activities popularly referred to as peer-to-peer support.

Missen and Cook (2007) discuss the potential impact that technology-based health information dissemination can have on the know–do gap in developing countries. The know–do gap reflects the fact that solutions to global health problems exist but are not implemented in a timely fashion because of the lack of access to important health information. The Internet connections in developing countries are widely scattered and may not be efficient or sufficient for viewing healthcare information. Missen and Cook describe the use of a freestanding hard drive loaded with hundreds of CDs of health-related information in a webpage format that responds to a search command. This is a great example of providing technologies that work with the constraints of the situation. Another example of addressing the digital divide is the growing number of health-related websites that support a Spanish language format.

Health Literacy and Health Initiatives The goal of health literacy for all is one that is widely embraced in many sectors of health care; it was a major goal of Healthy People 2010, and is being continued in the health communication and health information technology objective of Healthy People 2020 (Office of Disease Prevention and Health Promotion & U.S. Department of Health and Human Services, 2009). Clinicians who have been practicing for some time recognize that informed patients have better outcomes and pay more attention to their overall health and changes in their health than those who are poorly informed. Some of the earliest formally developed patient education programs, which included postoperative teaching, diabetes education, cardiac rehabilitation, and diet education, were implemented in response to research that suggested the positive impact of patient education on health outcomes and satisfaction with care. Glassman (2008) updated the National Network Libraries of Medicine webpage on health literacy (http://nnlm.gov/outreach/consumer/hlthlit.html). She concluded from the research on the economic impact of health literacy that those persons with low health literacy have less ability to manage a chronic illness properly and tend to use more healthcare services than those who are more literate. In addition, she used results of health research to demonstrate the impact of low health literacy and the incidence of disease.

The site states that “Health Literacy is defined in the Institute of Medicine report Health Literacy: A Prescription to End Confusion as ‘The degree to which individuals have the capacity to obtain, process, and understand basic health information and services needed to make appropriate health decisions’” (Glassman, 2008, para. 2). For example, healthcare providers depend on a patient’s ability to understand and follow directions associated with preparation for surgery or taking medications. It is also assumed, sometimes erroneously, that people will correctly interpret symptoms of a serious illness and act appropriately. The ability to locate and evaluate health information for credibility and quality, to analyze the various risks and benefits of treatments, and to calculate dosages and interpret test results are among the tasks Glassman identifies as essential for health literacy. Other important and less easily learned health literacy skills are the ability to negotiate complex healthcare environments and understand the economics of payment for services. Parker, Ratzan, and Lurie (2003) estimated that at least one third of all Americans have health literacy problems and lament that in a time-is-money economic climate, healthcare practitioners are not always reimbursed for patient education activities. This is still true today.

The eHealth Initiative was developed to address the growing need for managing health information and to promote technology as a means of improving health information exchange, health literacy, and healthcare delivery. The eHealth Initiative website provides more information (http://www.ehealthinitiative.org/). Although the scope of the eHealth Initiative goes beyond health literacy, a major goal continues to be empowering consumers to understand their health needs better and to take action appropriate to those needs. Poor interoperability among healthcare systems and failure to embrace national data standards for health care continue to be identified as barriers to the eHealth Initiative. Further, concerns about privacy and security of information and the failure to invest appropriately in technology have slowed the development of this important initiative. Several states developed viable health information networks as part of the eHealth Initiative: the Utah Health Information Network (http://www.uhin.org) and the Vermont Health Information Technology Plan (http://hcr.vermont.gov/sites/hcr/files/pdfs/vermont_health_it_plan.pdf) are examples of attempts to implement e- health initiatives. The Centers for Disease Control and Prevention (CDC, 2012) maintains an interactive map of the United States that

provides access to health literacy and e-health initiatives by state (http://www.cdc.gov/healthliteracy/).

Healthcare Organization Approaches to Education Healthcare organizations (HCOs) use a wide variety of approaches and tools to promote patient education and health literacy. Although the old standby for disseminating information is the paper-based flyer, some HCOs are recognizing that today’s consumers are more attracted to a dynamic rather than static medium. In addition, the cost of designing and printing pamphlets and flyers becomes prohibitive when one considers the rapidity of change of information; the brochure may be outdated almost as soon as it is printed. One approach to deal with these issues is to have patient education information stored electronically so that changes can be made as needed or information can be better tailored to the specific patient situation and then printed out and reviewed with the patient.

Another old standby approach that is still widely used is the group education class. These classes initially were developed to help people manage chronic health problems (e.g., diabetes) and were typically scheduled while people were hospitalized. Now, many HCOs also sponsor health promotion education classes as a way of marketing their facilities and showcasing some of their expert practitioners.

The movement from static to dynamic presentations began in many HCOs as DVDs and videotapes that were shown in groups or broadcast on demand over dedicated channels via television in patients’ rooms. HCOs are now also taking advantage of the fact that patients and families are captive audiences in waiting rooms by promoting education via pamphlet distribution, health promotion programs broadcast on television, and health information kiosks in those locations. The kiosks are typically computer stations and often contain a variety of self-assessment tools (especially those related to risks for diabetes, heart disease, or cancer) and searchable pages of information about specific health conditions. The self-assessment tools represent yet another step forward in technological support for education: In addition to being dynamic, the kiosk is interactive. On the assessment page, the user is asked to respond to a series of questions and then the health risk is calculated by the computer program. One caution, however, is that just because the information is made available, it does not mean that people will participate or that they will understand what they have experienced. Issues related to the level of health literacy, the digital divide, and the gray gap still exist in these situations.

Many HCOs have invested time and money in developing interactive websites and believe that Web presence is a critical marketing strategy. Sternberg (2002) suggests that many websites begin as an e-brochure and progress through various stages to reach a true e-care status. Most offer physician search capabilities, e-newsletters, and call-center tie-ins. As with all patient education materials, there must be a sincere commitment to keeping information current and easily accessible. Web designers must pay particular attention to the aesthetics of the site, the ease of use, and the literacy level of those in the intended audience.

A usability study conducted by Lauterbach (2010) provides some insights into how to measure website usability. This researcher compared the usability of the symptom checker functions of two popular websites by asking volunteers to navigate each using four different case scenarios. Users navigated the site to find the symptom checker and then entered the symptoms and evaluated site feedback. Users rated the ease of understanding for each site and completed a short comprehension quiz. Data were collected on descriptions of user site preferences, user satisfaction with the sites, results of the comprehension quiz, and efficiency, which was measured by tracking the number of webpage changes the user performed while navigating the site.

The rapid growth of electronic communication through increased use of computers and access to the Internet, particularly for medical purposes, empowers the clinician as well as the consumer of healthcare information. The integration of information and communication technologies (ICTs) and the growing trend of consumer empowerment have reshaped the delivery of health care. Some HCOs have developed secure patient portals that allow patients to access their health records, including tracking laboratory results and reviewing the records. Most HCOs, however, do not allow patients to edit these records. Many people are interested in interacting with others who have the same or similar conditions, and some HCOs are providing the information necessary to help them connect. This so-called peer-to-peer support is especially popular with patients who have cancer diagnoses, diabetes, and other chronic and debilitating conditions (Lober & Flowers, 2011).

Research Brief Case studies of educational videos used on six hospital websites were the focus of this qualitative study by Huang (2009). This researcher used both within-case and cross-case analyses to describe rationales for developing and using e-health videos. He identified six main reasons for implementing e-health videos:

1. The proliferation of high-speed Internet access has made online video a well-accepted culture in the United States. 2. Many online visitors, especially young people, like to watch videos rather than read lengthy articles. 3. Videos can draw online visitors’ attention, thereby attracting online traffic and further attracting visitors to the hosting hospital. 4. A video, when well produced, is worth more than a thousand words. 5. Videos can relate not only to online visitors’ minds but also, and more importantly, to their hearts to build trust and drive their decision making. 5. Videos empower and inform the visitors outside of the hospital visit time, and such self-driven homework is beneficial to both the visitor and

the hospital (pp. 67–68).

Source: The full article appears in Huang, E. (2009). Six cases of e-health videos on hospital web sites. E-Service Journal, 6(3), 56–72. Retrieved from ABI/INFORM Global (Document ID: 1945312291).

Some HCOs are using social media as health education tools to promote actual engagement of audiences rather than as means of one-way messaging. Neiger, Thackeray, Burton, Giraud-Carrier, and Fagen (2013) suggested “that the use of social media in health promotion must lead to engagement between the health promotion organization and its audience members, that engagement must provide mutual benefit, and that an engagement hierarchy culminates in program involvement with audience members in the form of partnership or participation (as recipients of program services)” (p. 158). The CDC (2011) has an excellent social media tool kit that can be used by health educators to guide the planning and implementation of social media strategies for health promotion. This tool kit

can be accessed at the following website: http://www.cdc.gov/socialmedia/Tools/guidelines/pdf/SocialMediaToolkit_BM.pdf.

Promoting Health Literacy in School-Aged Children Promoting health literacy in school-aged children presents special challenges to health educators. There is wide agreement that childhood obesity is a serious and growing issue, which is related not only to poor choice of foods, but also to the sedentary lifestyles promoted by video games and television. In addition, the time once devoted to health and physical education programs in schools has given way to more time spent on core subjects, such as math and science.

The Children’s Nutrition Research Center responded early to these challenges by supporting the development of nutrition education programs as interactive computer games, video games, and cartoons referred to as “edutainment” (Flores, 2006). These e- health programs are developed specifically to appeal to the generational (highly connected and computer literate) and cultural needs of this group. Flores describes the Family Web project, which uses comic strips to impart nutrition information, and Squires Quest, where the students earn points by choosing fruits and vegetables to fight the snakes and moles that are trying to destroy the healthy foods in the Kingdom of SALot. These are great examples of health education programs that are designed to appeal to this connected generation of learners and their intuitive ability to use interactive technologies.

Donovan (2005) describes an interdisciplinary Web quest designed to appeal to older school-aged children. The quest is interdisciplinary, in that it requires reading comprehension, critical thinking, presentation, and writing; thus core skills and health literacy skills are learned in a single assignment. Students are directed to the Web to search for information on the pros and cons of low-carbohydrate diets and obesity prevention. Students learn along the way as they search for information, collect and interpret it, and then develop a presentation and final paper.

The Cancer Game (Oda & Kristula, n.d.) was developed by a young man taking a college class on Macromedia software and who had previously undergone a bone marrow transplant. Subsequently, he and a professor collaborated and expanded the project to its present form. The game is designed as an arcade-style video game for cancer patients to relieve stress by visualizing the fighting of cancer cells. Although cancer victims of any age can access and play the game, it has a special appeal to children and adolescents. Find the game here: http://www.cancergame.org/. Similarly, Ben’s Game (http://www.makewish.org/site/pp.asp? c=cvLRKaO4E&b=64401) is a video game designed to help relieve the stress of cancer treatment for children (Anderson & Klemm, 2008).

Games have also been designed for other conditions, such as diabetes (Glymetrix, 2009). You can access these newer games on the Internet by typing “health games” into a search engine. Be sure to review the information presented in the game for accuracy before you recommend it to parents and children.

Supporting Use of the Internet for Health Education Nurses and other healthcare providers need to embrace the Internet as a source of health information for patient education and health literacy. Patients are increasingly turning there for instant information about their health maladies. Health-related blogs (short for weblog, an online journal) and electronic patient and parent support groups are also proliferating at an astounding rate. Clinicians need to be prepared to arm patients with the skills required to identify credible websites. They also need to participate in the development of well-designed, easy-to-use health education tools. Finally, they need to convince payers of the necessity of health education and the powerful impact education has on promoting and maintaining health. Box 17-1 provides more information about health education.

BOX 17-1 CONSIDERATIONS FOR PATIENT EDUCATION Julie A. Kenney and Ida Androwich Nurses need to take many things into account when teaching patients. They need to assess patients’ willingness to learn, their reading ability, the means by which they learn best, and their existing knowledge about the subject. Nurses also need to take cultural differences and language differences into account when teaching their patients. If the nurse chooses to use an electronic method to educate the patient, digital natives (patients who have grown up with technology) need to be taught differently than digital immigrants (those who have been forced to learn technology) (“Educational Strategies,” 2006). Digital natives are typically born after 1982 and may also be referred to as “Generation Y.” This generation prefers to learn using technology. The younger group learns quite well if information is presented in a format to which they are accustomed, such as an interactive video game to introduce them to a topic. This group is also comfortable using information that they can access via their iPods and MP3 players (Maag, 2006). Those born before 1982 have learning styles that range from preferring to learn in a classroom setting to reading a book about the topic to learning using a hands-on, interactive approach (“Educational Strategies,” 2006).

REFERENCES Educational strategies in generational designs. (2006). Progress in Transplantation, 16(1), 8–9. Maag, M. (2006). Pod casting and MP3 players: Emerging education technologies. CIN: Computers, Informatics, Nursing, 24(1), 9–13.

PATIENT EDUCATION WEBSITES American Academy of Family Physicians: http://www.familydoctor.org American Cancer Society: http://www.cancer.org American Heart Association: http://www.americanheart.org Centers for Disease Control and Prevention: http://www.cdc.gov Krames (products to purchase): http://www.krames.com Merck (products to purchase and free information for patients):

http://www.merckservices.com/portal/site/merckservices/&tcode=K09FA Thomson—Educational products for nurses (requires a subscription):

http://www.micromedex.com/products/nurses and http://www.micromedex.com/products/carenotes/cn_brochure.pdf

The Health on the Net (HON) Foundation (2005) survey describes the certifications and accreditation symbols that identify trusted

health sites. The HONcode and Trust-e were identified as the two most common symbols that power users look for. The survey also indicated that Internet users look at the domain name and frequently gravitate toward university sites (.edu), government sites (.gov), and HCO sites (.org). Half of the survey respondents were in favor of the use of a domain name called .health to identify quality health information websites. In contrast, Pew/Internet (2006) indicates that nearly 75% of online searchers do not check the date or the source of information they are accessing on the Web and 3% of online health seekers report knowing someone who was harmed by following health information found on the Web.

The U.S. National Library of Medicine and the National Institutes of Health jointly sponsor MedlinePlus, a website that has a tutorial for learning how to evaluate health information and an electronic guide to Web surfing that is available in both English and Spanish. This site is found at http://www.nlm.nih.gov/medlineplus/webeval/webeval.html. A similar guide explains the major things one should evaluate when accessing health-related resources on the Web (National Center for Complementary and Alternative Medicine, 2008) and can be accessed at http://nccam.nih.gov/health/webresources. Suggest that patients visit these sites to become more adept at identifying whether a website is credible before they adopt the recommendations provided.

Clearly, the clinician is very important in patient education. Refer to Boxes 17-1 and 17-2 to review effective education methods used in teaching patients and their families.

BOX 17-2 A CLINICIAN’S VIEW ON PATIENT EDUCATION Denise D. Tyler Knowledge dissemination in nursing practice includes sharing information with patients and families so that they understand their healthcare needs well enough to participate in developing the plan of care, make informed decisions about their health, and ultimately comply with the plan of care, both during hospitalization and as outpatients.

There are several effective methods for educating patients and their families. Providing one-on-one and classroom instruction are traditional and valuable forms of education. One-on-one education is interactive and can be adjusted at any time during the process based on the needs of the individual patient or family; it can also be supplemented by written material, videos, and Web-based learning applications. Classroom education can be beneficial because patients and families with similar needs or problems can network, thereby enhancing the individual experience. However, the ability to interact with each member of the group and to tailor the educational experience based on individual needs may be limited by the size and dissimilarities of the group. Individual follow-up should be available when possible.

Paper-based education that is created, printed, and distributed by individual institutions or providers can be very effective because materials can be distributed at any time and reviewed when the patient feels like learning. Many agencies, such as the Centers for Disease Control and Prevention, have education for patients available on their websites. These documents can be reviewed online, or they can be printed out by healthcare providers or patients. Organizations can also develop and distribute information and instructions specific to their policies and procedures. In addition, printed educational material can be purchased from companies that employ experts in the subject matter and instructional design.

One of the newer sources of patient education information is the Internet. Many hospitals and healthcare organizations provide proprietary information, such as directions, information on procedures, and instructions on what to expect during hospitalization, in this manner. Other health organizations, such as the National Institutes of Health, provide detailed information on their websites. Clinicians should be cautious when recommending websites to patients and families, because not all sites are reliable or valid.

Many companies that provide clinical information systems also include patient education materials linked to the clinical system via an intranet. Thus standardized instructions that are specific to a procedure or disease process can be printed from this computer-based application. Discharge instructions that are interdisciplinary and patient specific can often be modified via drop-down lists or selectable items that can be deleted or changed by the clinician. This ability to modify before printing provides more consistent and individualized instruction. The computer-based generation of instruction is preferable to free text and verbal instruction modification because it also allows the information to be linked to a coded nursing language and, therefore, easily used for measurement and quality assurance reporting. Relevant triggers may be embedded in the clinical information systems. For example, when a patient answers “yes” to a question about current smoking, smoking cessation information should automatically be printed, or a trigger should remind the nurse to explore this topic with the patient and then provide the patient with preprinted information on smoking cessation.

Integration of standardized discharge instructions and patient education into the clinical system is another way to improve the compliance and documentation of education; it also streamlines the workflow of clinicians. Printing the information to give to the patient should be seamless to the clinician who is charting. The format should be logical and easy to read. The more transparent the process, the more efficient the system and the easier it is to use for the clinician. What I envision for the future is a system that “remembers” the style of learning preferred by patients and their families, prompts the provider to print handouts, and programs the bedside computer/video education system based on previous selections and surveys. This interactive patient and family education will be integrated into the clinical system and the patient’s personal health record.

Some providers have developed a list of credible websites and apps that are shared with patients or family members. Recommendations for websites might include the U.S. Department of Health and Human Services–sponsored healthfinder site (http://www.healthfinder.gov), a website dedicated to helping consumers find credible information on the Internet. Other excellent sources of reliable information are the National Institutes of Health (http://www.nih.gov), the Centers for Disease Control and Prevention (http://www.cdc.gov), Medline Plus (www.medlineplus.gov), NIHSeniorHealth (www.nihseniorhealth.gov), and the National Health Information Center (http://www.health.gov/nhic). Some of the apps that might be recommended include Mayo Clinic on Pregnancy (https://itunes.apple.com/WebObjects/MZStore.woa/wa/viewSoftware?id=656908781&mt=8), WebMD Pain Coach (https://itunes.apple.com/us/app/webmd-pain-coach/id536303342?mt=8), and Understanding Diseases (https://itunes.apple.com/us/app/understanding-diseases/id530900371?mt=8). These are great examples of the wealth of patient information being developed as apps by hospitals and other health care providers.

Future Directions Predicting future directions for technology-based health education is somewhat difficult, because one may not be able to completely envision the technology of the future. One can predict, however, that some current technologies will be used increasingly to support health literacy. For example, audio and video podcasts may become more commonplace in health education and be provided as free downloads from the websites of HCOs.

Abreu, Tamura, Sipp, Keamy, and Eavey (2008) described the processes used to create patient and family surgical education video podcasts. They began by digitally recording actual pediatric otologic surgeries, and then edited the videos, developed a script, and finally recorded the audio. They report that the educational podcasts were developed in 8–10 hours (excluding the surgical recording time). These podcasts are used to supplement the traditional face-to-face patient and family education sessions for pediatric otologic surgeries.

Voice recognition software used to navigate the Web may reduce the frustration and confusion associated with attempting to spell complex medical terms. However, the confusion and frustration may increase if the patient or client is unable to pronounce the terms. Voice interactivity should help to reduce the digital access disparity associated with those who have limited keyboard or mouse skills. For those persons with visual impairments, some websites may provide both audio and text information and support increased text size for greater ease of reading (Anderson & Klemm, 2008).

Many websites associated with government and national organizations are also providing multiple-language access to health information and decision-support tools. The multilanguage access broadens the population to whom education can be provided, and the decision-support programs allow users to access results that are tailored to their age, risk factors, or disease state (Anderson & Klemm, 2008).

Those individuals who are frequent e-mail users may be interested in being able to communicate with physicians and other healthcare personnel via e-mail rather than the telephone. This idea may meet with some resistance from physicians who perceive the e-mail correspondence as bothersome and time consuming. However, it is possible that work efficiency might also increase if patients and their needs are screened via e-mail before an office visit. For example, as a result of an e-mail correspondence in lieu of an initial office visit, medications could be changed or diagnostic tests could be performed before the office visit. In addition, patients could be directed to an interactive screening form housed on a website where they would answer a series of questions that would help them make a decision about whether they should call for an appointment, head for the emergency room, or self-manage the issue. If self- management is the outcome of the screening tool, then the patient or caregiver could be directed to a credible website for more information. The idea is not to interfere with or replace the face-to-face visit, but rather to supplement the physician–patient relationship and perhaps streamline the efficiency of healthcare delivery. McCray (2005) also suggests that physicians may be resistant to providing e-mail consultations and recommending health-related websites because of the potential for malpractice liability. There is some evidence, however, that text message reminders delivered via a cell phone are more effective and efficient as appointment reminders than traditional phone calls (Car, Gurol-Urganci, de Jongh, Vodopivec-Jamsek, & Atun, 2012). Similarly, in a descriptive research study by Dudas, Pumilia, and Crocetti (2013) it was found that parents of children who recently visited an emergency department were interested in receiving follow-up communication from healthcare providers by text messaging and/or email. A major barrier to widespread adoption of e-mail and text messaging among American healthcare providers is the fact that reimbursement mechanisms for electronic health care are inadequate or nonexistent.

Piette (2007) describes the use of interactive behavior change technology to improve the effectiveness of diabetes management. The goal of the interactive behavior change technology is to improve communication between patients and healthcare providers and to provide educational interventions that promote better disease management between visits. The combination of electronic medication reminders, meters that track glycemic control longitudinally, and personal digital assistant–based calculators was found to support the behavioral interventions necessary to better manage the diabetes.

As a conclusion to their study, Watson, Bell, Kvedar, and Grant (2008) caution that even though patients are part of the digital divide (lacking access or skill in electronic communications and Internet use), one cannot assume that they will be resistant to using other forms of technology to support health. These authors compared Internet users to non-Internet users and found that both groups were willing to learn to use new technology to manage type 2 diabetes, including wireless communication devices for information sharing with physicians.

Healthcare practitioners may soon embrace the use of “information prescriptions” (D’Alessandro, 2010) that direct patients and families to credible websites, including government and HCO websites, and wikis and blogs that may help them understand their health issues or share information with and seek support from others who have similar issues. “Information prescriptions are prescriptions of focused, evidence-based information given to a patient at the right time to manage a health problem” (p. 81). The National Health Service in the United Kingdom has developed an information prescription generator that can be used by providers or the public to access Web-based health information (http://www.nhs.uk/ipg/Pages/IPStart.aspx).

Summary It is clear that the consumer empowerment movement will continue to drive the need for access to quality health education and support programs. In an ideal world, practitioners will design educational materials that are user friendly, culturally competent, interesting, dynamic, and interactive, and that meet the skills, education needs, and interests of the user.

THOUGHT-PROVOKING QUESTIONS

1. How do you envision technology enhancing patient or consumer education in your setting? 2. Formulate a plan evidencing a potent patient education episode on methicillinresistant Staphylococcus aureus. Provide a rationale for each

approach and describe a tool you would use to educate the patient and his or her family.

References Abreu, D., Tamura, T., Sipp, J., Keamy, D., & Eavey, R. (2008). Podcasting: Contemporary patient education. Ear, Nose & Throat Journal, 87(4), 208,

210–211. Retrieved from ProQuest Nursing & Allied Health Source (Document ID: 1468588971). Anderson, A., & Klemm, P. (2008). The Internet: Friend or foe when providing patient education? Clinical Journal of Oncology Nursing, 12(1), 55–63.

Retrieved from ProQuest Nursing & Allied Health Source (Document ID: 1430141231). Car, J., Gurol-Urganci, I., de Jongh, T., Vodopivec-Jamsek, V., & Atun, R. (2012). Mobile phone messaging reminders for attendance at healthcare

appointments. Cochrane Database of Systematic Reviews, 7. Centers for Disease Control and Prevention (CDC). (2011). The health communicator’s social media toolkit.

http://www.cdc.gov/socialmedia/Tools/guidelines/pdf/SocialMediaToolkit_BM.pdf Centers for Disease Control and Prevention (CDC). (2012). Health literacy: Accurate, accessible and actionable health information for all.

http://www.cdc.gov/healthliteracy/ D’Alessandro, D. (2010). Challenges and options for patient education in the office setting. Pediatric Annals, 39(2), 78–83. Retrieved from Health

Module (Document ID: 1972816891). Donovan, O. (2005). The carbohydrate quandary: Achieving health literacy through an interdisciplinary WebQuest. Journal of School Health, 75(9),

359–362. Retrieved from Health Module database (Document ID: 924409661). Dudas, R. A., Pumilia, J., & Crocetti, M. (2013). Pediatric Caregiver Attitudes and Technologic Readiness Toward Electronic Follow-Up

Communication in an Urban Community Emergency Department. Telemedicine & E-Health, 19(6), 493–496. doi:10.1089/tmj.2012.0166 Flores, A. (2006). Using computer games and other media to decrease child obesity. Agricultural Research, 54(3), 8–9. Retrieved from Research Library

Core database (Document ID: 1005199991). Fox, S. (2006). Online health search 2006. http://www.pewinternet.org/Reports/2006/Online-Health-Search-2006.aspx Fox, S. (2011, February 1). Health topics. http://www.pewinternet.org/Reports/2011/HealthTopics.aspx Fox, S. (2013). Health online 2013. http://pewinternet.org/Reports/2013/Health-online.aspx Fox, S., & Purcell, K. (2010). Chronic disease and the

Internet. http://www.pewinternet.org/Reports/2010/Chronic-Disease/Summary-of-Findings/Adults-living-with-chronic-diseaseare- disproportionately-offline-in-an-online-world.aspx

Glassman, P. (2008). Health literacy. http://nnlm.gov/outreach/consumer/hlthlit.html Glymetrix Diabetes Game. (2009). http://www.diabetesgame.com/ Health on the Net (HON) Foundation. (2005). Analysis of 9th HON survey of health and medical Internet users.

http://www.hon.ch/Survey/Survey2005/res.html Lauterbach, C. (2010). Exploring the usability of e-health websites. Usability News, 12(2).

http://psychology.wichita.edu/surl/usabilitynews/122/pdf/Usability%20News%20122%20-%20Lauterbach.pdf Lober, W. B., & Flowers, J. L. (2011, August). Consumer empowerment in health care amid the Internet and social media. Seminars in Oncology

Nursing, 27(3), 169–182. http://dx.doi.org/10.1016/j.soncn.2011.04.002 McCray, A. (2005). Promoting health literacy. Journal of the American Medical Informatics Association, 12(2), 152–163. Retrieved from ProQuest

Nursing & Allied Health Source database (Document ID: 810410751). Missen, C., & Cook, T. (2007). Appropriate information-communications technologies for developing countries.

http://www.who.int/bulletin/volumes/85/4/07-041475/en/index.html National Center for Complementary and Alternative Medicine (NCCAM), National Institutes of Health. (2008). 10 things to know about evaluating

medical resources on the Web. http://nccam.nih.gov/health/webresources Neiger, B. L., Thackeray, R., Burton, S. H., Giraud-Carrier, C. G., & Fagen, M. C. (2013). Evaluating social media’s capacity to develop engaged

audiences in health promotion settings: Use of Twitter metrics as a case study. Health Promotion Practice, 14(2), 157–162. doi: 10.1177/1524839912469378 Oda, Y., & Kristula, D. (n.d.) The Cancer Game: A side-scrolling, arcade-style, cancer-fighting video game. http://www.cancergame.org/ Office of Disease Prevention and Health Promotion & U.S. Department of Health and Human Services. (2009). Proposed Healthy People 2020

objectives. http://www.healthypeople.gov/hp2020/Objectives/TopicAreas.aspx Parker, R., Ratzan, C., & Lurie, N. (2003). Health literacy: A policy challenge for advancing highquality health care. Health Affairs, 22(4), 147.

Retrieved from ABI/INFORM Global database (Document ID: 376436551). Pew/Internet. (2006). The future of the Internet II. http://news.bbc.co.uk/1/shared/bsp/hi/pdfs/22_09_2006pewsummary.pdf Piette, J. (2007). Interactive behavior change technology to support diabetes self-management: Where do we stand? Diabetes Care, 30(10), 2425–2432.

Retrieved from Health Module database (Document ID: 1360494771). Sternberg, D. (2002). Building on your quick wins. Marketing Health Services, 22(3), 41–43. Retrieved from ABI/INFORM Global database (Document

ID: 155769441). Watson, A., Bell, A., Kvedar, J., & Grant, R. (2008). Reevaluating the digital divide: Current lack of Internet use is not a barrier to adoption of novel

health information technology. Diabetes Care, 31(3), 433–435. Retrieved from Health Module.

Chapter 18

Using Informatics to Promote Community/Population Health Margaret Ross Kraft, Ida Androwich, Kathleen Mastrian, and Dee McGonigle

OBJECTIVES

1. Provide an overview of community and population health informatics. 2. Describe informatics tools for promoting community and population health. 3. Define the roles of federal, state, and local public health agencies in the development of public health informatics.

Key Terms

Behavioral Risk Factor Surveillance System Bioterrorism Centers for Disease Control and Prevention (CDC) Community risk assessment (CRA) Crowdsourcing Epidemiology National Center for Public Health Informatics National health information network National Health and Nutrition Examination Survey Public health Public health informatics Public health interventions Regional health information exchange Risk assessment Social media Suicide Prevention Community Assessment Tool Surveillance Surveillance data systems Syndromic surveillance Youth Risk Behavior Surveillance System

Introduction In late fall of 2002, severe acute respiratory syndrome (SARS) appeared in China. By March 2003, SARS had become recognized as a global threat. According to World Health Organization (WHO) data, more than 8,000 people from 29 countries became infected with this previously unknown virus and more than 700 people died. By 2004, the last SARS cases were caused by laboratory-acquired infections. Because of computerized global data collection, the potentially negative impact of a widespread global epidemic was averted.

Many surveillance systems, loosely termed “syndromic surveillance systems,” use data that are not diagnostic of a disease but that might indicate the early stages of an outbreak. Outbreak detection is the overriding purpose of syndromic surveillance for terrorism preparedness. Enhanced case finding and monitoring the course and population characteristics of a recognized outbreak also are potential benefits of syndromic surveillance. In recent years, new data have been used by public health officials to enhance surveillance, such as patients’ chief complaints in emergency departments, ambulance log sheets, prescriptions filled, retail drug and product purchases, school or work absenteeism, and medical signs and symptoms in persons seen in various clinical settings. With faster, more specific and affordable diagnostic methods and decision-support tools, timely recognition of reportable diseases with the potential to lead to a substantial outbreak is now possible. Tools for pattern recognition can be used to screen data for patterns needing further public health investigation. During the 2003 epidemic, the Centers for Disease Control and Prevention (CDC) worked to develop surveillance criteria to identify persons with SARS in the United States, and the surveillance case definition changed throughout the epidemic, to reflect increased understanding of SARS (CDC, 2007).

Information acquired by the collection and processing of population health data becomes the basis for knowledge in the field of public health. There is an ever-increasing need for timely information about the health of communities, states, and countries. Knowledge about disease trends and other threats to community health can improve program planning, decision making, and care delivery. Patients seen from the perspective of major health threats within their communities can benefit from opportunities for early intervention.

This chapter focuses on the application of informatics methods to public health surveillance. The availability of clinical information for public health has been fundamentally changed by the introduction of the electronic health record (EHR) and health information technology (IT), which now give public health “an unprecedented opportunity to leverage the information, technologies and standards to support critical public health functions such as alerting and surveillance” (Garrett, 2010).

Core Public Health Functions The core public health functions are “The assessment and monitoring of the health of communities and populations at risk to identify health problems and priorities; The formulation of public policies designed to solve identified local and national health problems and priorities; To assure that all populations have access to appropriate and cost-effective care, including health promotion and disease prevention services, and evaluation of the effectiveness of that care” (Medterms Medical Dictionary, 2007). “Public health is a field that encompasses an amalgam of science, action, research, policy, advocacy and government” (Yasnoff, Overhage, Humphreys, & LaVenture, 2001, p. 536).

Historically, Dr. John Snow can be designated as the “father” of public health informatics (PHI). In 1854, he plotted information about cholera deaths and was able to determine that the deaths were clustered around the same water pump in London. He convinced authorities that the cholera deaths were associated with that water pump; when the pump handle was removed, the cholera outbreak ended. It was Dr. Snow’s focus on the cholera-affected population as a whole rather than on a single patient that led to his discovery of the source of the cholera outbreak (Vachon, 2005).

Florence Nightingale should also be recognized as an early public health informaticist. Her recommendations about medical reform and the need for improved sanitary conditions were based on data about morbidity and mortality that she compiled from her experiences in the Crimea and England. Her efforts led to a total reorganization of how and which healthcare statistics were collected (Dossey, 2000).

Just as information has been recognized as an asset in the business world, so health care is now recognized as an information- intensive field requiring timely, accurate information from many different sources. Health information systems address the collection, storage, analysis, interpretation, and communication of health data and information. Many health disciplines, such as medicine and nursing, have developed their own concepts of informatics integrating computer, information, and cognitive science with the science of the professional domain. That trend has reached the field of public and community health. PHI represents “a systematic application of information and computer science and technology to public health (PH) practice, research and learning” (Yasnoff, O’Carroll, Koo, Linkings, & Kilbourne, 2000, p. 67). This area of informatics differs from others because it is focused on the promotion of health and disease prevention in populations and communities. PHI efficiently and effectively organizes and manages data, information, and knowledge generated and used by public health professionals to fulfill the core functions of public health: assessment, policy, and assurance (Agency for Toxic Substances and Disease Registry, 2003). Public health changes the social conditions and systems that affect everyone within a given community. It is because of public health initiatives that people understand the importance of clean water, the dangers of second-hand smoke, and the fact that seat belts really do save lives (Public Health Institute, 2007).

The scope of PHI practice includes knowledge from a variety of additional disciplines, including management, organization theory, psychology, political science, and law and fields related to public health, such as epidemiology, microbiology, toxicology, and statistics (O’Carroll, Yasnoff, Ward, Ripp, & Martin, 2003, p. 5). PHI focuses on applications of IT that “promote the health of populations rather than individuals, focus on disease prevention rather than treatment, focus on preventive intervention at all vulnerable points” (pp. 3–4). PHI addresses the data, information, and knowledge that public health professionals generate and use to meet the core functions of public health (Public Health Data Standards Consortium [PHDSC], 2007b). Yasnoff et al. (2000) defined four principles that define and guide the activities of PHI: (1) applications promote the health of populations, (2) applications focus on disease and injury prevention, (3) applications explore prevention at “all vulnerable points in the causal changes,” and (4) PHI reflects the “governmental context in which public health is practiced” (p. 69).

The Institute of Medicine defines the role of public health as “fulfilling society’s interest in assuring conditions in which people can be healthy” (Khoury, 1997, p. 176). Functions of public health include prevention of epidemics and the spread of disease, protection against environmental hazards, promotion of health, disaster response and recovery, and providing access to health care (PHDSC, 2007a).

The initiative of integrating the healthcare enterprise to ensure that healthcare information can be shared more easily and used more effectively has inspired the creation of the domain known as quality, research, and public health (QRPH). Participants in this domain address the repurposing of clinical, demographic, and financial data collected in the process of providing clinical care to the monitoring of disease patterns; incidence, prevalence, and situational awareness of such patterns; and the identification of new patterns of disease not previously known or anticipated. Such data can be incorporated within existing public health population analyses and programs for direct outreach and condition management through registries and locally determined appropriate treatment programs or protocols (QRPH, 2010).

Community Health Risk Assessment: Tools for Acquiring Knowledge As the public has become more aware of harmful elements in the environment, risk assessment tools have been developed. Such tools allow assessment of pesticide use, exposure to harmful chemicals, contaminants in food and water, and toxic pollutants in the air to determine if potential hazards need to be addressed. A risk assessment may also be called a “threat and risk assessment.” A “threat” is

a harmful act, such as the deployment of a virus or illegal network penetration. A “risk” is the expectation that a threat may succeed and the potential damage that can occur (PCMag.com, 2007). “Risk factor assessments complement vital statistics data systems and morbidity data systems by providing information on factors earlier in the causal chain leading to illness, injury or death” (O’Carroll, Powell-Griner, Holtzman & Williamson, 2003, p. 316).

“Health risk assessments are used to estimate whether current or future exposures will pose health risks to broad populations” (California Environmental Protection Agency [CEPA], 1998, p. 4) and are used to weigh the benefits and costs of various program alternatives for reducing exposure to potential hazards. They may also influence public policy and regulatory decisions. Health risk assessment is a constantly developing process based in sound science and professional judgments. There are usually four basic steps ascribed to risk assessment:

1. Hazard identification seeks to determine the types of health problems that could be caused by exposure to a potentially hazardous material. All research studies related to the potentially hazardous material are reviewed to identify potential health problems.

2. Exposure assessment is done to determine the length, amount, and pattern of exposure to the potentially hazardous material. 3. Dose–response assessment is an estimation of how much exposure to the potential hazard would cause varying degrees of

health effects. 4. Risk characterization is an assessment of the risk of the hazardous material causing illness in the population (CEPA, 1998).

The overall question the risk assessment has to answer is, “How much risk is acceptable?” Risk factor systems are used throughout the United States and may be local, regional, or national in scope. Specific risk assessment tools exist for specific health issues, such as the Suicide Prevention Community Assessment Tool, which addresses general community information, prevention networks, and the demographics of the target population and community assets and risk factors. Other risk assessment tools include the Youth Risk Behavior Surveillance System, the Behavioral Risk Factor Surveillance System, and the National Health and Nutrition Examination Survey.

Determining the presence of risk factors in community is a key part of a community risk assessment (CRA). Communities may be concerned about which elements in the environment affect or may affect the community’s health, the level of environmental risk, and other factors that should be included in public health planning. Ball (2003) defines value as “a function of cost, service, and outcome” (p. 41). The value of a CRA derives from its ability to provide information crucial to planning, build consensus regarding how to mobilize community resources, and allow for comparison of risks with those of other communities. The goal of a CRA is risk reduction and improved health. A CRA may identify unmet needs and opportunities for action that may help set new priorities for local public health units. It may also be used to monitor the impact of prevention programs.

Processing Knowledge and Information to Support Epidemiology and Monitoring Disease Outbreaks There is a need to define the role of federal, state, and local public health agencies in the development of PHI and IT applications. The availability of IT today challenges all stakeholders in the health of the public to adopt new systems that can provide adequate disease surveillance; it also challenges people to improve outmoded processes.

Preparedness in public health requires more timely detection of potential health threats, situational awareness, surveillance, outbreak management, countermeasures, response, and communications. Surveillance uses health-related data that signal a sufficient probability of a case or an outbreak that warrants further public health response. Although historically syndromic surveillance has been used to target investigations of potential infectious cases, its use to detect possible outbreaks associated with bioterrorism is increasingly being explored by public health officials (CDC, 2007). Early detection of possible outbreaks can be achieved through timely and complete receipt, review, and investigation of disease case reports; by improving the ability to recognize patterns in data that may be indicative of a possible outbreak early in its course; and through receipt of new types of data that can signify an outbreak earlier in its course. Such new types of data might include identification of absences from work or school; increased purchases of healthcare products, including specific types of over-the-counter medications; presenting symptoms to healthcare providers; and laboratory test orders (CDC, 2007). The University of Pittsburgh Realtime Outbreak and Disease Surveillance Laboratory (RODS), for example, developed the National Retail Data Monitor (NRDM) system. The NRDM collects data on over-the-counter medications and other healthcare products from 28,000 stores and uses computer algorithms to detect unusual purchase patterns that might potentially signal a disease outbreak (RODS Laboratory, 2013). A comprehensive surveillance effort supports timely investigation and identifies data needs for managing the public health response to an outbreak or terrorist event. Informatics tools are becoming increasingly important in these public health efforts.

To appropriately process public health data, PHI has a need for a standardized vocabulary and coding structure. This is especially important as national public health data are collected and data mining performed so that data variables can be understood across systems and between agencies. Health information organizations (HIOs) have been established to support data sharing via health information exchanges (HIE) promoted by the meaningful use criteria of the electronic health record (EHR). Central to these initiatives is the need for standardized codes and terminologies that may be used by the HIOs to map data from disparate sources (Shapiro, Mostashari, Hripcsak, Soulakis, & Kuperman, 2011).

In the early 1990s, the CDC launched a plan for an integrated surveillance system that moved from stand-alone systems to networked data exchange built with specific standards. Early initiatives were the National Electronic Telecommunications System for Surveillance and the Wide-ranging Online Data for Epidemiologic Research. Six current initiatives reflect this early vision:

1. PulseNet USA: A surveillance network for food-borne infections. 2. National Electronic Disease Surveillance System: Facilitates reporting on approximately 100 diseases, with data feeding

directly from clinical laboratories, which allows for early detection. 3. Epidemic Information Exchange: A secure communication system for practitioners to access and share preliminary health

surveillance information. 4. Health Alert Network: A state and nationwide alert system. 5. Biosense: Provides improved real-time biosurveillance and situational awareness in support of early detection. 6. Public Health Information Network: Promotes standards and software solutions for the rapid flow of public health information.

Certainly, the events of September 11, 2001, which indicated the need for the United States to increase its efforts directed toward prevention of terrorism, accelerated the need for informatics in public health practice. Today, response requirements include fast detection, science, communication, integration, and action (Kukafka, 2006). In 2005, the CDC created the National Center for Public Health Informatics to provide leadership in the field. This center aims to protect and improve health through PHI (McNabb, Koo, Pinner, & Seligman, 2006).

Information is vital to public health programming. The data processed into public health information can be obtained from administrative, financial, and facility sources. Included in this data stream may be encounter, screening, registry, clinical, and laboratory and surveillance data. It has been recommended that the functions of population health beyond surveillance be integrated into the EHR and the personal health record. Such an initiative might allow for population-level alerts to be sent to clinicians through these electronic record systems. Systems now being developed allow for automated syndromic surveillance of emergency department records and media surveillance, which in turn supports early detection of potential pandemic occurrences. Such systems were tested during the 2009 H1N1 flu outbreak. The public health–enhanced electronic medical record can provide immediate detection and reporting of notifiable conditions. The incorporation of geographic information systems allows public health data to be mapped to specific locations that may indicate an immediate need for intervention (Grannis & Vreeman, 2010).

Vital statistics from state and local governments are also used for public health purposes. It should be noted that databases created with public funds are public databases that are available for authorized public representatives for public purposes (Freedman & Weed, 2003).

Widespread implementation of EHRs is likely to facilitate the concept of a public health–enabled record, which can automatically send patient information alerts from the point of care to public health departments when reportable symptoms, conditions, or diseases are encountered. A public health–enabled EHR can be bidirectional, allowing public health information and recommendations for treatment to be accessible at the point of care. One public health EHR prototype addresses the information flow related to newborn screenings (Orlova et al., 2005).

Potential applications of HIE to public health have been described by Shapiro et al. (2011). They include syndromic surveillance using data generated from mandated and nonmandated laboratory results, physician diagnoses, and emergency or clinic chief complaints; strategies to locate loved ones in mass-casualty events; and public health alerts at the individual and population levels.

Applying Knowledge to Health Disaster Planning and Preparation The availability of data and the speed of data exchange can have a significant impact on critical public health functions, such as disease monitoring and syndromic surveillance. Currently, surveillance data are limited and historical in nature, although this situation is rapidly changing. Nevertheless, special data collections are needed to address specific public health issues, and investigations and emergencies are still frequently addressed and managed with paper. In the future, PHI will make real-time surveillance data available electronically, and investigations and emergences will be managed with the tools of informatics (Yasnoff et al., 2004). “Surveillance data systems such as infectious disease trackers that collect data on adverse health effects are invaluable tools for public health officials to tap for planning, evaluation, or implementation of public health interventions” (Agency for Toxic Substances and Disease Registry, 2003). “Syndromic surveillance for early outbreak detection is an investigational approach where health department staff, assisted by automated data acquisition and generation of statistical signals, monitor disease indicators continually (real-time) or at least daily (near realtime) to detect outbreaks of diseases earlier and more completely than might otherwise be possible with traditional public health methods” (Buehler, Hopkins, Overhage, Sosin, & Tong, 2004, para. 7). Traditionally, there has been no common infrastructure to respond to pandemics, but the development of health IT is creating opportunities that go far beyond national boundaries to impact global public health initiatives.

In New York City, a primary care information project funded by the CDC has developed a multifaceted initiative, the Center for Excellence in Public Health Informatics, to address issues of measurement of meaningful use, disease and outbreak surveillance, and decision support alerts at the point of care (Buck, Wu, Souliakis, & Kukalka, 2010).

Informatics Tools to Support Communication and Dissemination The revolution in IT has made the capture and analysis of health data and the distribution of healthcare information more achievable and less costly. Since the early 1960s, the CDC has used IT in its practice; PHI emerged as a specialty in the 1990s. PHI has become more important with improvements in IT, changes in the care delivery system, and the challenges related to emerging infections, resistance to antibiotics, and the threat of chemical and biologic terrorism. Two-way communication between public health agencies, community, and clinical laboratories can identify clusters of reportable and unusual diseases. In turn, health departments can consult on case diagnosis and management, alerts, surveillance summaries, and clinical and public health recommendations. Ongoing healthcare provider outreach, education, and 24-hour access to public health professionals may lead to the discovery of urgent health threats. The automated transfer of specified data from a laboratory database to a public health data repository improves the timeliness and completeness of reporting notifiable conditions.

Public health information systems represent a partnership of federal, state, and local public health professionals. Such systems facilitate the capture of large amounts of data, rapid exchange of information, and strengthened links among these three system levels.

Dissemination of prevention guidelines and communication among public health officials, clinicians, and patients has emerged as a major benefit of PHI. IT solutions can be used to provide accurate and timely information that guides public health actions. In addition, the Internet has become a universal communications pathway and allows individuals and population groups to be more involved and take greater responsibility for management of their own health status.

Few public health professionals have received formal informatics training, and many may not be aware of the potential impact of IT on their practice. A working group formed at the University of Washington Center for PHI has published a draft of PHI competencies needed (Karras, 2007). These competencies include the following (Center for Public Health Informatics, 2007):

Supporting development of strategic direction for PHI within the enterprise Participating in development of knowledge management tools for the enterprise Using standards Ensuring that the knowledge, information, and data needs of project or program users and stakeholders are met Managing information system development, procurement, and implementation Managing IT operations related to a project or program (for public health agencies with internal IT operations) Monitoring IT operations managed by external organizations Communicating with cross-disciplinary leaders and team members Participating in applied public health informatics research Developing public health information systems that are interoperable with other relevant information systems Supporting use of informatics to integrate clinical health, environmental risk, and population health Implementing solutions that ensure confidentiality, security, and integrity, while maximizing availability of information for

public health Conducting education and training in PHI

Using Feedback to Improve Responses and Promote Readiness Improvement of community health status and population health depends on effective public and healthcare infrastructures. In addition to information from public health agencies, there is now interest in the capture of information from hospitals, pharmacies, poison control centers, laboratories, and environmental agencies. Timely collection of such data allows early detection and analysis, which can increase the rapidity of response with more effective interventions. Yasnoff et al. (2000) identify the “grand challenges” still facing PHI as the development of national public health information systems, a closer integration of clinical care with public health, and concerns of confidentiality and privacy.

Population health data must be considered an important part of the infrastructure of all regional health information exchanges, which are the building blocks for a national health information network. Organizations and agencies interested in promoting and protecting the public’s health must commit to collaboration and seamless data sharing (PHDSC, 2007b). Public health data include data related to surveillance, environmental health, and preparedness systems as well as client information, such as data from immunization registries and laboratory results reporting and analysis. These types of data can provide information about outbreaks, patterns of drug-resistant organisms, and other trends that can help improve the accuracy of diagnostic and treatment decisions (LaVenture, 2005). A regional health information exchange and national health information network can also support public health goals through broader opportunities for participation in surveillance and prevention activities, improved case management and care coordination, and increased accuracy and timeliness of information for disease reporting (LaVenture, 2005).

Much of the information presented here is focused on reaction to issues and timely intervention, rather than harnessing information technology for disease prevention. Fuller (2011) has advocated for a shift to prevention informatics by harnessing realtime social data and aggregating and representing these data in a meaningful way so that an appropriate prevention response can be mounted. For example, Internet searches related to flu symptoms might prompt a public health prevention response such as a school closure to minimize spread. Newer software tools to support mapping and real-time data visualization include Riff and Ushahidi, each of which supports “gathering of distributed data from the web and other data streams” (p. 40). “Prevention informatics offers a useful paradigm for re-imagining health information systems and for harnessing the vast array of data, tools, technologies and systems to respond proactively to health challenges across the globe” (p. 41).

Harnessing data from social media such as Twitter and Facebook provides yet another example of using citizen-generated information (crowdsourcing) in community health. Merchant, Elmer, and Lurie (2011) have described how mining data generated in social media can improve response to mass disasters by helping responders locate people who need help and identify areas where to send resources, build social capital, and promote community resilience postdisaster. “Tweets and photographs linked to timelines and interactive maps can tell a cohesive story about a recovering community’s capabilities and vulnerabilities in real time” (p. 291). These authors caution, however, that social media should be used to augment—not replace—current disaster response and communication systems, as not all communications in social media are entirely trustworthy.

Summary Public health informatics strives to ensure that evolving health data systems will meet the data needs of all organizations interested in population health as national and international standards are developed for healthcare data collection. This includes standardization of environmental, sociocultural, economic, and other data that are relevant to public health (PHDSC, 2007b). Table 18-1 provides the names, addresses, and URLs for important organizations dedicated to public health data and informatics. Table 18-2 lists abbreviations commonly used in PHI.

The future of practice in public health depends on how efficiently and effectively public health data are captured, analyzed, and disseminated for regional, national, and global health planning and management. In an ideal world, we would see seamless data collection and sharing and a commitment to global health planning.

TABLE 18-1 IMPORTANT PHI SITES

Name Address Website

American Public Health Association

APHA 800 I Street, NW Washington, DC 20001

www.apha.org

Center for Public Health Informatics

CPHI University of Washington 1100 NE 45th Street, Ste 405 Seattle, WA 98105

www.cphi.washington.edu

Centers for Disease Control and Prevention

Centers for Disease Control and Prevention 1600 Clifton Road Atlanta, GA 30333

www.cdc.gov

National Center for Public Health Informatics

The National Center for Public Health Informatics (NCPHI) 1600 Clifton Road, NE Mailstop E-78 Atlanta, GA 30333

https://web.archive.org/web/20110123075557/ http://www.cdc.gov/ncphi/

Public Health Data Standards Consortium

Public Health Data Standards Consortium c/o Johns Hopkins Bloomberg School of Public Health 624 N Broadway, Room 325 Baltimore, MD 21205

www.phdsc.org

Public Health Institute Public Health Institute 555 12th Street, 10th Floor Oakland, CA 94607

www.phi.org

TABLE 18-2 ABBREVIATIONS USED IN PHI

BRFSS Behavioral Risk Factor Surveillance System CDC Centers for Disease Control and Prevention CEPA California Environmental Protection Agency CPHI Center for Public Health Informatics CRA Community Risk Assessment EPI-X Epidemic Information Exchange HAN Health Alert Network IOM Institute of Medicine IT Information Technology NCPHI National Center for Public Health Informatics NEDSS National Electronic Disease Surveillance System NETSS National Electronic Technology System for Surveillance NHANES National Health and Nutrition Examination Survey NHIN National Health Information Network PH Public Health PHDSC Public Health Data Standards Consortium PHI Public Health Informatics PHIN Public Health Information Network PHRAP Pennsylvania’s Health Risk Assessment Process QRPH Quality, Research, Public Health RHIO Regional Health Information Exchanges SPRC Suicide Prevention Community Assessment Tool WONDER Wide-ranging Online Data for Epidemiologic Research YRBSS Youth Risk Behavior Surveillance System

THOUGHT-PROVOKING QUESTIONS

1. Imagine that you are a public health informatics specialist and that you and your colleagues are concerned about a new strain of influenza. Which public health data are used to determine the need for a mass inoculation? Which data will be collected to determine the success of such a program?

2. What are the advantages and disadvantages of using crowdsourced social media data during a disaster response?

References Agency for Toxic Substances and Disease Registry. (2003). ATSDR glossary of terms. www.atsdr.cdc.gov/glossary.html Ball, M. (2003). Better health through informatics: Managing information to deliver value. In P. O’Carroll, W. L. Yasnoff, M. E. Ward, L. Ripp, & E.

Martin (Eds.), Public health informatics and information systems (pp. 39–51). New York, NY: Springer-Verlag. Buck, M., Wu, W., Souliakis, N., & Kukalka, R. (2010). Achieving excellence in public health informatics: The New York City experience. Washington,

DC: AMIA. Buehler, J. W., Hopkins, R. S., Overhage, J. M., Sosin, D. M., & Tong, V. (2004). Framework for evaluating public health surveillance systems for early

detection of outbreaks. http://www.cdc.gov/mmwr/preview/mmwrhtml/rr5305a1.htm California Environmental Protection Agency (CEPA). (1998). A guide to health risk assessment. www.oehha.ca.gov Center for Public Health Informatics (CPHI). (2007). Draft competencies V7 for the public health informatician. Seattle, WA: University of Washington. Centers for Disease Control and Prevention (CDC). (2007). www.cdc.gov Dossey, B. M. (2000). Florence Nightingale: Mystic, visionary, healer. Springhouse, PA: Springhouse. Freedman, M. A., & Weed, J. A. (2003). National vital statistics system. In P. O’Carroll, W. Yasnoff, M. Ward, L. Ripp, & E. Martin (Eds.), Public

health informatics and information systems (pp. 269–285). New York, NY: Springer-Verlag. Fuller, S. (2011). From intervention informatics to prevention informatics. Bulletin of the American Society for Information Science & Technology,

38(8), 36–41. Garrett, N. (2010). Leveraging the EHR for public health alerting. CDC Clinician Outreach & Communication Activity (COCA) conference call, June

22, 2010. Grannis, S., & Vreeman, D. (2010). A vision of the journey ahead: Using public health notifiable condition mapping to illustrate the need to maintain

value sets. Washington, DC: AMIA. Karras, B. (2007). Competencies for the public health informatician. Seattle, WA: University of Washington. Khoury, M. J. (1997). Genetic epidemiology and the future of disease prevention and public health. Epidemiology Reviews, 19(1), 175–180. Kukafka, R. (2006). Public health informatics. Medical Informatics Course for Health Professionals. Woods Hole, MA. LaVenture, M. (2005, May). Role of population/public health in regional health information exchanges. PHDSC/eHealth Initiative Annual Conference,

Washington, DC. McNabb, S., Koo, D., Pinner, R., & Seligman, J. (2006). Informatics and public health at CDC.

http://www.cdc.gov/mmwr/preview/mmwrhtml/su5502a10.htm Medterms Medical Dictionary. (2007). www.medterms.com Merchant, R., Elmer, S., & Lurie, N. (2011). Integrating social media into emergency-preparedness efforts. New England Journal of Medicine, 365(4),

289–291. doi:10.1056/NEJMp1103591 O’Carroll, P. L., Powell-Griner, E., Holtzman, D., & Williamson, G. D. (2003). Risk factor information systems. In P. O’Carroll, W. Yasnoff, M. Ward,

L. Ripp, & E. Martin (Eds.), Public health informatics and information systems (pp. 316–334). New York, NY: Springer-Verlag. O’Carroll, P., Yasnoff, W., Ward, M., Ripp, L., & Martin, E. (Eds.). (2003). Public health informatics and information systems. New York, NY:

Springer-Verlag. Orlova, A., Dunnagan, M., Finitzo, T., Higgins, M., Watkings, T., Tien, A., & Beales, S. (2005). An electronic health record–public health (EHR-PH)

system prototype for interoperability in 21st century healthcare systems (pp. 575–579). AMIA, Annual Symposium Proceedings. PCMag.com Encyclopedia. (2007). Retrieved from http://www.pcmag.com/encyclopedia Public Health Data Standards Consortium (PHDSC). (2007a).

Tutorial module 1: What is public health. Retrieved August 2007 from www.phdsc.org/knowresources/Tutorials/module1 Public Health Data Standards Consortium (PHDSC). (2007b). Tutorial module 8: Viewing public health data and data standards in a larger context.

Retrieved August 2007 from www.phdsc.org/knowresources/tutorials/module1 Public Health Institute. (2007). www.phi.org QRPH. (2010). Retrieved January 2011 from http://www.interoperabilityshowcase.org/…/HIMSS09_interop_QRPH RODS Laboratory. (2013). About the National Retail Data Monitor. www.rods.pitt.edu/site/content/blogsection/4/42/ Shapiro, J. S., Mostashari, F., Hripcsak, G., Soulakis, N., & Kuperman, G. (2011). Using health information exchange to improve public health.

American Journal of Public Health, 101(4), 616–623. doi: 10.2105/AJPH.2008.158980 Vachon, D. (2005). Dr John Snow blames water pollution for cholera epidemic. Old News, 16(8), 8–10.

http://www.ph.ucla.edu/epi/snow/fatherofepidemiology.html Yasnoff, W., Humphreys, B., Overhage, J., Detmer, D., Brennan, P., Morris, R., … Fanning, J. (2004). A consensus action agenda for achieving the

national health information infrastructure. Journal of the American Medical Informatics Association, 11(4), 332–338. Yasnoff, W., O’Carroll, P., Koo, D., Linkings, R., & Kilbourne, E. (2000). Public health informatics: Improving and transforming public health in the

information age. Journal of Public Health Management Practice, 6(6), 67–75. Yasnoff, W., Overhage, M., Humphreys, B., & LaVenture M. (2001). A national agenda for public health informatics. Journal of the American Medical

Informatics Association, 8(6), 535–545.

Chapter 19

Telenursing and Remote Access Telehealth Original contribution by Audrey Kinsella, Kathleen Albright, Sheldon Prial, and Schuyler F. Hoss; revised by Kathleen Mastrian and Dee McGonigle

OBJECTIVES

1. Explore the use of telehealth technology in nursing practice. 2. Identify the socioeconomic factors likely to increase the use of telehealth interventions. 3. Describe clinical and nonclinical uses of telehealth. 4. Specify and describe the most common telehealth tools used in nursing practice. 5. Explore telehealth pathways and protocols. 6. Identify legal, ethical, and regulatory issues of home telehealth practice. 7. Describe the role of the telenurse. 8. Apply the Foundation of Knowledge model to home telehealth.

Key Terms

Call centers Central stations Chronic disease Home health care Home telehealth care Medication management devices Patient informed consent Peripheral biometric (medical) devices Personal emergency response system Portals Real-time telehealth Sensor and activity-monitoring systems Store-and-forward telehealth transmission Telehealth Telehealth care Telemedicine Telemonitoring Telenursing Telepathology Telephony Teleradiology Web servers

Introduction Telehealth, a relatively new term in the medical and nursing vocabulary, refers to a wide range of health services that are delivered by telecommunications-ready tools, such as the telephone, videophone, and computer. The most basic of telecommunications technology, the telephone, has been used by health professionals for many years, sometimes by nurses to counsel a patient or by doctors to change a patient’s plan of care. Because of these widespread uses, people are already somewhat familiar with the value of the direct, expedient contact that telecommunications-ready tools provide for healthcare professionals. A recent press release by IMS Research (2013) reported that telehealth monitoring was used for 308,000 patients in the United States in 2012 and that the demand for services on a worldwide basis is expected to reach 1.8 million patients by 2017. The growing field of telehealth, particularly in nursing practice, will allow clinicians to improve care delivery services even more.

History of Telehealth In the early 1960s, President John F. Kennedy gave the National Aeronautics and Space Administration (NASA) the goal of landing an American on the moon. A surprise benefit of the space program was the demonstration of effective remote monitoring of the astronauts—and thus modern telehealth was born.

Although most of the advances in telehealth have taken place in the last 20 to 30 years, Craig and Patterson (2005) describe much earlier examples, such as the use of bonfires to alert neighboring villages of the arrival of bubonic plague during the Middle Ages. Postal services and telegraphs were used to transmit health information in the mid-19th century, and 1910 marked the first transmission of stethoscope sounds over a telephone. Radio communications were used to provide medical support for crews on ships; the Seaman’s Church Institute of New York (1920) and the International Radio Medical Center (1938) are two examples of organizations founded to provide health support at a distance. These services were later expanded to cover air travel (Craig & Patterson, 2005). The National Institutes of Mental Health supported a program in the mid-1950s that connected seven state hospitals in four different states via a closed-circuit telephone system (Venable, 2005). As technology evolved, its use in health care continued to grow. The first reported use of television to monitor patients in a clinical facility occurred in the 1950s, which then led to the development of interactive closed-circuit applications in the mid-1960s. These early applications of television to health care occurred within the facility but still had the benefit of extending the reach of the caregivers because they did not need to be in the same room to monitor their patients effectively (Prial & Hoss, 2009).

In the 1970s, uses of more advanced forms of telehealth in the medical field, referred to as telemedicine, included teleradiology and telepathology—radiologic and pathologic images transmitted to specialists who were located at some distance (Allan, 2006). As additional specialties, such as dermatology and ophthalmology, entered the telemedicine arena, telehealth use enabled even more physicians to access information about their patients, regardless of the distances between themselves and the patients, and in sites other than conventional healthcare settings.

Success in telehealth was achieved after decades spent refining the technology, which resulted in clearer imaging, more speedy transmissions, and accurate replication of data from remote locations to a central hub. Technical advances in imaging, for example, have increased the usage of telehealth in wound care and increased specialists’ satisfaction with quality of patient data received from remote sites (Kinsella, 2002b). The end results of telehealth interactions today have helped to ensure that professionals, whether working off site or directly with patients, can replicate the usual clinical interactions in all specialties regardless of the distance involved in the contact.

This capacity to supervise patients away from an office is predicted to continue to grow rapidly; indeed, “worldwide telemedicine markets at $7 billion in 2009 are expected to reach $24 billion by 2016” (Aarkstore Enterprise, 2010, para. 30). The ability to provide better healthcare access is the number one benefit of using telehealth. By reducing the need for face-to-face interaction with the patient, the nurse, physician, or even technician can be much more productive. When information is collected in the home, it becomes much more convenient for the patient, and the quality and timeliness of the information is improved dramatically. Home telemonitoring should be viewed as an enhancement to care, because it allows more direct, physical intervention to occur only when it is actually needed. Care is not directed by prescheduled appointment or subjective perceptions of condition, but instead can be determined by objective measures of physical status. With telehealth, care can also be delivered at the most appropriate site of care, reducing reliance on emergency departments and inpatient facilities (Prial & Hoss, 2009).

Nursing Aspects of Telehealth Understanding telehealth and the potential use of telehealth technology in nursing practice is necessary in today’s changing healthcare arena. As this chapter describes, nurses using telehealth have much greater access to their patients’ conditions and needs and are able to respond in a more timely way than is possible using only face-to-face visits. Patients’ responses to new medications, for example, can be tracked within hours rather than the several days that elapse between face-to-face visits. The telecommunications-ready tools that can be used to achieve these results are described here, and cases that have demonstrated successful outcomes are highlighted.

Today, the use of many telehealth tools by nurses is new. Telehealth interventions or contacts are performed off site and often require less time to be spent on certain tasks because of the efficiencies offered by the technology applications. Their use, however, must never be associated with less care. It is also important to note that nursing activity in telehealth still follows the same best practice standards as those espoused in conventional care. One should simply look at the use of telehealth tools as a means for nurses to do their work better.

The case study that follows indicates the capability of a home healthcare nurse’s working with telehealth tools to detect and respond to a patient’s condition more expediently so that needed care could be accessed.

Driving Forces for Telehealth A significant increase is expected in the use of information technology tools in nursing venues in the coming decades. This use is affected by a number of factors in all of Western society. The following factors are drivers of the growing trend toward telehealth and technology use and will influence nursing practice significantly in the next decades: demographics; nursing and healthcare worker shortages; chronic diseases and conditions; the new, educated consumers; and excessive costs of healthcare services that are increasing in need and kind.

CASE STUDY: EARLY DETECTION OF A CHANGE IN CONDITION

Mrs. C., an independent, 96-year-old woman, has a history of rehospitalization because of atrial fibrillation resulting from congestive heart failure

(CHF) and hypertension. After her most recent hospitalization, Mrs. C. was treated and released into home care at an agency in Washington. A home telemonitoring

system that tracks and transmits patients’ vital signs was placed in her home. The primary goal of placing this patient on the telemonitor was to provide daily monitoring of her condition, thereby avoiding unnecessary rehospitalizations.

One morning, Mrs. C.’s telenurse detected an alarmingly low oxygen saturation level in the patient’s transmitted data. In response, the nurse telephoned Mrs. C. and asked her to retake her oxygen reading. The reading was confirmed and the telenurse contacted the patient’s physician, who requested immediate transportation of the patient to the hospital emergency room. Medics were called and Mrs. C. was taken to the hospital, where she was diagnosed with a pulmonary embolism.

The prompt response resulted from early detection and timely intervention enabled by the home telehealth equipment and a home health nurse’s oversight. One notable fact in this case is that although the primary goal of monitoring patients is to avoid unnecessary hospitalization, in this case the hospitalization was necessary for the patient as a result of her elevated blood pressure and compromised oxygen saturation levels. The patient was still asymptomatic at the time of detection. However, the telehealth intervention and subsequent hospitalization allowed for the embolism to be treated before any serious damage occurred.

Under the traditional home care model, this patient might have been seen by a nurse only two to three times per week. The clinician does not have knowledge of the patient’s condition in between visits; however, having vital patient data tracked and transmitted daily allowed for rapid response that resulted in a positive outcome, perhaps a life-saving intervention for this patient.

Demographics

One hears it every day: The baby boomers are getting older and people are living longer. In 2000, 13% of the American population was older than age 65, and the number of older adults is growing significantly. According to the U.S. Bureau of the Census (2006), 7,918 Americans turn 60 years old every day. Consequently, by 2030, 19% of the U.S. population will be 65 years old or older and the oldest-old (85 years or older) will increase by 3 million persons between 2010 (5.8 million) and 2030 (8.7 million) (U.S. Department of Commerce, Bureau of the Census, 2010). Also on the rise is the number of older Americans living with at least one chronic disease or condition; they account for 4 out of every 5 people older than age 50 (American Association for Retired Persons [AARP], 2009). This trend should alert clinicians to the much greater demand for planned professional care that will arise in the near future.

Nursing and Healthcare Worker Shortages

The crisis related to the well-known nursing shortage has two key aspects: a greater need for nurses by more persons, particularly those living with sometimes multiple comorbidities, and a significant decrease in the number of young people entering the nursing profession. In 2004, there were 2.9 million registered nurses (RNs) in the United States, more than 41% of whom were aged 50 years or older (U.S. Department of Health and Human Services, 2004). Nationwide, the demand for nurses is clearly exceeding the supply. A report from the Health Resources and Services Administration (U.S. Department of Health and Human Services, 2002) on the shortage of RNs notes that the shortage was expected to reach 12% by 2010, and then nearly double to 20% as soon as 2015. A more recent report by Buerhaus, Auerbach, and Staiger (2009) stated that the recession of the past few years has helped to ease the RN shortage currently, but suggested that the aging of the RN workforce and the aging of the population will likely combine to produce a shortfall of nurses beginning in 2018, with the deficit reaching nearly 260,000 by 2025.

The very serious shortage of healthcare workers in the United States must be addressed with some foresight. Although there is currently more focus on training laypeople, such as aides and other paraprofessionals, to perform certain nursing tasks, this venture certainly cannot replicate the clinical expertise of trained nurses skilled in nursing science. We must begin to look seriously at using effective adjuncts to skilled care, with telehealth being one of these important developments. Some organizations have already begun to do so. For example, a recent study by the Pennsylvania Homecare Association and Penn State University (2004) looked at how telehealth can be used to address workforce issues in the home healthcare industry and determined that telehealth use may enhance nurses’ job satisfaction and help to retain nurses in their current positions.

Chronic Diseases and Conditions

Chronic conditions are the leading cause of illness, disability, and death in the United States today. The aging population living with chronic diseases and conditions is expected to increase dramatically in the United States in the next few decades. Many age groups are also affected by chronic diseases, not just the elderly. Currently, more than 100 million Americans are living with one or more chronic diseases or conditions. The costs of their direct medical care are excessive—as much as $539 billion per year, according to the Institute of Aging’s 1996 report, Chronic Care in America: A 21st Century Challenge. As noted in a more recent report from the Centers for Disease Control and Prevention (2005), medical care costs of people with chronic diseases account for more than 75% of the United States’ $1.4 trillion medical care expenditures. Furthermore, by the year 2030, 148 million Americans will join these ranks, with at least one third of them being limited in their ability to go to school or to live independently. Follow the CDC’s chronic disease surveillance activities at this website http://www.cdc.gov/chronicdisease/stats/index.htm. Securing appropriate, adequate, and affordable care services for these populations should be a national concern.

Educated Consumers

The wave of today’s aging baby boomers is steering some of the usual health service practices toward a very different course. Many of these individuals are more educated than their parents and more comfortable with the use of technology. They want to become more informed and involved with their care plans. These empowered consumers will be financially motivated with the introduction of consumer-directed healthcare plans that reward healthier lifestyles and better disease management of chronic conditions. All of these circumstances will further drive the use and the innovation of new technologies to meet consumer need. However, as Kinsella (2002a) has pointed out, the consumerdriven trend of which the boomers are a part will not only be affected by technological innovation but will also affect how healthcare services are delivered. New plans for this new generation of consumers very much lean toward meeting

their demands for whenneeded, as-needed care, or care services delivered on their own terms and timing.

Economics

When one connects the drivers of today’s healthcare market—the demographics, nursing shortages, and increased number of persons living with chronic conditions and their extensive use of healthcare services—with excessive costs of this health care, the critical need for solutions becomes obvious. The U.S. healthcare system spends $1.4 trillion per year on conventional medical care. Much more will be spent annually in the coming decades. One must ask: Taking all of the driving factors of today’s healthcare market into account, what needs to be done to address healthcare issues in the United States and meet the burgeoning numbers and needs of patients?

One solution is to develop a new clinical model for American health care that includes technology. In particular, telehealth technology should be included to fill the gap resulting from an overabundance of patients and a scarcity of healthcare providers. This concept is indicated in Figure 19-1.

Figure 19-1 Technology fills the ga