Handbook of informatics for nurses & healthcare professionals pdf

Informatics Paper To Be Brushed Up

Textbook attached.

Redo paper so turnitin score is less than 20 %

Assignment:

The Future of Healthcare Informatics

Write an essay addressing each of the following points/questions. Be sure to completely answer all the questions for each number item. There should be three sections, one for each item number below, as well the introduction (heading is the title of the essay) and conclusion paragraphs. Separate each section in your paper with a clear heading that allows your professor to know which bullet you are addressing in that section of your paper. Support your ideas with at least three (3) citations in your essay. Make sure to reference the citations using the APA writing style for the essay. The cover page and reference page do not count towards the minimum word amount. Review the rubric criteria for this assignment.

Identify the current role of the informatics nurse and predict the future role of the informatics nurse, based on scholarly sources.
Explain what is meant by connected health. Provide three examples of connected health in today’s healthcare environment. Explain the benefits and drawbacks of each.
In what ways has informatics impacted public health – please provide at least three examples.
Assignment Expectations:

Length: 500 words per essay prompt/section (1500 total for this assignment)

Structure: Include a title page and reference page in APA style. These do not count towards the minimal word amount for this assignment. All APA Papers should include an introduction and conclusion.

References: Use the appropriate APA style in-text citations and references for all resources utilized to answer the questions. Include at least three (3) scholarly sources to support your claims.

Rubric: This assignment uses a rubric for scoring. Please review it as part of your assignment preparation and again prior to submission to ensure you have addressed its criteria at the highest level.

Format: Save your assignment as a Microsoft Word document (.doc or .docx) or a PDF document (.pdf)

File name: Name your saved file according to your first initial, last name, and the module number (for example, “RHall Module 1.docx”)

Notice: Care has been taken to confirm the accuracy of information presented in this book. Theauthors, editors, and the publisher, however, cannot accept any responsibility for errors or omissionsor for consequences from application of the information in this book and make no warranty, expressor implied, with respect to its contents.

Cataloging in Publication data is available at the Library of Congress

ISBN 10: 0-13-471101-7 ISBN 13: 978-0-13-471101-0

A01_HEBD1010_06_SE_FM.indd 2 3/16/18 8:36 PM

iii

Preface ix Acknowledgments xiii Contributors xv Reviewers xvii About the Authors xix

1 An Overview of Informatics in Healthcare 1 Jennifer A. Brown, Taryn Hill, Toni Hebda

Informatics 2

The Relevance of Informatics for Healthcare 3

Creating an Informatics Culture 8

Caring for the Patient Not the Computer 12

Future Directions 13

Summary 14

2 Informatics Theory and Practice 20 Maxim Topaz

Overview of Theory 20

Critical Theories Supporting Informatics 22

Informatics Specialties within Healthcare 30

Informatics Competencies for Healthcare Practitioners 33

TANIC AND NICA 37

Future Directions 37

Summary 38

3 Effective and Ethical Use of Data and Information 42 Toni Hebda, Kathleen Hunter

Overview of Data and Information 42

Using Data for Quality Improvement 44

Data Management 46

Big Data, Data Analytics, and Data Modeling 47

Ethical Concerns with Data and Information Use 52

Future Directions 52

Summary 53

4 Electronic Resources for Healthcare Professionals 58 Brenda Kulhanek

Information Literacy 58

Critical Assessment of Online Information 59

Social Media—Responsibilities and Ethical Considerations 61

Healthcare Information and Services 62

Online Services for Healthcare Professionals 64

Professional Organizations and Watchdog Groups 65

Healthcare Websites of Interest for Healthcare Providers 66

ELearning 67

Using Information Technology to Organize and Use Information Effectively 68

Future Directions 70

Summary 70

5 Using Informatics to Support Evidence-Based Practice and Research 73 Melody Rose

History 74

Levels of Evidence 75

Applying Information Literacy to Find the Highest Levels of Evidence 77

Contents

A01_HEBD1010_06_SE_FM.indd 3 3/16/18 8:36 PM

iv Content

Integration of EBP into Clinical Systems and Documentation 78

Managing Research Data and Information 80

Creating and Maintaining the Infrastructure to Support Research 81

Ethical and Legal Principles for Handling Data and Information in Research 83

Practices for Collecting and Protecting Research Data 84

Supporting Dissemination of Research Findings 86

Effecting Practice Change 87

Future Directions 88

Summary 89

6 Policy, Legislation, and Regulation Issues for Informatics Practice 94 Sunny Biddle, Jeri A. Milstead

The Policy Process 95

Legislation and HIT/Informatics 98

Regulation (Rule-Making) and Implications for Informatics 101

Accreditation 104

Policy Making, Interprofessional Teams, and Informatics 106

Future Directions 108

7 Electronic Health Record Systems 112 Rayne Soriano, Kathleen Hunter

Meaningful Use 114

Benefits of EHRSs 119

Current Status of EHRSs 121

Considerations When Implementing the EHRS 123

Future Directions 128

Summary 129

8 Healthcare Information Systems 135 Carolyn Sipes, Jane Brokel

Clinical Information Systems 136

Administrative Information Systems 139

Smart Technology 141

Current Topics in Healthcare Information Systems 143

Summary 145

9 Strategic Planning, Project Management, and Health Information Technology Selection 149 Carolyn Sipes

Overview of Strategic Planning 150

Information Management Components 152

One Vendor versus Best of Breeds 155

Configurability 156

Interoperability 156

Ease of Use/Usefulness of Systems 156

Planning at the Project Level—The Project Management Process 157

The Informatics Nurse’s Role as Project Manager 161

Essential Skills in Other Advanced Nurse Practice Roles 162

Future Directions 163

Summary 164

10 Improving the Usability of Health Informatics Applications 167 Nancy Staggers

Introduction to Usability 168

Definitions of Terms and Interrelationships of Concepts 169

The Goals of Usability 171

A01_HEBD1010_06_SE_FM.indd 4 3/16/18 8:36 PM

Content v

Information System Security 242

Security Mechanisms 249

Administrative and Personnel Issues 256

Levels of Access 257

Audit Trails 260

Handling and Disposal of Confidential Information 260

Special Considerations with Mobile Computing 262

Security for Wearable Technology/Implanted Devices/Bedside Technology 263

Future Directions 266

Summary 266

14 Information Networks and Information Exchange 271 Jane M. Brokel

Introduction 271

Health Information Network Models 272

Clinical Data Networks or Health Information Networks 273

Interoperability 274

International Standards 278

Nationwide Health Information Network 279

Implications of Interoperability 280

Process and Use Cases for Health Information Exchange 280

Key Factors 281

Driving Forces 284

Current Status 285

Obstacles 285

Future Directions 286

Summary 287

Usability and the System Life Cycle 172

Human–Computer Interaction Frameworks 172

Usability Methods 175

Usability Tests 179

Steps in Conducting Usability Tests 183

Future Directions 185

Summary 186

11 System Implementation, Maintenance, and Evaluation 191 Sue Evans

System Implementation 192

System Installation 203

System Evaluation 206

Summary 207

12 Workforce Development 210 Diane Humbrecht, Brenda Kulhanek

Workforce Population 210

Devising a Workforce Development Preparation Plan 212

Identifying the Scope of Efforts 214

Target Technology and Related Competencies 217

Education Methods 219

Training Resources 225

Evaluating Success 226

When Information Technology Fails (Training on Backup Procedures) 228

Future Directions 229

Summary 229

13 Information Security and Confidentiality 238 Ami Bhatt, Patricia Mulberger

Privacy, Confidentiality, Security, and Consent 239

A01_HEBD1010_06_SE_FM.indd 5 3/16/18 8:36 PM

vi Content

15 The Role of Standardized Terminology and Language in Informatics 293 Susan Matney

Introduction to Terminology 293

Languages and Classification 297

Benefits of Implementing Standardized Terminologies 309

National Healthcare Reporting Requirements 312

Issues and Concerns 313

Future Directions 313

Summary 314

16 Continuity Planning and Management (Disaster Recovery) 320 Carolyn S. Harmon

Introduction and Background 320

What Is Continuity Planning? 321

Steps in the Developing a Preparedness Program 324

Advantages of Continuity Planning 328

Disasters Versus System Failure 329

Continuity and Recovery Options 329

Planning Pitfalls 337

Using Post-Disaster Feedback to Improve Planning 338

Legal and Accreditation Requirements 338

Future Directions 340

Summary 340

17 Using Informatics to Educate 343 Diane A. Anderson, Julie McAfooes, Rebecca J. Sisk

Why Informatics? 344

Preparing the Learner 344

Educational Software Sources 344

Barriers and Benefits 345

Necessary Tools 346

Simulation and Virtual Learning Environments 354

Future Directions 363

Summary 363

18 Consumer Health Informatics 370 Melody Rose, Toni Hebda

Evolution 371

Driving Forces 372

Issues 372

Consumer Health Informatics Applications 377

The Role of the Informatics Nurse with CHI 385

The Future of CHI 388

Summary 389

19 Connected Healthcare (Telehealth and Technology-Enabled Healthcare) 398 Lisa Eisele

Introduction 398

History of Connected Health 399

Current State 400

Driving Forces 400

Connected Health Modalities 403

Implications for Practitioners 408

The Role of the INS in Connected Health 412

Future Directions 413

Summary 414

20 Public Health Informatics 418 Marisa L. Wilson

Introduction 418

Exploring Public Heath 419

Public Health Mandate 419

A01_HEBD1010_06_SE_FM.indd 6 3/16/18 8:36 PM

Content vii

Public Health Informatics 422

Public Policy Driving Informatics Change 425

Current Public Health Informatics Systems 426

New Technological Sources of Public Health Information 428

Future Directions 430

Summary 432

Appendix A: Hardware and Software 435 Athena Fernandes

Appendix B: The Internet and the Worldwide Web 439 Athena Fernandes

Appendix C: An Overview of Tools for the Informatics Nurse 441 Carolyn Sipes

Glossary 446 Index 454

A01_HEBD1010_06_SE_FM.indd 7 3/16/18 8:36 PM

This page intentionally left blank

A01_PERL5624_08_GE_FM.indd 24 2/12/18 2:58 PM

ix

The idea for Handbook of Informatics for Nurses & Healthcare Professionals first came from the realiza-tion that there were few resources that provided practical information about computer applications and information systems in healthcare. From its inception, this book served as a guide for nurses and other health- care professionals who needed to learn how to adapt and use computer applications and informatics in the work- place. Over time, this text became a reliable resource for students in a variety of healthcare professions who needed to develop informatics competencies. This book serves undergraduates who need a basic understanding, as well as those who require more depth, such as infor- matics nurse specialists, clinical nurse leaders, doctoral students, and other healthcare professionals.

After a thorough revision in response to reviewers and users of the book, the sixth edition reflects the rapid changes in healthcare information technology (HIT) and informatics. The authors endeavour to provide an understanding of the concepts, skills, and tasks that are needed for healthcare professionals today and to achieve the federal government’s national information technology goals to help transform healthcare delivery.

The sixth edition builds upon the expertise pro- vided by contributors currently involved in day-to-day informatics practice, education, and research. Both the primary editors and the contributors share an avid inter- est and involvement in HIT and informatics, as well as experience in the field, involvement in informatics groups, and a legacy of national and international pre- sentations and scholarly publications.

New to This Edition • New! All chapters thoroughly revised to reflect the

current and evolving practice of health information technology and informatics

• New! Chapter on informatics theory and prac- tice connects theoretical concepts to applications (chapter 2)

• New! Coverage of technology and caring and their symbiotic relationship

• New! Content on ethical use of information lays encompasses appropriate and inappropriate behav- iour and actions, and of right and wrong.

• New! Information on analytics and data science that explains how Big Data applies to healthcare

• New! Cutting-edge content on wearable and mobile technology security, and its impact on nursing and patient care

• New! Academic electronic health record resources and the role they play in educating the next generation of healthcare providers on documentation principles

• New! Hardware and software appendix (appendix A)

• New! Guide to the Internet (appendix B)

• New! An Overview of Tools for the Informatics Nurse (appendix C)

Changes to This Edition • The sixth edition streamlines content by combining

chapters with topics that fit together, and shifting hardware, software, and information on the Inter- net to new appendices.

• This edition reworks previous content on informa- tion systems training and presents it within the context of workforce development. The content still retains the emphasis upon privacy and confidential- ity, introduction of information policies, educational methods and resources. New content on evaluation models and training on backup procedures has also been added.

• Former content on integration, interoperability and health information exchange is now presented within the context of information networks and information exchange.

• Moves from defining evidence-based practice to a discussion of levels of evidence and using informat- ics to support evidence-based practice and research.

• Separate chapters on policy, legislation, regulatory, reimbursement, and accreditation issues were com- bined to better show the connection among these

Preface

A01_HEBD1010_06_SE_FM.indd 9 3/16/18 8:36 PM

x Preface

areas and the relationship between them and infor- mation system design and use.

• Experts from various health disciplines cover the latest on the interprofessional aspects of infor- matics with more emphasis on interdisciplinary approaches.

• Increases focus on current electronic health record issues while decreasing coverage of the historical evolution of EHRs.

• Highlights strategic planning and project management.

• Underscores the importance of patient engagement and shared decision making.

• Expands content on simulation and virtual learning environments.

Hallmark Features Learning Objectives—Learning Objectives appear at the beginning of each chapter and identify what readers can expect to learn in the chapter.

Future Directions—As the last section in each chapter, Future Directions forecasts how the topic covered in the chapter might evolve in the upcoming years.

Case Study Exercises—Case studies at the end of each chapter discuss common, real-life appli- cations, which review and reinforce the concepts presented in the chapter.

Summary—The Summary at the end of each chapter highlights the key concepts and information from the chapter to assist in the review.

References—Resources used in the chapter appear at the end.

Glossary—The glossary familiarizes read- ers with the vocabulary used in this book and in healthcare informatics. We recognize that healthcare professionals have varying degrees of computer and informatics knowledge. This book does not assume that the reader has prior knowledge of computers. All computer terms are defined in the chapter, in the glossary at the end of the book, and on the Online Student Resources Web site.

Organization The book is divided into three sections: Information and Informatics, Information Systems Development Life Cycle, and Specialty Applications. The major themes of privacy, confidentiality, and information security are woven throughout the book. Likewise, project manage- ment is a concept introduced in the strategic planning chapter and carried through other chapters. Chapters include content on the role of the informatics profes- sional, future directions relative to the topic, summary bullet points, and a case study.

Section I: Information and Informatics This section provides a foundation for why information and informatics are important to healthcare. It details the relationship between policy, legislation, regulation and accreditation and reimbursement and information system use.

• Chapter 1: Provides a definition of informatics and its significance for healthcare, discusses healthcare professionals as knowledge workers, addresses the need for uniform data and the relationship between data, big data, and evidence. This chapter also addresses the increased prevalence of information technology in healthcare, major issues in healthcare that are driving the adoption of information tech- nology, what is necessary to create an informatics culture, and includes a special section on caring and technology.

• Chapter 2: Provides information on informatics theory and practice, and nursing informatics as a discipline.

• Chapter 3: Emphasizes effective and ethical use of data and information, and includes a discussion of big data challenges and issues. Data characteristics, types, integrity, and management are covered. Cli- nician and informaticist roles pertaining to this area are discussed.

• Chapter 4: Addresses electronic resources for healthcare professionals, basic concepts and appli- cations of the Internet, including criteria for evalu- ating the quality of online information.

• Chapter 5: Discusses informatics to support evidence-based practice and research. Concepts include levels of evidence, information literacy, managing research data and information, creating

A01_HEBD1010_06_SE_FM.indd 10 3/16/18 8:36 PM

Preface xi

and maintaining the infrastructure needed to sup- port research, dissemination of evidence, and effect- ing practice change.

• Chapter 6: Examines the relationship between pol- icy, legislation, accreditation, reimbursement and HIT design and use.

• Chapter 7: Provides information on electronic health records including definition, components, incentives for adoption, benefits, current status, selection criteria, implications for collection of meaningful data and big data, current issues, and future directions.

• Chapter 8: Provides an overview of types of health- care information systems, including clinical infor- mation systems and administrative information systems, as well as decision support, knowledge representation, and smart data.

Section II: Information Systems Development Life Cycle This section covers information and issues related to the information systems development life cycle.

• Chapter 9 This chapter discusses the importance of strategic planning for information management, HIT acquisition and use and provides an overview of project management and information system selection considerations. The role of informatics professionals, particularly informatics nurse spe- cialists, in the planning process and project manage- ment are addressed, as is the process to introduce change.

• Chapter 10: Addresses the concepts of usability and health informatics applications inclusive of the role that usability plays in the system life cycle and methods of usability assessment.

• Chapter 11: Covers information system implemen- tation, maintenance, and evaluation.

• Chapter 12: Provides a comprehensive look at workforce development in relation to health infor- mation technology use.

• Chapter 13: Discusses information security and confidentiality, including practical information on ways to protect information housed in informa- tion systems and on mobile devices and addresses security for wearable and implantable information technology.

• Chapter 14: Provides detailed information about health information exchanges.

• Chapter 15: Provides an overview of the role of standardized terminology and language in infor- matics. Also includes an outline of individual lan- guages and classifications used in healthcare.

• Chapter 16: Discusses the relationship between strategic planning for the organization and the sig- nificance of maintaining uninterrupted operations for patient care. Also touches on legal requirements to maintain and restore information. Much of this chapter is geared for the professional working in information services.

Section III: Specialty Applications This section covers specialty applications of informatics.

• Chapter 17: Details ways that information tech- nology and informatics can support education of healthcare professionals, including sections on sim- ulation and virtual learning environments.

• Chapter 18: Emphasizes the relationship between health and information literacy, patient engage- ment, shared decision-making, changing healthcare delivery models, patient satisfaction, outcomes, and healthcare reform. Discusses applications of con- sumer health informatics.

• Chapter 19: Examines telehealth and connected healthcare applications, starting with a historical perspective and including driving forces, appli- cations, and implications for providers as well as informatics professionals.

• Chapter 20: Explores public health informatics and its use to maintain and improve population health.

Three appendices are included. Appendix A pro- vides basic information on hardware and software for the reader who needs a better understanding of this area. Appendix B provides information on the Internet. Appendix C provides an overview of some tools for the informatics nurse.

Instructor Resources Lecture PowerPoint showcases key points for each chapter.

Test Generator offers question items, making test creation quick and simple.

A01_HEBD1010_06_SE_FM.indd 11 3/16/18 8:36 PM

xii Preface

Student Resources New! eText offers a rich and engaging experi- ence with interactive exercises. Readers can ac- cess online or via the Pearson eText app. Note: Faculty can opt to package an eText access code card with the print textbook, or students can purchase access to the eText online.

Notice Care has been taken to confirm the accuracy of information pre- sented in this book. The authors, editors, and the publisher, however, cannot accept any responsibility for errors or omissions or for conse- quences from application of the information in this book and make no warranty, express or implied, with respect to its contents.

A01_HEBD1010_06_SE_FM.indd 12 3/16/18 8:36 PM

xiii

Special thanks to Kathy Hunter, who agreed to join me on this 6th edition, lending her knowledge, insights, and support when I most needed it and never said “no” despite her many other commitments. A special thanks to Patricia Czar, RN, without whom there would be no

Handbook of Informatics for Nurses & Healthcare Professionals today. Pat actively contributed to the book from the original outline through to the present, provid- ing her knowledge, insights, organizational skills, support, and friendship. Pat was active in informatics for more than 25 years, serving as manager of clini- cal systems at a major medical center where she was responsible for planning, design, implementation, and ongoing support for all of the clinical information systems. Pat was also active in several informatics groups, presented nationally and internationally, and served as a mentor for many nursing and health infor- matics students. She is now fully retired and enjoying time with her family.

We acknowledge our gratitude to our loved ones for their support as we wrote and revised this book. We are grateful and excited to have work from our contributors who graciously shared their knowledge and expertise for this edi- tion. We are grateful to our co-workers and professional colleagues who provided encouragement and support throughout the process of conceiving and writing this book. We appreciate the many helpful comments offered by our reviewers. Finally, we thank Lisa Rahn, Michael Giacobbe, Susan Hannahs, Daniel Knott, Taylor Scuglik, and all of the persons who worked on the production of this edi- tion for their encouragement, suggestions, and support.

Thank You

This edition brings in work from multiple contributors for a robust coverage of topics throughout the book. We thank them for their time and expertise. We would also like to thank all of the reviewers who carefully looked at the entire manuscript. You have helped shape this book to become a more useful text for everyone.

Acknowledgments

A01_HEBD1010_06_SE_FM.indd 13 3/16/18 8:36 PM

This page intentionally left blank

A01_PERL5624_08_GE_FM.indd 24 2/12/18 2:58 PM

Contributors

Diane A. Anderson, DNP, MSN, RN, CNE Chapter 17: Using Informatics to Educate Associate Professor, MSN Specialty Tracks ~ Nurse Educator, Chamberlain College, Downers Grove, IL

Ami Bhatt, DNP, MBA, RN, CHPN, CHCI Chapter 13: Information Security and Confidentiality Dr. Bhatt is currently enrolled in the DNP to PhD program at University of Nevada, Las Vegas, NV

Sunny Biddle, MSN, RN Chapter 6: Policy, Legislation, and Regulation Issues for Informatics Practice Circulating Nurse in the Operating Room at Genesis Healthcare in Zanesville, OH and Clinical Instructor for Central Ohio Technical College in Newark, OH

Jane M. Brokel, PhD, RN, FNI Chapter 8: Healthcare Information Systems Chapter 14: Information Networks and Information Exchange Section Instructor at Simmons College, Boston, MA Adjunct faculty for the University of Iowa College of Nursing, Iowa, City, IA

Jennifer A. Brown, MSN, RN, HNB-BC Chapter 1: An Overview of Informatics in Healthcare Faculty, Bronson School of Nursing at Western Michigan University in Kalamazoo, Michigan in the undergraduate and RN-BSN programs.

Lisa Eisele, MSN, RN Chapter 19: Connected Healthcare (Telehealth and Technology-enabled healthcare) Chief - Quality, Performance & Risk Management Manchester VA Medical Center, Manchester VA

Sue Evans, MSN RN-BC Chapter 11: System Implementation, Maintenance, and Evaluation Informatics Nurse II University of Pittsburgh Medical Center East, Monroeville, PA

Athena Fernandes DNP, MSN, RN-BC Appendix A: Hardware and Software Appendix B: A Guide to the Internet and World Wide Web Senior Physician Systems Analyst, Penn Medicine Chester County Hospital, West Chester, PA

Carolyn S. Harmon, DNP, RN-BC Chapter 16: Continuity Planning and Management Clinical Assistant Professor and Program Director for the Masters of Nursing Informatics and the Masters of Nursing Administration at University of South Carolina, Columbia, SC

Toni Hebda, PhD, RN-BC, MSIS, CNE Chapter 3: Effective and Ethical Use of Data and Information Chapter 18: Consumer Health Informatics Professor, Chamberlain College of Nursing MSN Program, Downers Grove, IL

Taryn Hill, PhD, RN Caring for the Patient Not the Computer in Chapter 1: An Overview of Informatics in Healthcare Dean of Academic Affairs for Chamberlain College of Nursing, Columbus, OH

Diane Humbrecht, DNP, RN Chapter 12: Workforce Development Chief Nursing Informatics Officer, Abington Jefferson Health, Abington, PA

Kathleen Hunter, PhD, RN-BC, CNE Chapter 3: Effective and Ethical Use of Data and Information Chapter 7: Electronic Health Record Systems Professor, Chamberlain College of Nursing MSN Program, Downers Grove, IL

Brenda Kulhanek, PhD, MSN, MS, RN-BC Chapter 4: Electronic Resources for Healthcare Professionals Chapter 12: Workforce Development AVP of Clinical Education for HCA in Nashville, TN

xv

A01_HEBD1010_06_SE_FM.indd 15 3/16/18 8:36 PM

xvi Contributors

Susan Matney, PhD, RN-C, FAAN Chapter 15: The Role of Standardized Terminology and Language in Informatics Senior Medical Informaticist, Intermountain Healthcare, Murray, UT

Julie McAfooes, MS, RN-BC, CNE, ANEF, FAAN High-fidelity simulation, software, support, and certification in Chapter 17: Using Informatics to Educate Web Development Manager for the online RN-to-BSN Option at the Chamberlain of Nursing, Downers Grove, IL

Jeri A. Milstead, PhD, RN, NEA-BC, FAAN Chapter 6: Policy, Legislation, and Regulation Issues for Informatics Practice Professor and Dean Emerita, University of Toledo College of Nursing, Toledo, OH

Patricia Mulberger, MSN, RN-BC Special Considerations with Mobile Computing in Chapter 13: Information Security and Confidentiality Clinical Informatics Quality Supervisor, Kalispell Regional Healthcare, Kalispell MT

Melody Rose, DNP, RN Chapter 5: Using Informatics to Support Evidence-based Practice and Research Chapter 18: Consumer Health Informatics Assistant Professor of Nursing. Cumberland University Jeanette C. Rudy School of Nursing, Lebanon, TN

Carolyn Sipes, PhD, CNS, APN, PMP, RN-BC Chapter 8: Healthcare Information Systems Chapter 9: Strategic Planning, Project Management, and Health Information Technology (IT) Selection Appendix C: An Overview of Tools for the Informatics Nurse Professor, Chamberlain College, Downers Grove, IL

Rebecca J Sisk, PhD, RN, CNE Virtual Learning Environment in Chapter 17: Using Informatics to Educate Professor, Chamberlain College Downers Grove, IL

Rayne Soriano, PhD, RN Chapter 7: Electronic Health Record Systems Regional Director for Medicare Operations and Clinical Effectiveness. Kaiser Permanente, San Francisco, CA

Nancy Staggers, PhD, RN, FAAN Chapter 10: Improving the Usability of Health Informatics Applications President, Summit Health Informatics and adjunct professor, Biomedical Informatics and College of Nurs- ing University of Utah College, Salt Lake City, UT

Maxim Topaz PhD, MA, RN Chapter 2: Informatics Theory and Practice Harvard Medical School & Brigham Women’s Health Hospital, Boston, MA, USA

Marisa L. Wilson DNSc MHSc RN-BC CPHIMS FAAN Chapter 20: Public Health Informatics Associate Professor and Specialty Track coordinator for the MSN Nursing Informatics program at the Uni- versity of Alabama at Birmingham School of Nursing.

A01_HEBD1010_06_SE_FM.indd 16 3/16/18 8:36 PM

xvii

Janet Baker DNP, APRN, ACNS-BC, CPHQ, CNE Associate Dean Graduate Nursing Programs Ursuline College, The Breen School of Nursing Pepper Pike, Ohio

Theresa L. Calderone, EdD, MEd, MSN, RN-BC Assistant Professor of Nursing Indiana University of Pennsylvania Indiana, PA

Vicki Evans, MSN, RN, CEN, CNE Assistant Professor of Nursing University of Mary-Hardin Baylor Belton, TX

Kathleen Hirthler DNP, CRNP, FNP-BC Chair, Graduate Nursing; Associate Professor Wilkes University, Passan School of Nursing Wilkes Barre, PA

Arpad Kelemen, Ph.D. Associate Professor of Informatics University of Maryland School of Nursing Baltimore, MD

Michelle Rogers, PhD, MS, MA, BS Associate Professor of Information Science Drexel University Philadelphia, PA

Charlotte Seckman, PhD, RN-BC, CNE, FAAN Associate Professor, Nursing Informatics Program University of Maryland School of Nursing Baltimore, MD

Nadia Sultana, DNP, MBA, RN-BC Program Director and Clinical Assistant Professor, Nursing Informatics Program New York University New York, NY

Reviewers

A01_HEBD1010_06_SE_FM.indd 17 3/16/18 8:36 PM

This page intentionally left blank

A01_PERL5624_08_GE_FM.indd 24 2/12/18 2:58 PM

Toni Hebda, PhD, RN-C, CNE, is a professor with the Chamberlain College of Nursing. MSN Program teaching in the nursing informatics track. She has held several academic and clinical positions over the years and worked as a system analyst. Her interest in informatics provided a focus for her dissertation, subse- quently led her to help establish a regional nursing informatics group, obtain a graduate degree in information science, and conduct research related to informat- ics. She is a reviewer for the Online Journal of Nursing Informatics. She is a member of informatics groups and has presented and published in the field.

Kathy Hunter, PhD, FAAN, RN-BC, CNE, is a professor with the Chamberlain College of Nursing MSN Program, teaching in the nursing informatics track. She has more than 40 years of experience in the fields of nursing informatics, healthcare informatics, and nursing education. After conducting clinical prac- tice in critical care and trauma nursing for several years, she began practicing nursing informatics (NI), working with end users and with information systems design, development, testing, implementation and evaluation. She has presented nursing-informatics research in national and international meetings as well as publishing numerous articles in peer-reviewed journals. Collaborating in a com- munity of practice with nursing-informatics faculty at Chamberlain, Dr. Hunter led the work resulting in the development of the TIGER-based Assessment of Nursing Informatics Competencies (TANIC) tool.

About the Authors

xix

A01_HEBD1010_06_SE_FM.indd 19 3/16/18 8:37 PM

This page intentionally left blank

A01_PERL5624_08_GE_FM.indd 24 2/12/18 2:58 PM

1

H er

o Im

ag es

/G et

ty Im

ag es

Chapter 1

An Overview of Informatics in Healthcare Jennifer A. Brown, MSN, RN, HNB-BC Taryn Hill, PhD, RN Toni Hebda, PhD, RN-C

Learning Objectives

After completing this chapter, you should be able to:

• Provide an overview of the current state of healthcare delivery.

• Discuss the role that technology plays in healthcare.

• Provide a definition for informatics.

• Discuss the significance of informatics for healthcare.

• Describe the process required to create an informatics culture.

• Examine the relationship between technology, informatics, and caring.

The healthcare delivery system today is a complex system faced with multiple, competing demands. Among these demands are: calls for increased quality, safety, and transparency; evolving roles for practitioners; a shift in consumer-provider relationships; eliminating dis- parities in care; adopting new models of care; the development of a learning health sys- tem (LHS); advanced technology as a means to support healthcare processes and treatment options; and providing a workforce with the skills needed to work in a highly technology- laden environment that is reliant upon data and information to function.

Technology is a pervasive part of every aspect of society including healthcare delivery. Many suggest that health information technology (HIT) provides the tools to enable the delivery of safe, quality care in an effective, efficient manner while improving communication and decreasing costs (Institute of Medicine, 2012). HIT was named as one of nine levers that stakeholders could use to align their efforts with the National Strategy for Quality Improve- ment in Health Care, a collaborative effort also known as the National Quality Strategy, a mandate of the 2010 Affordable Care Act (ACA). The National Quality Strategy, published in 2011, represented input from more than 300 groups and organizations from various sectors of

M01_HEBD1010_06_SE_C01.indd 1 3/16/18 1:53 PM

2 Chapter 1

healthcare industry and the public (Agency for Healthcare Research and Quality, 2017). Yet, the healthcare sector has been slow to adopt and use technology to its full potential. Lucero (2017) noted that the failure for technology in healthcare to live up to its full promise to the present is not surprising given the complexity of healthcare delivery. So, what is information technology? Information technology (IT) is a broad term referring to the process of search- ing, organizing, and managing data supported by the use of computers. It has also come to include electronic communication. IT represents only a portion of the technology found in healthcare today, but is significant because data leads to information, which in turn provides knowledge. This chapter and the book as a whole will discuss the role that informatics plays to help address the multiple challenges facing healthcare today.

Informatics Before we can discuss the role of informatics in healthcare, infomatics must first be defined. The American Medical Informatics Association (AMIA) (2017, Para. 1) states that informatics is an interdisciplinary field that draws from, as well as contributes to, “computer science, decision science, information science, management science, cognitive science, and organi- zational theory.” Informatics drives innovation in how information and knowledge man- agement are approached. Its broad scope encompasses natural language processing, data mining, research, decision support, and genomics. Health informatics encompasses several fields that include:

• Translational bioinformatics. This area deals with the storage, analysis, and interpretation of large volumes of data. It includes research into ways to integrate findings into the work of scientists, clinicians, and healthcare consumers.

• Clinical research informatics. This area concentrates on discovery and management of new knowledge pertinent to health and disease from clinical trials and via secondary data use.

• Clinical informatics. The concentration here is on the delivery of timely, safe, efficient, effective, evidence-based and patient-centered care (Levy, 2015). Examples include nursing informatics and medical informatics. Nursing informatics has its own scope and standards for practice as set forth by the American Nurses Association (ANA) as well as certification established by the American Nurses Credentialing Center (ANCC) ( American Nurses Association, 2015a). AMIA began the process, in 2007, of defining clini- cal informatics and its competencies, to lay the foundation for a credentialing process to recognize competence of clinical informaticists (Shortliffe, 2011). There is also discussion at a global level on specialty-board certification for physicians in clinical informatics (Gundlapall et al., 2015).

• Consumer health informatics. The focus here is the consumer, or patient, view and the structures and processes that enable consumers to manager their own health.

• Public health informatics. Efforts here include surveillance, prevention, health promotion, and preparedness.

As might be surmised from a review of the above list, there are areas of overlap among the fields.

Informatics and its subspecialties—including nursing informatics—continue to evolve as has the terminology used to discuss this field. For example, medical informatics was previously used as the umbrella term under which the subspecialties of health informatics fell.

M01_HEBD1010_06_SE_C01.indd 2 3/16/18 1:53 PM

An Overview of Informatics in Healthcare 3

The Relevance of Informatics for Healthcare Informatics is an essential component of healthcare today. The Institute of Medicine (2013a) noted its vision for the development of a continuously learning health system in which sci- ence, informatics, incentives, and culture are aligned for continuous improvement and inno- vation, and new knowledge is captured as a by-product of care processes. Together, HIT and informatics have been hailed as tools that can streamline processes, improve the quality of care delivered, reduce mortality, cut costs, and collect data to support learning (Institute of Medicine, 2012, 2015; Kohli & Tan, 2016; Lucero, 2017; Luo, Min, Gopukumar, & Yiqing, 2016; McCullough, Parente, & Town, 2016; Pinsonneault, Addas, Qian, Dakshinamoorthy, & Tamblyn, 2017). In fact, the Institute of Medicine (2013b, p. 1) stated that “digital health data are the lifeblood of a continuous learning health system.” Achieving this learning health system will require the work of many individuals and organizations.

There are several factors to consider on the journey to a learning healthcare system. These include:

• Healthcare professionals are knowledge workers.

• Structures must be in place to support the collection, interpretation, and reuse of data in a meaningful way.

• Evidence-based practices are a pre-requisite to achieving optimal outcomes.

• Big data and data analytics are quickly becoming a major source of evidence, augment- ing, and even replacing, other traditional forms of evidence such as clinical trials.

• HIT and all forms of technology are present but best use is inconsistent.

• Healthcare reform and a learning healthcare system are intricately linked.

• Patient safety and the need to improve quality of care are drivers for healthcare reform.

Each of these will be discussed briefly.

Knowledge Work Nurses and other healthcare professionals have a long tradition of gathering data, which is then used to create information and knowledge. When previous knowledge and experience are applied appropriately to take action or intervene in some fashion, it is known as wisdom. These processes constitute a major part of the clinician’s day and, when done well, yield good outcomes. As an example, a piece of data without context has no meaning. The number 68 in isolation conveys nothing. It could be an age, a pulse rate, or even a room number, but in and of itself, there is no way to know what it means. However, if 68 is determined to be a pulse rate, the nurse can make the determination that this falls within the normal range, indicating that the patient is in no distress and requires no intervention. On the other hand, if that same number represents the rate of respirations per minute, the patient is in respiratory distress and immediate intervention is required.

Gaberson and Langston (2017) noted that changes in the healthcare system, inclusive of demands for safe, accessible, quality care, have increased both the awareness of and demand for well-prepared knowledge workers. Gaberson and Langston also cited the assertion of the landmark 2010 Institute of Medicine report, The Future of Nursing: Leading Change, Advancing Health, that nursing is an appropriate profession to play a major role in transforming the healthcare system; yet, nursing education has not adequately prepared its graduates for this role. As a consequence, there is a need to better prepare nurses—and other healthcare professionals—during their basic education for this role and to provide better options to aid

M01_HEBD1010_06_SE_C01.indd 3 3/16/18 1:53 PM

4 Chapter 1

the new professional to assume the knowledge-worker role and to maintain essential com- petencies in this area.

Structures to Support Meaningful Use of Data To be useful, data and information must be available when needed, to whom it is needed, and in a form that can be analyzed or used. Historically, the healthcare delivery system has collected huge amounts of data and information from different sources and in different formats, creating data silos within departments and facilities. Without organization, this data and information has limited value, even at its collection site, and is not amenable to sharing for learning purposes. The use of electronic health records (EHRs) moved data and information to a digital format, which is conducive to organization, analysis, and sharing, but differences in format still make analysis difficult. Data exists in raw and processed states and unstructured and structured forms. Examples of unstructured data include docu- ments, email, and multimedia. Structured data fits into predetermined classifications such as that seen with a list of selectable options that can easily be quantified. Even before the widespread adoption of EHRs, there was a growing recognition that improved commu- nication among professionals required the adoption of standardized languages and ter- minologies to ensure that a concept had the same meaning in all settings; this also makes generalization of research findings possible. One example of a standardized language that is familiar to most nurses is NANDA, which was created by the North American Nursing Diagnosis Association to provide standardized terms for nursing diagnoses. Standardized languages and terminologies can be integrated into EHRs. A lack of data standardization jeopardizes opportunities for learning because important data may not be available for analysis ( Auffray et al., 2016). Standardization of data and its collection in a digital format in databases facilitate collecting, sorting, retrieval, selection, and aggregation of data to a degree never before possible. Aggregate data can be analyzed to discover trends and, subsequently, to inform and educate.

Researchers use both qualitative and quantitative methods to analyze data. Qualitative methods focus on numbers and frequencies, with the goal of finding relationships or vari- ables specific to an outcome. Qualitative methods are variable and not focused on counting. These methods can include any data captured. This data can be in the form of questionnaires, surveys including web surveys, interviews, list serves, and email. Electronic data collection tools include personal digital assistants or laptop computers.

Another important facet of information access is related to the electronic literature data- bases for the health sciences, business, history, government, law, and ethics that healthcare professionals and administrators use to keep up-to-date and inform their practices. Libraries purchase electronic literature databases that users can easily search using keywords, Boolean search operators, title, author, or date to find relevant information. Literature databases use key terms to index collections. Medical subject headings (MeSH) are used by the controlled vocabulary thesaurus of the National Library of Medicine (NLM) to index articles in PubMed, a free search engine maintained by the NLM. PubMed is used to access the MEDLINE biblio- graphic database. Some other examples of literature databases relevant for healthcare include EBSCO, Ovid, ProQuest, CINAHL, and Cochrane Library. Becoming familiar with the data- bases most relevant to one’s purpose or focus is important. Adept use requires time and practice. When searching a database, one should define the subject and the question; then, search for the evidence in multiple components of the literature: for example, use evidence from multiple studies (not just one random study), incorporate what was learned into prac- tice, and evaluate the impact of what was implemented.

M01_HEBD1010_06_SE_C01.indd 4 3/16/18 1:53 PM

An Overview of Informatics in Healthcare 5

Evidence-Based Practice Evidence-based practice (EBP) entails using the current best evidence for patient-care decisions in order to improve the consistency and quality of patient outcomes (Mackey & Bassendowski, 2017). It requires critical thought processes. EBP provides the foundation for clinical-practice guidelines and clinical decision-support tools that are widely found in health- care organizations today. EBP in nursing evolved from Florence Nightingale’s idea that she could improve patient outcomes through systematic observations and application of subse- quent learning. EBP has been further defined by the International Council of Nurses (2012) as an approach that incorporates a search for the best available, current evidence with clinical expertise and patient preferences.

Big Data and Big Data Analytics According to the National Academies of Sciences, Engineering, and Medicine (2017), a learning health system is one that uses real-time evidence for continuous improvement and innovation. The implications of real-time evidence are that traditional research and publication cycles where months, or even years, transpire from the time of research until dissemination of results no longer satisfy the criteria for best evidence because data may no longer be current or timely. Real-time data for analysis requires different methods, tools, and dissemination methods. Enter big data and big data analytics.

Big data are very large data sets that are beyond human capability to manage, let alone ana- lyze, without the aid of information technology. Big data has been collected for years by retail- shopping organizations. As an example, consider the shopper’s card that nearly everyone has for their favorite grocery store. In exchange for special store discounts on select merchandise or points earned for discounts, the store collects information on shopper preferences every time the card is used. The aggregate data that healthcare providers collect via their EHRs is a type of big data. Another example of big data is seen when healthcare providers submit data collected for meaningful use core data (with one exemplar being smoking status) to the US Centers for Medicare and Medicaid Services (CMS) (2010), CMS analyzes the data for trends, with the intent to better allocate funds and services to improve care coordination and population health.

Big data, and the technologies used to reveal the knowledge within it, provide new opportunities for healthcare to discover new insights and create new methods to improve healthcare quality (Luo, Min, Gopukumar, & Yiqing, 2016). Furthermore, the computing speed associated with big data (Kaggal et al., 2016) provides a promising development to make the LHS possible. A new science, known as data science, has emerged to deal with all aspects of big data including data format, cleaning, mining, management, and analysis.

Analysis of big data, or analytics, looks for patterns in data, then uses models to recommend actions (Wills, 2014). Analytics can be used to forecast the likelihood of an event. Real-time analytics use current data from multiple sources to support decisions; this may result in powerful tools useful at the bedside as well as to support executive-level thought processes. Business intelligence is another term that is used when discussing best use of data, although business intelligence is a broader term that encompasses a plan, strategy, and tool sets to support decisions.

Increased Prevalence of Technology in Care Settings According to recent projections, US hospital adoption of EHRs is expected to surpass 98% by the end of 2017, with adoption by physicians running slightly below that figure (Bulletin Board, 2016; Orion Market Research, 2017). EHRs are also found in long-term care settings,

M01_HEBD1010_06_SE_C01.indd 5 3/16/18 1:53 PM

6 Chapter 1

although adoption rates there lag behind hospital and physician-office settings (EHR Adop- tion, 2017) . There are also many different types of technology found at the bedside, or point of care. These range from point of care computer terminals to access patient records or lit- erature databases to monitoring biometric measures such as pulse, heart rate and rhythm, blood pressure, oxygen saturation, and many tests that were formally only done in labora- tory settings. There are also medication-dispensing cabinets, smart-technology that includes medication-administration infusion pumps that link with provider order entry, pharmacy, and medication-administration systems for greater safety. A growing number of implantable devices such as insulin pumps, pacemakers, and defibrillators, and various telehealth appli- cations such as telestroke consultations that allow the neurologist at another site to evaluate and communicate with the stroke victim and attending family and care givers. There are also telesitter applications that allow an individual at a central location to monitor several patients at one time, observing them for attempts to get out of bed without assistance, and having the capability to verbally reorient them or call for further assistance. Many of these technologies already have the capability to communicate and input data into EHRs. There are also voice- activated, hands-free communication devices for staff use. Technology is supplementing work once done by ancillary staff. There are robots that deliver supplies while other robots use ultraviolet light to disinfect patient rooms and operating rooms.

The range of technology available in the home includes telemonitoring and care devices to track congestive heart patients, the mentally ill, and many more conditions. The number and range of mobile applications available to track wellness and manage chronic healthcare conditions is growing at an exponential rate. Patients have implantable devices to monitor their cardiac function, control seizures, control pain, and control the function of prosthetics. Robots to assist with care are expected to become commonplace in the near future.

The move to a technology-laden environment has implications for informatics. Informat- ics specialists are prepared to design, implement, and evaluate technologies that support healthcare providers and consumers.

Healthcare Reform Health reform has many drivers. The United States spends more per capital on healthcare than any other nation in the world, without commensurate results (Robert Wood Johnson Foundation, 2017). In one effort to enact change, value-based payment models reward pro- viders for quality of care provided and efficient resource use rather than volume of services. In another effort, the enactment of the American Recovery and Reinvestment Act (ARRA) in 2009, along with its component Health Information Technology for Economic and Clinical Health (HITECH) Act, provided economic stimuli and incentives for the adoption of EHRs, in alignment with the goal that each person in the United States would have a certified digital health record by 2014. As of 2016, this goal was achieved by more than 98% of nonfederal acute care hospitals. These digital records meet the technical capabilities, functionality, and security criteria promulgated by the Center for Medicaid and Medicare Services (Office of the National Coordinator for Health Information Technology, 2017a). The push for EHRs was consistent with the thinking that a longitudinal health record would improve access to infor- mation and consequently improve care. HITECH also ensured the collection of aggregate data that could be used to improve policy decisions relative to allocation of services and population health. Digital data also facilitates collection of data needed to measure quality of healthcare delivery, as well as improving data dissemination, as digitation allows easier data sharing.

Other drivers for healthcare reform include calls for improved safety and quality, trans- parency, the rise of consumerism with greater patient participation in planning care, and changing provider-patient relationships.

M01_HEBD1010_06_SE_C01.indd 6 3/16/18 1:53 PM

An Overview of Informatics in Healthcare 7

The Push for Patient Safety and Quality Despite life or death consequences of decisions, healthcare is not as safe as it might be. Inef- fective collaboration and poor communication have led to fragmented care and potentially dangerous errors and poor patient outcomes (Titzer, Swenty, & Mustata Wilson, 2015). The World Health Organization (WHO) (2017, Para. 1) refers to patient safety as a “fundamental principle of health care,” calling for policy, leadership, data to drive improvements, patient engagement, and a skilled workforce to make healthcare safer. The Joint Commission Interna- tional publishes patient safety goals that are integrated into the national accreditation process (The Joint Commission, 2017). Joint Commission International (2017) lists six patient safety goals that focus upon correct identification, effective communication, improved safety of high-alert medications, procedures that do not introduce harm, decreased risk of healthcare- acquired infections, and reduced risks of harm secondary to falls. HIT can improve safety and quality through alerts and decision support that help to improve the hand-off process—a point where many errors occur—and through the use of checklists. Zikhani (2016) noted that there are active and latent errors. Active errors include mistakes, slips, and lapses made by clinicians, while latent errors occur with imperfect organization design such as those seen with incomplete procedures, poor training, and poor labeling. Zikhani outlined steps to pre- vent errors in healthcare that include:

• Checklists that can prevent slips and lapses.

• Tools that improve communication such as hand-off tools.

• Automation when possible.

• Simplification, organization, and standardization.

• Not allowing errors to happen. An example of the latter might be the bar-code adminis- tration system that tells the nurse that it is not the correct medication during the medica- tion administration process

Clearly, these processes lend themselves well to automation, or technology. Technology can also be used to simulate clinical scenarios to educate the members of

an interprofessional team (Titzer, Swenty, & Mustata Wilson, 2015). Nurse leaders have rec- ognized the importance of integrating nursing informatics into undergraduate curricula by adding an informatics-competency category to the quality and safety curriculum developed by the Quality and Safety Education for Nurses (QSEN) project (QSEN Institute, 2017a). Many hospitals have elearning systems or use their intranets to provide ongoing education for personnel (Chuo, Liu, & Tsai, 2015).

Another effort to improve the coordination of care has led to new care models such as accountable care organizations (ACOs) and patient medical homes (PMHs). ACOs bring together primary care providers, specialists, and hospitals to share information and coordinate care and payment plans with the aims of greater efficiency and quality at a lower cost and, ideally, with less aggravation for the patient (Dewey, 2016). PMHs also bring together an interdisciplinary team that networks with other practices and networks to deliver or improve access to services (Hefford, 2017). Hefford (2017) noted that PMHs represent a move towards an integrated system of care. Team-based healthcare delivery models require great levels of collaboration (Rajamani et al., 2015). All models are dependent upon data, particularly shared data, for success.

Another model of care is seen with the changing dynamics of the provider-patient relationship. In the past, patients relied upon the judgment of their provider, often without question. However, with the rise in consumerism and widespread recognition that health- care reform requires input from everyone, including consumers, patients are encouraged to be involved in their healthcare decisions. The transition from passive recipient to active

M01_HEBD1010_06_SE_C01.indd 7 3/16/18 1:53 PM

8 Chapter 1

participant requires several skills that include language literacy, health literacy, digital lit- eracy, and transparency. The latter—transparency—requires access to information. The digi- tization process—making information available in electronic format—makes it easier to post and share information needed to make health decisions.

Provider roles are also changing and evolving. In addition to traditional roles, providers serve as gatekeeper to services, coach, navigator, and, sometimes, informatician (Johnson, 2015). And at a time where not every local practitioner has privileges at local hospitals, or patients are transported to other facilities, the hospitalist fills that void—a role that is still new to many healthcare consumers.

Creating an Informatics Culture While informatics is much more than data management, knowledge that is derived from data and information is a central tenet. Creating a knowledge strategy and the infrastructure, expertise, and tools required to discover new learning and knowledge in data, particularly big data, fits well within the scope of informatics (Dulin, Lovin, & Wright, 2016; Kabir & Carayannis, 2013) . An informatics culture requires a vision to develop the policies, funding, infrastructure, and education to instill the knowledge and skills needed by all healthcare executives, clinicians, and informaticists, and the tools to gather and analyze amassed data. The process to do this takes time.

The first step in the process is assessing the current state to determine gaps (How Informatics can reshape healthcare, 2016). A highly innovative culture provides a solid foundation with the EHR playing a key role, because it provides a view of what is going on within an organization and beyond as data from healthcare exchanges and national data sets are examined.

Foundational Skills There are foundational skills that are required for an information-driven culture. These include computer literacy, information literacy, and (for the consumer), health literacy.

Computer literacy is a term used to refer to the basic understanding and use of comput- ers, software tools, spreadsheets, databases, presentation graphics, social media, and com- munication via email. The fundamentals of basic literacy—the ability to read, write, and comprehend—are prerequisite. Without a basic understanding of literacy, barriers to other forms of literacy cannot be addressed (Nelson & Staggers, 2018). Health informatics is built on overlapping layers of literacies.

Information literacy is the ability to read and understand the written word and numbers as well as the ability to recognize when information is needed. One of the biggest challenges today is making health information accessible to all without regard to background, educa- tion, or level of literacy.

Health literacy is the ability to understand and act upon basic healthcare information. A simple example would be how a person acts upon a change in diet in relation to a new medical diagnosis. Clearly each type of literacy is important for both healthcare consumer and healthcare worker.

Creating a Policy, Legal, and Reimbursement Framework Professional organizations and informaticists have been working to create an informatics cul- ture for some time through their involvement in national and organizational policy-setting. As an example, the American Nurses Association (2014) position statement Standardization and Interoperability of Health Information Technology: Supporting Nursing and the National Quality

M01_HEBD1010_06_SE_C01.indd 8 3/16/18 1:53 PM

An Overview of Informatics in Healthcare 9

Strategy for Better Patient Outcomes called for standard representation and interoperability of data collected in EHRs and other HIT. The National Association of Clinical Nurse Specialists (2017) set two goals relative to HIT for their 2016–2018 public policy agenda that included representing the role of the clinical nurse specialist in relevant legislative, policy, and advocacy efforts for increased access to healthcare via the use of technology. The US Office of the National Coordinator for Health Information Technology (ONCHIT), the federal entity charged with coordinating national efforts to implement and use HIT and electronic exchange of health information, invites input from healthcare professionals and consumers (HealthIT.gov, 2016). ONCHIT also has many committees with healthcare professions representation. Informatics groups, inclusive of the American Medical Informatics Association, American Nursing Infor- matics Association, Health Information Management Systems Society (HIMSS), and the Alliance for Nursing Informatics (ANI), include public policy related to HIT-enabled care among their goals (Collins, Sensmeier, Weaver, & Murphy, 2016; Health Information Systems Society, 2017a).

Ethical Framework Ethics is the formal study of values, character, and/or conduct of individuals or collections of individuals from a variety of perspectives or viewpoints (American Nurses Association, 2015b). The field of health informatics focuses on using computers to enhance the way health infor- mation is processed. Today, the Internet opens up multiple avenues for obtaining information. Most links on the information highway do not have an overseer or monitor screening for good ethical decision making. This process is individual and personal, based on standards and the ability to differentiate right from wrong. Ethical decision making is the basis for this process. There are also issues related to how information collected for one purpose may be used for another. In a work that remains relevant today Beauchamp and Childress (1994) proposed four simple guiding principles for moral action. First is autonomy. Autonomy is the individual’s freedom to control interferences by others, retaining a personal capacity for intentional action. Second is nonmaleficence: the obligation for doing no intentional harm, Third is beneficence, which refers to actions that result in positive outcomes in which benefits and utility are balanced. Finally, fourth is justice, which refers to the standards practiced by healthcare professionals. Professional associations for informatics also have codes of ethics that provide guidance for ethical use of data and information.

Workforce Preparation Fox, Flynn, Clauson, Seaton, & Breeden, (2017, p. 1) noted that “informatics education for clinicians is a national priority,” particularly since there is a lack of consistency in teaching informatics competencies. Informatics competencies are needed to help healthcare profes- sionals manage and use technology effectively. The Institute of Medicine (2012) recognized the need for a workforce prepared to work with technology. The Technology Informatics Guiding Education Reform (TIGER) Initiative is another effort that grew out of the need to develop informatics skills among an interprofessional workforce (Healthcare Information and Management Systems Society, 2017). Informatics competencies are delineated for nursing graduates by the American Association of Colleges of Nursing, National League for Nursing, and the QSEN Institute, among others.

QSEN Institute identified quality and safety competencies for nurses that fit well with an informatics culture. These competencies include: patient-centered care, teamwork, evidence- based practice, quality improvement, safety, and informatics. Educators can use the QSEN framework as a guide. Teaching strategies can start with incorporating the QSEN compe- tencies into curricula via classroom, simulation lab, and clinical strategies. The goal of the competencies is to use information and technology to communicate, manage knowledge,

M01_HEBD1010_06_SE_C01.indd 9 3/16/18 1:53 PM

http://healthit.gov/
10 Chapter 1

mitigate error, and support decision making (QSEN Institute, 2017b). The institute recom- mends incorporating the competencies beginning in the first semester of education and continuing throughout the nursing program. The competencies are formatted into three categories: knowledge, skill, and attitude. An example of knowledge would be the ability to contrast benefits and limitations, understand the value of databases for patient care monitor- ing, and establish a good understanding of terminology and interoperability of systems. An example of skills is for the nurse to play an active role in the design, promotion and modeling of standard practice. Nurses are an important member of the healthcare informatics team that can bring a clinical lens to the development table. Attitude incorporates nursing values whether it is in the realm of reporting or preventing errors, improving patient safety in a no- blame environment, and acting as a sentry for self, patients, and family. QSEN (2017c) also lists competencies for nurses prepared at the graduate level.

Hersh et al. (2014) spoke to the need for physicians needing informatics competen- cies because of their interaction with EHRs, clinical decision support, quality measures and improvement, personalized medicine, personal health records, and telehealth. Obvi- ously, physicians are not the only healthcare professionals who use EHRs, decision support, telehealth, personal health records, or have concerns related to quality measurement and improvement, so all clinicians are impacted.

The Office of the National Coordinator for Health Information Technology (2017b) funded curriculum development centers to develop curricula and education in response to the mandate by the HITECH Act of 2009 to aid institutions of higher learning to establish or expand medical-informatics education programs. Twenty topics were developed originally, and more recently, five additional topics were developed in population health, care coordi- nation and interoperability, value-based care, analytics, and patient-centered care. Materials developed through this effort are available for use at no cost.

Workforce preparation is under review in other areas of the world as well. One exemplar is the collaborative effort between the United States and European Union, which yielded an extensive list of competencies, including an informatics category. The workforce published the list of competencies as a tool for self-assessment. The Health Information Technology Competencies (HITCOMP) tool may be accessed without charge at http://hitcomp.org/

Technical Infrastructure The technical infrastructure for healthcare informatics and exchange of information is the result of policy, legislation, funding, a multitude of agencies that are working to advance HIT for the benefit of healthcare, and technical standards. Policy and legislation and the relation- ship with funding will be discussed later in the book. One of the most important US agencies to advance HIT is the Agency for Healthcare Research and Quality (AHRQ). AHRQ is a divi- sion of the US Health and Human Services committed to research and evidence to improve the safety and quality of healthcare and to providing education for healthcare professionals that will enable them to improve care (Agency for Healthcare Research and Quality, n.d.).

Another agency that is a division of the US Health and Human Services is the National Institutes of Health (NIH). While NIH does not focus on technology to the same extent as AHRQ, it does provide funding for research to improve health (NIH, n.d.).

The third notable US government agency is the Office of the National Coordinator for Health Information Technology (ONCHIT). This office was funded with money granted by the Public Health Service Act (PHSA) as defined by the Health Information Technology for Economic and Clinical Health Act (HITECH). ONCHIT provides EHR certification, and its structure includes multiple offices that are relevant for HIT as may be seen in Figure 1-1 (HealthIT.gov, 2017).

M01_HEBD1010_06_SE_C01.indd 10 3/16/18 1:53 PM

http://hitcomp.org/
http://healthit.gov/
An Overview of Informatics in Healthcare 11

Technical standards provide specific directions to ensure that data and information can be exchanged in a fashion so that uniform meaning is maintained on both sides of the exchange. Health-information data standards may be grouped into the following four categories: con- tent, transport, vocabulary, and privacy/security standards (Health Information Management Systems Society, 2017b). Content standards establish the structure and organization of the con- tent. Transport standards set forth the format for exchange. Terminology standards improve communication through the use of structured terms and facilitate organization of data. (More will be said on terminology standards later). Privacy standards protect personal health infor- mation, while security standards provide administrative, physical, and technical actions that provide patient confidentiality as well as the availability and integrity of health information.

It is important to dispel the idea that computers are taking nurses away from the bed- side. As nursing practice evolves, technology evolves in tandem. Technology supports all aspects of nursing practice, which include direct care, administration, education, and research (McGonigle, Hunter, Sipes, & Hebda, 2014). In order to create an informatics culture, there must be harmonious interaction between people and technology. While technology changes rapidly, so do the needs of the user. Informaticists play a key role in both system design and nurturing the user’s abilities.

Figure 1-1 • ONC Organization. SOURCE: From Office of the National Coordinator for Health Information Technology (ONC), Published by U.S. Department of Health and Human Services.

O�ce of the chief privacy o�cer

O�ce of the chief operating o�cer

O�ce of the chief scientist

O�ce of standards and technology

O�ce of care transformation

O�ce of procurement and grants

O�ce of ethics and compliance

O�ce of planning oversight and data

O�ce of clinical quality and safety

O�ce of planning, evaluation, and

analysis

O�ce of public a�airs and communications

O�ce of policy

O�ce of budget

O�ce of programs and engagement

O�ce of operational services

O�ce of human capital

O�ce of the National Coordinator

M01_HEBD1010_06_SE_C01.indd 11 3/16/18 1:53 PM

12 Chapter 1

EHRs bring a meaningful medium to enhance continuity of care, care coordination, access to information, and satisfaction for both patient and provider, while decreasing costs. Various studies have reported mixed reviews. A study published by Gomes, Hash, Orso- lini, Watkins, and Mazzoccoli (2016) intended to determine the effects of implementing an EHR and the direct relationship to patient-centered activities, attitudes, and beliefs. A well- known EHR was implemented, with the study taking place six months post implementation. Data from nurses’ self-reports showed that post-implementation, nurses spent more time in patient rooms and more time engaged in purposeful interaction. Nursing documentation time decreased by 4%, which may be related to increased skill in doing documentation via computer. Although time spent in the patients’ rooms had increased, that increase did not always equate to higher quality care if interactions were not patient-focused.

Caring for the Patient Not the Computer There is currently a gap in the research related to integrating technology within the caring nurse-patient relationship. In our current digital world, reliance on technology is high. In healthcare, some may argue that this reliance is even higher. Nurses and other healthcare workers rely on machines to obtain vital signs that were previously assessed manually. The change from manual to automated blood pressures has the ability to change the focus of the healthcare system from person to machine. Using the machines as extensions of nursing care, rather than as replacements for it, can allow for continued relationship building, progress toward optimal health, and reduction in medical errors.

The notable expectation regarding the use of technology is using it as a tool to gather data about a patient’s health status. We must always remember that these devices are tools to be used for this purpose and not to replace assessment skills. There is currently a gap in the literature regarding effective ways to integrate the concept of caring with the use of today’s health information technology.

Let’s take, for example, the concept of alarm fatigue. Alarms on medical equipment are designed to alert the healthcare team of an existing or impending change in the patient’s healthcare status. However, it is estimated that “While defects of devices threatened patient safety in the past, alarms indiscriminately generated by the explosive increase in the num- ber of medical devices now threaten their safety. Reports on safety accidents related to the diversity of medical device alarms have raised awareness of the clinical alarm hazard” (Cho, Hwasoon, Lee, & Insook, 2016, p. 46). This alarm fatigue is compounded by the number of potential false alarms during a nurses’ work shift. It is important that we visit the reason for the alarm fatigue and the importance of using technology as a means to improve patient out- comes. Cho et al. (2016) noted that when The Joint Commission introduced the latest patient safety goals in 2013, hospitals were asked to identify ways to manage alarms. This included a deep dive into the most important alarms and what type of signals could be identified to improve alarm safety. Hospitals began the task to create policies and procedures to address this issue. As the primary caregiver at the bedside, nurses are empowered to identify ways to improve the safety of patient care through the management of alarm fatigue. Nurse infor- maticists are especially equipped to identify ways to highlight important alarms and reduce the number of non-actionable alarms. Nurses need to be equipped with the resources needed to make this happen. Part of this process includes the realization that the alarm-generating technology is paramount in providing data to nurses that allows them to make critical deci- sions about the care of their patients.

The electronic health record provides the nurse with the opportunity to use technol- ogy in a caring way that provides direct one-on-one interactions with the patient, using the

M01_HEBD1010_06_SE_C01.indd 12 3/16/18 1:53 PM

An Overview of Informatics in Healthcare 13

computer as a tool to gather and store data that is important to patient care. Nurse infor- maticists can be on the cutting edge in devising technology that focuses on decreasing the frequency of monitor alarms so that the alarms become more actionable to the nurse. Through a systematic review of research articles on physiologic-monitor alarms and alarm fatigue, Paine et al. (2016) identified that the proportion of actionable alarms ranged from less than 1% to 36% across hospital settings. Some studies showed that the amount of alarm exposure affected nurse response time to the alarm. Longer response times may lead to poorer patient outcomes. The findings of this systematic review are further support that nurses need to be well versed in the reasons they use technology as a support. When nurses do not utilize technology to support the care of the patient, but, instead, use it as a substitute for assessing the patient, part of the nurse-patient relationship is lost.

Nursing education is at a pivotal time to be able to educate current and future nurses on the importance of utilizing information technology as a tool for safe patient care. Nursing faculty and nursing staff need to be able to minimize barriers to both training and implemen- tation of tools within a technology-rich environment. Understanding how to use technology for patient intake and assessment, while still creating a trusting nurse-patient relationship, can provide an environment that enhances both quality and safety in nursing.

An important way to assist in creating this type of environment is to ensure that all patients are aware of the type of technology that is used for data collection and communica- tion. It is important that they have the perception that the nurse cares about them. Instilling this value in the relationship is difficult when nurses are so heavily reliant on technology such as computers, specialized communication devices, and telephones that they carry with them during the course of patient care. Limitations to building a trusting, caring relationship come when patients perceive the nurse does not care, or is distracted during interactions—as can be the case when nurses stop to answer the phone or other communication devices during the course of a nurse-patient interaction. Patients need to be able to see the relevance of technol- ogy to the quality and safety of the care they receive. A collaborative approach to the use of technology between the patient and the nurse may assist in increasing caring relationships and decreasing patient events related to alarm fatigue.

Future Directions Over the last few years, the focus has been on health information exchange for care delivery and quality. Over the next ten years, the infrastructure to support interoperability of systems and data exchange must be completed. The Office of Health and Human Services is responsible for increasing the amount of electronic health information and interoperability of HIT. This coin- cides with the ONC mission to protect the health of all Americans and provide essential human services, especially for those least able to help themselves (Office of the National Coordinator for Health Information Technology, 2015). The ONC roadmap for interoperability is written for both public and private stakeholders who will advance health IT interoperability for the betterment of patient care, smarter spending, and a healthier people. The document is intended to be dynamic as goals are met and new ones created. In order to achieve interoperability and ensure electronic health information security, the ONC proposed the following pathways:

• Improved technical standards and implementation guidance. In short, this means use of commonly known standards and consistency in application of standards.

• A shift in alignment of federal, state, and commercial payment policies away from fee- for-service to a value-based model.

• Coordination among stakeholders to promote and align policies and business practices.

M01_HEBD1010_06_SE_C01.indd 13 3/16/18 1:53 PM

14 Chapter 1

The IT ecosystem is important as new technology enters the market. At the core of the ecosystem are the patient, practice, population, and public. Surrounding the core of stake- holders are the products and services that allow interoperability to happen. Figure 1-2 depicts the health IT ecosystem.

Nursing informatics will continue to evolve as a specialty, particularly as its visibility increases and the need for all healthcare professionals to develop their own informatics competencies becomes increasingly apparent. Nursing informatics will continue its journey by staying current with technology trends, building strong collaborative teams, promot- ing standardization, and being proactive. As clients demand more health information and quicker access to it, information research using the tools of technology is a basic must-have skill for the nurse. Just as nurses face the challenges of patient care through competen- cies, the same approach should be incorporated into practice while facing the future of technology.

New technologies afford the opportunity to create new tools or to use them in new ways. As one example consider the growing use of virtual reality for education. Virtual worlds are found in computerized settings that simulate environments without typical boundaries. Second Life (http://secondlife.com) is one example of a virtual application that allows for creativity although it can be time-intensive, costly, and unable to provide feedback from a sense of smell and touch.

Summary • The healthcare delivery system faces many demands that include calls for increased

quality, safety, and transparency; evolving roles for practitioners; a shift in consumer- provider relationships; eliminating disparities in care; adopting new models of care; the need to develop a learning health system; increased technology; and workforce preparation.

Figure 1-2 • Health IT Ecosystem. SOURCE: From Office of the National Coordinator for Health Information Technology (ONC), Published by U.S. Department of Health and Human Services.

Individuals access and share health

information

Quality measures Public health Clinical research

Technical standards and services

Certification of HIT to accelerate interoperability

Privacy and security protections

Supportive business, clinical, and regulatory environments

Rules of engagement and governance

Patient

Clinical decision support

Public health policy

Clinical guidelines

Practice Population Public

HIT for quality and safety in care

delivery

Population health management and regional

information exchange

Big data and analytics

M01_HEBD1010_06_SE_C01.indd 14 3/16/18 1:53 PM

http://secondlife.com/
An Overview of Informatics in Healthcare 15

• HIT has the potential to facilitate delivery of safe, quality care in an effective, efficient manner while improving communication and decreasing costs.

• Informatics in healthcare provides the knowledge and skills to harness the potential of HIT.

• Healthcare professionals are knowledge workers, and their work is supported via well- used HIT; but educational preparation for knowledge work has been inconsistent.

• Structures are needed to support meaningful use of data. These include digital for- mats using standardized languages and terminologies to ensure consistent meanings across all settings.

• Large data sets, known as big data, increasingly provide evidence to support learning and new practices—often supplementing or replacing traditional research findings.

• Healthcare delivery needs to become a learning health system, which is defined as one that uses real-time evidence for continuous improvement and innovation. This real- time evidence can be supplied through big data and analytics or business intelligence.

• Technology is pervasive throughout healthcare delivery inclusive of point of care devices, wearable, implantable, monitoring, as well as information systems and EHRs. Informatics professionals play a role in the design, implementation, and evalu- ation of that same technology.

• Economic incentives for the adoption of EHRs provides means to measure quality of care and provided learning that can be used for improved allocation of resources.

• Patient safety is a global initiative. HIT can provide or enhance safety through the provision of checklists, improved communication, and prevention of errors, as well as simplification and standardization.

• New care models are reliant upon data to better coordinate patient care. • The move to consumerism, with care as a partnership, drives the need for available

quality data for consumers to facilitate informed decision-making. • An informatics culture recognizes the value of data, establishes a knowledge strategy,

and the infrastructure, expertise, and tools required to discover new learning and knowledge in data.

• An infrastructure conducive to an informatics culture fosters legislative, policy, and advocacy efforts to increase access to information and quality care. Professional groups and government agencies, including the US Office of the National Coordina- tor for Health Information Technology, have demonstrated efforts to foster an infor- matics culture.

• Informatics and healthcare professionals have ethical codes to guide the use of data and information.

• All healthcare professionals need informatics knowledge and skills to ensure appro- priate use of technology and data, information, and knowledge. Informatics pro- fessionals need to provide leadership and informatics to ensure that all healthcare professionals receive these competencies.

• Technology provides a tool to augment, not replace, the care. Informaticists must consider the needs of healthcare professionals and consumers when technology is deployed.

• HIT is not deployed in isolation; instead, it is part of the health IT ecosystem that brings together patients, provider practices, populations, and the public in a system designed to support each through research, policy, guidelines, and decision support, while measuring quality and outcomes.

• The development of new technologies and informatics competencies is a given over time.

M01_HEBD1010_06_SE_C01.indd 15 3/16/18 1:53 PM

16 Chapter 1

Case Study

The community member on your hospital’s advisory body has asked you to provide an overview of the relationship between informatics, technology in healthcare, and the status of healthcare delivery today. In your efforts to provide a short answer what would be four points that you would make?

About the Authors Jennifer A. Brown has been a nurse educator for over eighteen years and has spent the last five years teaching Health Informatics to students in nursing, health information management, interdisciplinary health sciences, and computer science. Board Certified in Holistic Nursing, her passion for holism is threaded throughout each course that Professor Brown teaches. She is a tenured full-time faculty and teaches in the Bronson School of Nursing at Western Michigan University in Kalamazoo, Michigan in the undergraduate and RN-BSN programs.

Taryn Hill serves as Dean of Academic Affairs for Chamberlain College of Nursing. She contributed the content Caring for the Patient Not the Computer. She has authored and pre- sented on nursing informatics topics.

Toni Hebda teaches graduate-level informatics courses at Chamberlain College of Nursing.

References Agency for Healthcare Research and Quality. (2017). About the national quality strategy.

Retrieved from www.ahrq.gov/workingforquality/about/index.html Agency for Healthcare Research and Quality. (n.d). What we do. Retrieved from

www.ahrq.gov/ American Medical Informatics Association (AMIA ). (2017). The science of informatics.

Retrieved from www.amia.org/about-amia/science-informatics American Nurses Association. (2014). Standardization and interoperability of health

information technology: Supporting nursing and the national quality strategy for better patient outcomes. Retrieved from http://nursingworld.org/MainMenuCategories/Policy- Advocacy/Positions-and-Resolutions/ANAPositionStatements/Position-Statements- Alphabetically/Standardization-and-Interoperability-of-Health-Info-Technology.html

American Nurses Association. (2015a). Nursing informatics: Scope and standards of practice (2nd ed.). Silver Spring, MD: Author.

American Nurses Association. (2015b). Code of ethics for nurses with interpretive statements. Silver Spring, MD: Author.

Beauchamp, T., & Childress, J.F. (1994). Principles of Biomedical Ethics. Oxford, United Kingdom: Oxford University Press.

Bulletin Board. (2016). Physician EHR adoption growing, but not physician information exchange. Journal of AHIMA, 87(4), 9.

Centers for Medicare and Medicaid Services. (2010). Medicare & Medicaid EHR incentive program: Meaningful use stage 1 requirements overview. Retrieved from www.cms .gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms/downloads/ MU_Stage1_ReqOverview.pdf

Cho, O. M., Hwasoon, K., Lee, Y. W., & Insook, C. (2016). Clinical alarms in intensive care units: Perceived obstacles of alarm management and alarm fatigue in nurses. Health Informatics Research, 22(1), 46–53. Published by The Korean Society of Medical Informatics © 2016.

M01_HEBD1010_06_SE_C01.indd 16 3/16/18 1:53 PM

http://www.ahrq.gov/
http://www.amia.org/about%E2%80%90amia/science%E2%80%90informatics
http://nursingworld.org/MainMenuCategories/Policy%E2%80%90Advocacy/Positions%E2%80%90and%E2%80%90Resolutions/ANAPositionStatements/Position%E2%80%90Statements%E2%80%90Alphabetically/Standardization%E2%80%90and%E2%80%90Interoperability%E2%80%90of%E2%80%90Health%E2%80%90Info%E2%80%90Technology.html
http://nursingworld.org/MainMenuCategories/Policy%E2%80%90Advocacy/Positions%E2%80%90and%E2%80%90Resolutions/ANAPositionStatements/Position%E2%80%90Statements%E2%80%90Alphabetically/Standardization%E2%80%90and%E2%80%90Interoperability%E2%80%90of%E2%80%90Health%E2%80%90Info%E2%80%90Technology.html
http://nursingworld.org/MainMenuCategories/Policy%E2%80%90Advocacy/Positions%E2%80%90and%E2%80%90Resolutions/ANAPositionStatements/Position%E2%80%90Statements%E2%80%90Alphabetically/Standardization%E2%80%90and%E2%80%90Interoperability%E2%80%90of%E2%80%90Health%E2%80%90Info%E2%80%90Technology.html
http://www.cms.gov/Regulations%E2%80%90and%E2%80%90Guidance/Legislation/EHRIncentivePrograms/downloads/MU_Stage1_ReqOverview.pdf
http://www.cms.gov/Regulations%E2%80%90and%E2%80%90Guidance/Legislation/EHRIncentivePrograms/downloads/MU_Stage1_ReqOverview.pdf
http://www.cms.gov/Regulations%E2%80%90and%E2%80%90Guidance/Legislation/EHRIncentivePrograms/downloads/MU_Stage1_ReqOverview.pdf
http://www.ahrq.gov/workingforquality/about/index.html
An Overview of Informatics in Healthcare 17

Chuo, Y., Liu, C., & Tsai, C. (2015). Effectiveness of elearning in hospitals. Technology & Health Care, 23, S157–S160.

Collins, S., Sensmeier, J., Weaver, C., & Murphy, J. (2016). Speaking with one voice: Alliance for Nursing Informtics policy responses. 2016 update. CIN: Computers, Informatics, Nursing, 34(11), 490–492.

Dewey, J. P. (2016). Accountable care organizations (ACOs). Salem Press Encyclopedia of Health https://salempress.com/

Dulin, M. F., Lovin, C. A., & Wright, J. A. (2016). Bringing big data to the forefront of healthcare delivery: The experience of Carolina’s healthcare system. Frontiers of Health Services Management, 32(4), 3–14.

EHR adoption continues to lag for long-term care providers. (2017). Journal of AHIMA, 88(3), 10. Fox, B. I., Flynn, A., Clauson, K. A., Seaton, T. L., & Breeden, E. (2017). An approach for all in

pharmacy informatics education. American Journal of Pharmaceutical Education, 81(2), 1–13. Gaberson, K., & Langston, N. F. (2017). Nursing as knowledge work: The imperative for

lifelong learning. AORN Journal, 106(2), 96–98. Gomes, M., Hash, P., Orsoline, L., Watkins, A., & Mazzoccoli, A. (2016). Connecting

professional practice and technology at the bedside: Nurses’ beliefs about using an electronic health record and their ability to incorporate professional and patient-centered nursing activities in patient care. CIN: Computers, Informatics, Nursing, 34(12), 578–586.

Gundlapalli, A. V., Gundlapalli, A. V., Greaves, W. W., Kesler, D., Murray, P., Safran, C., & Lehmann, C. U. (2015). Clinical informatics board specialty certification for physicians: A global view. Studies in Health Technology and Informatics, 216, 501–505.

Healthcare Information and Management Systems Society (HIMSS). (2017a). What is TIGER? Retrieved from www.himss.org/professionaldevelopment/tiger-initiative.

Health Information Management Systems Society (HIMSS). (2017b). About HIMSS. Retrieved from www.himss.org/about-himss

HealthIT.gov. (2016). About ONC. Retrieved from www.healthit.gov/newsroom/about-onc HealthIT.gov. (2017). ONC HealthIT certification program. Retrieved from www

.healthit.gov/policy-researchers-implementers/about-onc-health-it-certification-program Hefford, B. (2017). The patient medical home: Working together to create an integrated

system of care. British Columbia Medical Journal, 59(1), 15–17. Hersh, W., Gorman, P., Biagioli, F., Mohan, V., Gold, J., & Mejicano, G. (2014). Beyond

information retrieval and electronic health record use: Competencies in clinical informatics for medical education. Advances in Medical Education and Practice, 2014 (5), 205–212. doi:10.2147/AMEP.S63903.

How informatics can reshape healthcare. (2015). Health Leaders Magazine, 19(4), 40–44. Institute of Medicine. (IOM). (2012). Health IT and Patient Safety: Building Safer Systems for

Better Care. Washington, DC: The National Academies Press. Institute of Medicine. (IOM). (2013a). Core measurement needs for better care, better health, and

lower costs: Counting what counts: Workshop summary by Claudia Grossman, Brian Powers, Julia Sanders. Washington, DC: The National Academies Press © 2013.

Institute of Medicine. (IOM). (2013b). Digital data improvement priorities for continuous learning in health and health care: Workshop summary. Washington, DC: The National Academies Press © 2013.

Institute of Medicine. (IOM). (2015). Genomics-enabled learning health care systems: Gathering and using genomic information to improve patient care and research: Workshop summary. Washington, DC: The National Academies Press.

International Council of Nurses. (2012). Closing the gap: From evidence to action. Geneva: International Council of Nurses.

M01_HEBD1010_06_SE_C01.indd 17 3/16/18 1:53 PM

http://www.himss.org/professionaldevelopment/tiger%E2%80%90initiative
http://www.himss.org/about%E2%80%90himss
http://www.healthit.gov/newsroom/about%E2%80%90onc
http://healthit.gov/
http://www.healthit.gov/policy%E2%80%90researchers%E2%80%90implementers/about%E2%80%90onc%E2%80%90health%E2%80%90it%E2%80%90certification%E2%80%90program
http://www.healthit.gov/policy%E2%80%90researchers%E2%80%90implementers/about%E2%80%90onc%E2%80%90health%E2%80%90it%E2%80%90certification%E2%80%90program
http://healthit.gov/
18 Chapter 1

Johnson, J. D. (2015). Physician’s emerging roles relating to trends in health information technology. Informatics for Health & Social Care, 40(4), 362–375. doi:10.3109/17538157.201 4.948172

Kabir, N., & Carayannis, E. (2013). Big data, tacit knowledge and organizational competitiveness. Proceedings of the International Conference on Intellectual Capital, Knowledge Management & Organizational Learning, 220–227.

Kaggal, V. C., Komandur Elayavilli, R., Mehrabi, S., Pankratz, J. J., Sunghwan, S., Yanshan, W., . . . Hongfang, L. (2016). Toward a learning health-care system—knowledge delivery at the point of care empowered by big data and NLP. Biomedical Informatics Insights, (8), 13–22. doi:10.4137/Bii.s37977

Kohli, R., & Tan, S. S. (2016). Electronic health records: how can researchers contribute to transforming healthcare? MIS Quarterly, 40(3), 553–574.

Lucero, R. J. (2017). Information technology for health promotion & care delivery. Improving health promotion and delivery systems through information technology. Nursing Economics, 35(3), 145–146.

Luo, J., Min, W., Gopukumar, D., & Yiqing, Z. (2016). Big data application in biomedical research and health care: A literature review. Biomedical Informatics Insights, (8), 1–10. doi:10.4137/BII.s31559

Mackey, A., & Bassendowski, S. (2017). The history of evidence-based practice in nursing education and practice. Journal of Professional Nursing, 33, 51–55. doi:10.1016/ j.profnurs.2016.05.009

McCullough, J. S., Parente, S. T., & Town, R. (2016). Health information technology and patient outcomes: The role of information and labor coordination. RAND Journal of Economics (Wiley-Blackwell), 47(1), 207–236.

McGonigle, D., Hunter, K., Sipes, C., & Hebda, T. Everyday informatics: Why nurses need to understand nursing informatics. AORN Journal, 100(3), 324–327. http://dx.doi.org/ 10.1016/j.aorn.2014.06.012

National Academies of Sciences, Engineering, and Medicine. (2017). Real-world evidence generation and evaluation of therapeutics: Proceedings of a workshop. Washington, DC: The National Academies Press. doi: https://doi.org/10.17226/24685.

National Association of Clinical Nurse Specialists. (2017). 2016–2018 Public Policy Agenda. Retrieved from http://nacns.org/advocacy-policy/public-policy-agenda/

National Institutes of Health (NIH). (n.d.). What we do. Retrieved from www.nih.gov/ about-nih/what-we-do

Nelson, R., & Staggers, N. (2018). Theoretical foundations of health informatics In Ramona Nelson & Nancy Staggers (Eds), Health informatics: An interprofessional approach (pp. 10–37). St. Louis, MO: Elsevier.

Office of the National Coordinator for Health Information Technology. (2015). Connecting health and care for the nation: A shared nationwide interoperability roadmap—version 1.0. Retrieved from www.healthit.gov/sites/default/files/hie-interoperability/nationwide- interoperability-roadmap-final-version-1.0.pdf

Office of the National Coordinator for Health Information Technology. (2017a). Health IT dashboard: Quick stats. Retrieved from https://dashboard.healthit.gov/quickstats/ quickstats.php

Office of the National Coordinator for Health Information Technology. (2017b). Health IT education opportunities. Retrieved from www.healthit.gov/providers-professionals/ health-it-education-opportunities

M01_HEBD1010_06_SE_C01.indd 18 3/16/18 1:53 PM

http://www.nih.gov/about%E2%80%90nih/what%E2%80%90we%E2%80%90do
http://www.nih.gov/about%E2%80%90nih/what%E2%80%90we%E2%80%90do
http://www.healthit.gov/sites/default/files/hie%E2%80%90interoperability/nationwide%E2%80%90interoperability%E2%80%90roadmap%E2%80%90final%E2%80%90version%E2%80%901.0.pdf
http://www.healthit.gov/sites/default/files/hie%E2%80%90interoperability/nationwide%E2%80%90interoperability%E2%80%90roadmap%E2%80%90final%E2%80%90version%E2%80%901.0.pdf
http://https//doi.org/10.17226/24685
http://https//dashboard.healthit.gov/quickstats/quickstats.php
http://https//dashboard.healthit.gov/quickstats/quickstats.php
http://www.healthit.gov/providers%E2%80%90professionals/health%E2%80%90it%E2%80%90education%E2%80%90opportunities
http://www.healthit.gov/providers%E2%80%90professionals/health%E2%80%90it%E2%80%90education%E2%80%90opportunities
An Overview of Informatics in Healthcare 19

Orion Market Research. (2017). Global healthcare information systems market research and analysis 2015–2022. Retrieved from www.omrglobal.com/industry-reports/ healthcare-information-systems-market/

Paine, C. W., Goel, V. V., Ely, E., Stave, C. D., Stemler, S., Zander, M., & Bonafide, C. P. (2016). Systematic review of physiologic monitor alarm characteristics and pragmatic interventions to reduce alarm frequency. Journal of Hospital Medicine, 11(2), 136–144.

Pinsonneault, A., Addas, S., Qian, C., Dakshinamoorthy, V., & Tamblyn, R. (2017). Integrated health information technology and the quality of patient care: A natural experiment. Journal of Management Information Systems, 34(2), 457–486.

QSEN Institute. (2017b). QSEN. Retrieved from http://qsen.org/about-qsen/ Quality and Safety Education for Nurses (QSEN). (2017a). Competencies. Retrieved from

http://qsen.org/competencies/ Quality and Safety Education for Nurses (QSEN). (2017c). Graduate QSEN competencies.

Retrieved from http://qsen.org/competencies/graduate-ksas/#informatics Rajamani, S., Westra, B. L., A. Monsen, K., LaVenture, M., & Gatewood, L. C. (2015).

Partnership to promote interprofessional education and practice for population and public health informatics: A case study. Journal of Interprofessional Care, 29(6), 555–561.

Robert Wood Johnson Foundation. (2017). What is the national quality strategy? Retrieved from www.rwjf.org/en/library/research/2012/01/what-is-the-national-quality- strategy-.html

Shortliffe, E. H. (2011). President’s column: Subspecialty certification in clinical informatics. Journal of the American Medical Informatics Association, 18(6), 890–891. doi:10.1136/ amiajnl-2011-000582.

The Joint Commission. (2017). 2017 National patient safety goals. Retrieved from www.jointcommission.org/assets/1/6/2017_NPSG_HAP_ER.pdf

Titzer, J. L., Swenty, C. F., & Mustata Wilson, G. (2015). Interprofessional education: Lessons learned from conducting an electronic health record assignment. Journal Of Interprofessional Care, 29(6), 536–540. doi:10.3109/13561820.2015.1021000

Wills, M. J. (2014). Decisions through data: Analytics in healthcare. Journal of Healthcare Management, 59(4), 254–262.

World Health Organization. (WHO). (2017). Health topics: Patient safety. Retrieved from www.who.int/topics/patient_safety/en/

Zikhani, R. (2016). Seven-step pathway for preventing errors in healthcare. Journal of Healthcare Management, 61(4), 271–281.

M01_HEBD1010_06_SE_C01.indd 19 3/16/18 1:53 PM

http://qsen.org/competencies/graduate%E2%80%90ksas/#informatics
http://www.who.int/topics/patient_safety/en/
http://qsen.org/competencies/
http://www.jointcommission.org/assets/1/6/2017_NPSG_HAP_ER.pdf
http://www.rwjf.org/en/library/research/2012/01/what%E2%80%90is%E2%80%90the%E2%80%90national%E2%80%90quality%E2%80%90strategy%E2%80%90.html
http://www.rwjf.org/en/library/research/2012/01/what%E2%80%90is%E2%80%90the%E2%80%90national%E2%80%90quality%E2%80%90strategy%E2%80%90.html
http://www.omrglobal.com/industry%E2%80%90reports/healthcare%E2%80%90information%E2%80%90systems%E2%80%90market/
http://www.omrglobal.com/industry%E2%80%90reports/healthcare%E2%80%90information%E2%80%90systems%E2%80%90market/
http://qsen.org/about%E2%80%90qsen/
20

H er

o Im

ag es

/G et

ty Im

ag es

Chapter 2

Informatics Theory and Practice Maxim Topaz, PhD, MA, RN

Learning Objectives

After completing this chapter, you should be able to:

• Discuss the relevance of theory for informatics research and practice.

• Apply the DIKW framework to a situation in your lived experience.

• Examine ways that informatics may use the wisdom-in-action framework to support clinical care.

• Compare and contrast the different informatics subdisciplines found within healthcare.

• Weigh how the scope of informatics practice determines the types and levels of competencies needed.

• Discuss future needs and directions for nursing informatics.

Overview of Theory Theory Definition In general, theory is defined as a scientifically acceptable general principle—or constellation of principles—offered to explain phenomena (Meleis, 2015). Scientific disciplines are often based on some central theories that define the general school of thought accepted within a discipline. For example, a theory of evolution formalized by Darwin (1859) states that through a process called natural selection, live organisms are changing over time while passing their new traits to the next generations. This process results in evolution of simple creatures to complex organisms. Eventually, the changes accumulate and produce an entirely different organism. This theory is fundamental to several fields, for example, biology, where a com- mon assumption states that all life on Earth has evolved from a common ancestor. Similarly, health sciences are based on several core theories, and each health discipline provides its own unique lens into ways of achieving optimal health and well-being.

As stated in the theory definition, all theories explain specific phenomena, large or small. The word phenomenon is often defined as an aspect of reality that can be consciously sensed or experienced otherwise (Meleis, 2015). Within a particular discipline, phenomena reflect

M02_HEBD1010_06_SE_C02.indd 20 3/15/18 3:00 PM

Informatics Theory and Practice 21

the domain or the boundaries of the discipline. Phenomena are often used to describe an idea about an event, a situation, a process, or a group of events. A phenomenon may be geographi- cally or time bound. Phenomena can be things that can be seen, heard, smelled, or felt (e.g., patient’s pulse or blood-pressure measures). A phenomenon can also take a more abstract form and be based on evidence that is grouped together through presumed connections (e.g., the observation that individuals with surgical incisions that live alone and have multiple medications are more likely to be readmitted to a hospital after cardiac-surgery hospitaliza- tion). In the example of Darwin’s theory, the studied phenomenon is that of natural selection and the theory describes the phenomenon’s characteristics in detail.

Theories have two major purposes: to guide research and practice (Meleis, 2015). In research, theory is used to formulate a minimum set of generalizable statements to explain a maximum number of observable relationships among the research variables. Theory informs research and vice versa; research results can be used to verify, alter, or defy theories. In prac- tice, theories help healthcare professionals in general, and nurses in particular, to construct a framework needed to set the goals of assessment, diagnosis, and intervention. For example, a theory can be used to set a general goal of nursing care to promote a patient’s self-care through patient-focused decision-support tools and reminders for symptom management and a healthy diet, sent to a patient’s smartphone.

Nursing Theory One of the most comprehensive definitions of nursing theories is suggested by Afaf I. Meleis: “Nursing theory is conceptualization of some aspect of nursing reality communicated for the purpose of describing phenomena, explaining relationships between phenomena, predicting consequences, or prescribing nursing care” (Meleis, 2015, p. 29). In nursing, theories are often labeled as conceptual frameworks, models, paradigms, etc., but in essence, they all share the same properties and aim to achieve the same results.

Some scholars identify three levels of abstraction into which nursing theories can be categorized: grand theories, middle-range theories, and situation-specific theories. Grand theories aim to describe the broadest scope of nursing phenomena and relationships between them and do not lend themselves to empirical testing. Grand theories mostly emerged in 1950–1960s and helped differentiate between nursing practice and the practice of medicine.

For example, Orem’s theory of self-care, first published around 1950, emphasized the person’s need to care for oneself (Orem, 1985). The self-care theory identified three types of nursing systems: wholly compensatory, in which the nurse cares for all the patient needs; partly compensatory, in which the nurse assists the patient to care for himself or herself; and supportive-educative, when the nurse assists the patient to learn how to care for himself or herself. According to Orem, nursing is needed when a person is limited or incapable in the provision of effective and continuous self-care. The theory identifies several types of needed actions: guiding, supporting, or teaching others; acting for and doing for others; and creat- ing an environment promoting personal development in an effort to meet future demands.

Middle-range theories are more limited in scope, focus on a specific phenomenon, and reflect practice (teaching, clinical, or administrative). These theories cross different nursing fields and reflect a wide variety of nursing-care situations. Middle-range theories are a good fit for empirical testing, because they are more specific and can be readily operationalized.

For example, Riegel, Jaarsma, and Strömberg (2012) have recently developed a middle- range theory of self-care among individuals with chronic illness. Based on the observation that not everyone is capable of performing self-care, Riegel and colleagues identified key concepts playing a role in individual decision making. For example, the theory makes the assumption

M02_HEBD1010_06_SE_C02.indd 21 3/15/18 3:00 PM

22 Chapter 2

that in order to make the right decisions, patients with chronic illness need focused attention and sufficient working-memory capacity. On the other hand, people with limited memory and attention (e.g., individuals with dementia) have little ability to interpret their symptoms and thus, may not be able to perform self-care. Situational influences on attention and memory (e.g., emotional stress or sleep deprivation) also affect decision making and interfere with effective self-care. According to Riegel, shared care, dependent care, or community support might be needed to help individuals experiencing these situations (Riegel et al., 2012). Middle- range theories were also applied to describe concepts like incontinence, uncertainty, social support, and quality of life, among others.

Situation-specific theories focus on a specific nursing phenomenon. They are often bound to a specific type of clinical practice and focus on a specific population. These theories are not meant to transcend time or go beyond a particular social structure, but rather they fit well within a certain social context (Meleis, 2015).

The previously described middle-range theory of self care among patients with chronic illness (Riegel et al, 2012) has been applied to patients with heart failure. This work has led to a situation-specific theory of heart failure self-care (Riegel, Dickson, & Faulkner, 2016; Riegel, Dickson, & Topaz, 2013; Riegel & Dickson, 2008.) This resulted in a situation-specific theory of heart failure self-care. Riegel and Dickson (2008) described self-care as a naturalistic decision-making process involving the choice of behaviors to maintain physiologic stability and the response to symptoms when they occur. In the theory, four propositions are used to specify the key assumptions: (a) symptom recognition is the key to successful self-care management; (b) self-care is better in patients with more knowledge, skill, experience, and compatible values; (c) confidence moderates the relationship between self-care and outcomes; and (d) confidence mediates the relationship between self-care and outcomes. Other examples of situation–specific theories are menopausal experiences of Korean immigrants, lived experi- ences of Asian American women caring for their elderly relatives, and preventive models for HIV among adolescents (Meleis, 2015).

Critical Theories Supporting Informatics Health informatics is formed by a merger of several disciplines, including information sci- ence, computer science, and a specific health discipline; for example, nursing or medicine. Thus, the study of health informatics is informed by several theories from the related fields. In this review, I will describe one central theory, called the data, information, knowledge, and wisdom theory (DIKW), and provide a general approach that can be applied to connect the different disciplines and create a shared theoretical framework to guide nursing-informatics practice and research (Ronquillo, Currie, & Rodney, 2016; Topaz, 2013). Following this exten- sive review, a summary of other supporting theories is provided.

The Data, Information, Knowledge, and Wisdom Theory HISTORICAL DEVELOPMENT The origins of the DIKW theory can be tracked to the early 17th century, when ideas about taxonomies emerged (Ronquillo, Currie, & Rodney, 2016). However, it wasn’t until the late 1980s that the framework was adapted to health informat- ics by Blum (1986). In this classic work, he identified three types of systems used in health informatics, including:

1. Data-oriented systems. For example, systems designed for patient monitoring, clinical laboratory data, diagnostic systems, and imaging (e.g., computed tomo- graphic scan);

M02_HEBD1010_06_SE_C02.indd 22 3/15/18 3:00 PM

Informatics Theory and Practice 23

2. Information-oriented systems. For example, clinical information systems that can provide administrative support (e.g., reduce errors) and healthcare decision support (e.g., alerts and reminders to support clinical decision making);

3. Knowledge-oriented systems. Examples include large knowledge databases (e.g., medical-articles collections) and artificial-intelligence systems (i.e., smart systems capable of applying advanced clinical reasoning).

Blum’s work was widely adopted and created a foundation for theorizing in health infor- matics. The concept of wisdom is sometimes attributed to Ackoff’s 1989 address to the Society for General Systems (Ackoff, 1989). Ackoff suggested that data is leads to information, infor- mation to knowledge and finally, knowledge leads to wisdom that guides the application of knowledge in clinical practice.

THE DATA, INFORMATION, KNOWLEDGE, AND WISDOM THEORY IN NURSING The theory was first adapted to nursing in Graves and Corcoran’s (1989) seminal paper, “The Study of Nursing Informatics” that established nursing informatics as a field of scholarly inquiry. This work was well accepted and implemented by the international nursing community. For example, the American Nurses Association (ANA) has adopted DIKW to guide the develop- ment of the scope and standards of practice in nursing informatics, suggesting that “Nursing informatics is a specialty that . . . communicates data, information, knowledge and wisdom in nursing practice” (American Nurses Association, 2008, p. 2). Nelson (2002) and most recently, Matney et al. (2011), studied and further adapted the theory to guide nursing informatics.

THE DATA, INFORMATION, KNOWLEDGE, AND WISDOM THEORY: CENTRAL CONCEPTS

• Data are the most discrete components of the DIKW framework. They are mostly presented as discrete observations with little interpretation. These are the smallest factors describing the patient, disease state, health environment, and so forth. Examples include a patient’s principal medical diagnosis (e.g., International Statistical Classification of Diseases (ICD-10) diagnosis # N18.1: Chronic kidney disease, stage 1) (World Health Organization, 2014) or marital status (e.g., married, divorced, single, etc.). A discrete data-point obser- vation (datum) is not meaningful when presented in isolation from other observations.

• Information might be described as data plus meaning. A meaningful clinical picture is constructed when different data points are put together and presented in a specific context. Information is a continuum of progressively developing and clustered data; it answers questions such as who, what, where, and when. For example, a combination of a patient’s ICD-10 diagnosis # N18.1: Chronic kidney disease, stage 1 and marital status of ‘Divorced’ has a certain meaning in a context of an older, homebound individual.

• Knowledge is information that has been processed and organized so that relations and inter- actions are identified. Knowledge is constructed of meaningful information built of discrete data points. Knowledge is derived by discovering patterns of relationships between differ- ent clusters of information and affected by assumptions and central theories of a scientific discipline with which it is concerned. Knowledge answers questions of why and/or how.

For nurses, the combination of different information clusters, such as the ICD-10 diagno- sis #N18.1: Chronic kidney disease, stage 1, coupled with the fact the patient is divorced, and additional information that an older man (78-years old) was just discharged from hospital to home with a complicated surgical-incision treatment, prescription could indi- cate that this person is at a high risk for hospital readmission.

• Wisdom is an appropriate use of knowledge to manage and solve human problems (Matney et al., 2011). Wisdom includes ethics or knowing why certain things or procedures

M02_HEBD1010_06_SE_C02.indd 23 3/15/18 3:00 PM

24 Chapter 2

should or should not be implemented in specific cases. Wisdom guides the nurse in rec- ognizing the situation at hand, based on the nurse’s expertise, patient’s and patient’s family’s values, and patient’s healthcare knowledge. Using wisdom and a combination of all these components, the nurse decides on a nursing intervention or action. Benner (2000) presents wisdom as a clinical judgment integrating senses, emotions, and intu- ition. Using the previous examples, wisdom will be displayed when the homecare nurse considers prioritizing the elderly kidney-disease patient with complex surgical-incision care for an immediate intervention, such as a first nursing visit within the first hours of discharge from hospital to assure appropriate wound care and prevent complications.

The elements of the DIKW framework have certain hierarchical structure: data constructs information; information grows into knowledge informed by a particular setting or a prob- lem; and knowledge progresses to wisdom to be applied in practice. However, the hierarchy is not strictly linear but rather, circular, and DIKW elements are interrelated. In a still relevant work, Nelson defined this phenomenon as a “constant flux” between the framework parts (Nelson, 2002, p. 27). See Figure 2-1 for a depiction of this flux. Simply put, new knowledge derived from specific data coupled with wisdom might warrant assessment of new data ele- ments (Matney et al., 2011). In clinical practice, for example, a nurse administering inpatient medications can discover that a patient refuses to take the prescribed lipid medication as scheduled (data and knowledge and medication nonadherence). This, in turn, will trigger the nurse to explore the reasons for patient nonadherence (new data), and then, a nurse might discover that the patient uses a different medication for lipid management at home. Using clinical wisdom, the nurse will discuss the situation with the attending physician who can reconcile the discrepancies in the patient’s medication list. In this scenario, information about

Figure 2-1 • Nelson’s depiction of the data-information-knowledge-wisdom (DIKW) continuum.

SOURCE: Based on 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.

Constant flux

Information Organizing and interpreting

Knowledge Interpreting, integrating, and understanding

Wisdom Understanding, applying, and applying with compassion

Data Naming, collecting, and organizing

Increasing interactions and interrelationships

In cr

ea si

ng c

om pl

ex ity

M02_HEBD1010_06_SE_C02.indd 24 3/15/18 3:00 PM

Informatics Theory and Practice 25

Figure 2-2 • The theory of wisdom in action for clinical nursing©. SOURCE: The Theory of Wisdom in Action for Clinical Nursing from Development of a Theory of Wisdom in Action for Clinical Nursing. Copyright © 2015 by Susan A. Matney.

Influences

Personal Factors (Nurse/Patient/Family/Provider)

Knowledge Factors

Age Education Social interaction Culture/Religion Values, Relativism, and Tolerance Cognition Life Experiences Openness to Learning Assertiveness Confidence

Fundamental Knowledge Procedural Knowledge Lifespan Contextualism Psychosocial Knowledge

Mentors/Role Models Clinical Experiences Clinical Training

Clinical Factors

Setting Type Setting Culture Nurse Familiarity with Setting Collaborative Team Electronic System Information Decision Support System

Setting Related Factors

Evaluation Information Processing

Wisdom Antecedents

Ex pe

rti se

Expertise

Expertise

Ex pe

rti se

Critical ThinkingDecision

Person Related Factors

General Wisdom in Action

Personal Wisdom in Action (post situation)

Increases

Integration Into

Reflection

Stressful or Uncertain Situation

Intervention KnowledgeIndetification

Discovery of Meaning

Leads to

Information Gathering

Clinical Judgment

Formulation (in context)

Insight and Intuition

Learning

Co lla

bo ra

tiv e

Em ot

io na

l In te

llig an

ce

Ad vo

ca te

a patient’s nonadherence triggered further data assessment, which, in turn, resulted in new information that was used to resolve the problem using wisdom.

EXPLORING WISDOM While data, information, and knowledge are often described as fairly straightforward constructs, the concept of wisdom may be quite confusing. Nelson and Joos are cited as the first to expand on the concept of wisdom in nursing; they described wisdom as “knowing when and how to use knowledge to manage a patient need or problem.” (Nelson & Joos, 1989, p. 6). Later on, the ANA defined wisdom as nurses’ ability to evaluate the knowledge and information within the context of caring and then use judgment to make care decisions (ANA, 1994).

Most recently, Matney (2015) developed a middle-range theory called the theory of wisdom in action for clinical nursing, using derivation and synthesis based on models from other disciplines and nursing literature. The theory comprised of four interrelated dimensions that are described here and depicted in Figure 2-2.

1. Person-related factors affecting wisdom include personal and clinical factors. Personal factors would include simple concepts, such as nurse and/or patient age, education, marital status, and more complex concepts (e.g., values, beliefs, or life experiences). Clinical factors include clinical training and experience and mentors and role models.

2. Environment-related factors affecting wisdom include setting-related and information- system factors. In term of settings, setting type and culture are important because they define, to some extent, how nurses act. Information-systems factors include the use of computerized data, which leads to clinical information in a certain context.

M02_HEBD1010_06_SE_C02.indd 25 3/15/18 3:00 PM

26 Chapter 2

3. Knowledge is constructed of three different knowledge types, increasing in complexity: rich factual knowledge, rich procedural knowledge, and lifespan contextualism. Factual knowledge refers to the knowledge of nursing process and patient care. This kind of knowledge is often presented in nursing textbooks and then refined and further bol- stered by continuous professional development. Procedural knowledge is comprised of clinical procedures, processes, and interventions required for care. Procedural knowl- edge is often acquired in a specific type of setting based on the accepted norms and rules of behavior. Lifespan refers to the understanding of others as well as understand- ing oneself. It can change over a person’s lifespan, based on the acquired experiences.

4. Wisdom in action requires “knowledge mastery when dealing with uncertain or stressful situations. Knowledge impacts and insight and intuition influence the clinical judgment in context of the situation. The judgment leads to a care decision. After a care decision is applied, reflection, and discovery of meaning occur, which results in learning. Gained knowledge is integrated back into the knowledge dimension” ( Matney, 2015, p. 135).

According to the wisdom-in-action theory, nurses make decisions in stressful or uncertain situations in an iterative process that includes applying knowledge based on skilled clinical judgment. Implemented decisions produce consequences, which, in turn, initiate reflection, discovery of meaning, and learning. Finally, new information is integrated back into changing and refining knowledge and judgment, when necessary. This theory provides a framework for translating wisdom into clinical nursing practice and learning about wisdom development.

Applying DIWK to Guide Nursing Research As an example, the DIKW framework provides a generic structure describing how data is used to produce wisdom. However, to apply the DIKW in practice to guide nursing or other research, one needs to identify the theory describing the data and information in a specific research domain. Thus, the DIKW, ideally, needs to be used in combination with a specific theory. For example, in his dissertation, Topaz (2014) combined a mid-range nursing- transitions theory (Meleis, 2010) with DIKW to guide his research.

Transitions theory emerged in the late 1970s, and since then, it was constantly developed and refined by Meleis and others (Meleis, Sawyer, Im, Hilfinger Messias, & Schumacher, 2000; Meleis, 2010). In general, a transition can be defined as a passage from one state to another, a process triggered by change. Transitions theory has been applied to various types of transi- tions, for example, immigration transition, health-illness transition, and administrative tran- sition, among others (Meleis, 2010). The transitions theory includes six central components: types and patterns of transitions; properties of transition experiences; transition conditions: facilitators and inhibitors; process indicators; outcome indicators; and nursing therapeutics (see Figure 2-1).

Topaz’s study focused on a particular type of transition: transition from hospital to home- care. In his preliminary work, the author found that nurses’ decisions on whom to prioritize for the first homecare visit vary among different homecare agencies, which results in delays of care and high risk for hospital readmissions. The goal of the study was to create a clinical- decision support tool that will help homecare nurses to identify a patient’s priority for a first homecare nursing visit. The study used the transitions theory to conceptualize the different data points for further inclusion in the study.

The transitions theory suggests that the nature of the transition is affected by several elements; for example, transition type. One type of transition is health-illness transition. For the majority of patients, admission to a hospital is a major health-illness transition. This type of transition includes sudden or gradual role change resulting from moving from wellness to

M02_HEBD1010_06_SE_C02.indd 26 3/15/18 3:00 PM

Informatics Theory and Practice 27

Figure 2-3 • Transitions-theory domains and factors as applied in Topaz’s study. SOURCE: From Developing a Tool to Support Decisions on Patient Prioritization at Admission to Home Health Care by Maxim Topaz. Copyright © 2014 by University of Pennsylvania. Used by permission.

Nature of transitions

Health/illness (e.g., heart failure, diabetes mellitus etc.) Situational (e.g., active involvement of informal caregiver, etc.) Developmental (e.g., widowhood, retirement, etc.)

Single Multiple Sequential Simultaneous Related Unrelated

Change and dierence (e.g., newly diagnosed heart failure) Time span (time to first home health visit) Critical point and events (e.g., hospital discharge, admission to home health, etc.)

Patterns of response

Transition condition facilitators and inhibitors

Transition types

Patterns

Properties

Personal conditions Sociodemographic characteristics (e.g., age, gender, education, etc.)

Community conditions Adequate housing informal caregiver support available

Society conditions Healthcare reform, etc.

Admission outcomes (avoiding unnecessary hospitalizations: preventing short-term medication mistakes)

Nursing therapeutics Identifying patients’ priority for the first nursing home health visit (assisted by a decision support tool)

acute or chronic illness or vice versa. For instance, the most common reasons for hospitaliza- tion in the US are newly diagnosed illness conditions (such as heart failure) or exacerbation of a chronic disease (such as chronic obstructive pulmonary disease). Thus, the timing of the diagnosis and comorbid conditions played an important role in the data collection and analysis of Topaz’s study.

According to transitions theory, it is necessary to uncover the personal, community, and societal conditions that facilitate or hinder progress toward achieving a healthy transition, also called transition facilitators and inhibitors (Meleis, 2010). Personal conditions include meanings that patients attribute to the transitions; these meanings might facilitate or hinder healthy transition. Transitions affect and are affected by cultural beliefs and attitudes. Socio- economic status might serve as an inhibitor or facilitator of an optimal transition. In practice, it meant that the study needed to incorporate a patient’s sociodemographic variables (e.g., age, gender, education level) and information about community supports available (e.g., care- giver’s availability and willingness to help). The goal of the study was to create and validate a decision support tool (nursing therapeutics according to transitions theory) to assist clinicians to identify a patient’s priority for the first nursing visit. See Figure 2-3 for more details on the transitions-theory domains and factors.

For his dissertation, Topaz merged two theories to create a cohesive guiding theoretical framework using the discipline-specific transitions theory to examine the individual’s tran- sition from hospital to home-health settings. The transitions theory guided the analysis of factors (disease characteristics, medications, patient needs, and social-support characteristics).

M02_HEBD1010_06_SE_C02.indd 27 3/15/18 3:00 PM

28 Chapter 2

The DIKW framework (American Nurses Association, 2008) was used to explicitly present all the informatics steps during the construction of a decision-support tool, the final goal of this study.

In other words, the transitions theory guided the selection of discrete data points during the transition process (patient’s clinical, environmental, and social-support characteristics); creation of meaningful information about the patient’s medical-and-social conditions (orga- nized into case studies); and analysis of the linkages between different information clusters to create a hierarchy of factors representing the knowledge of patient’s priority for the first home-health nursing visit. This knowledge was used to create a tool to support patient priori- tization at admission to home health. Figure 2-4 presents the combination of the frameworks to address study goals. Other researchers in nursing informatics might consider this merged approach to generate useful theoretical frameworks for their studies.

Additional Supporting Theories and Sciences These additional theories and sciences include communication theory, information sciences, computer science, group dynamics, change theories, organizational behavior, learning theo- ries, management science, and systems theory.

Figure 2-4 • The data information knowledge wisdom theory coupled with the transitions theory as applied in Topaz’s dissertation study.

SOURCE: From Developing a Tool to Support Decisions on Patient Prioritization at Admission to Home Health Care by Maxim Topaz. Copyright © 2014 by University of Pennsylvania.

Aim I Extracting the factors

Aim II Constructing

and validating decision

support tool

Beyond study scope

Interpreting and Understanding Knowledge on

nursing therapeutics

Ethical and compassionate application of the decision support

tool by nurses Wisdom to improve

patterns of response

Organizing Information on

transition conditions: facilitators and inhibitors

Naming and Collecting Data on

nature of transition (type, pattern and

properties)

Transitions Theory will assist:

M02_HEBD1010_06_SE_C02.indd 28 3/15/18 3:00 PM

Informatics Theory and Practice 29

Theory/Science Key Ideas

Information Communication Model

“The fundamental problem of communication is that of reproducing at one point, either exactly or approximately, a message selected at another point” (Shannon, 1948, p. 379). Sender S Medium (Noise and Distortion) S Receiver Encoder and Decoder Focus—Analyze information transfer and communication effective- ness and efficiency

Information Sciences

Exploitation of scientific and technical information of all kinds and by all means. Application of science and technology to general information handling. Branches:

• Information retrieval • Human-computer interaction • Information handling within a system

Computer Science Engineering and technology of hardware, software, and communications. Includes aspects of information and cognitive science.

Group Dynamics Focuses on the nature of groups. Influence of a group may rapidly become strong, influencing or over- whelming individual proclivities and actions. Within every organization, there are formal and informal group pressures.

Change Theories Change in people or social systems, such as healthcare organizations. Informatics specialists are change agents. Seek to manage impact of IS to yield positive results. Two perspectives:

• Planned Change—Kurt Lewin • Unfreezing • Moving • Freezing

• Diffusion of innovations—E. Rogers

• Process for communicating an innovation throughout a social system. • Innovators • Early adopters • Early majority • Late majority • Laggards

• Rogers identified five perceived characteristics of an innovation that affect the rate of adoption: • Relative advantage • Compatibility • Complexity • Trialability • Observability

• Adoption of an innovation by an individual is dependent on the perceptions the individual has of that innovation.

M02_HEBD1010_06_SE_C02.indd 29 3/15/18 3:00 PM

30 Chapter 2

Theory/Science Key Ideas

Organizational Behavior

Focuses on small groups and individuals within organizations. Organizational health requires a balance, among participants, of:

• Autonomy • Control • Cooperation

Guides plans for system implementation.

Learning Theories Changes in knowledge, skills, attitudes and values. More than 50 major theories of learning. Types of theories:

• Behavioral • Cognitive • Adult learning • Learning styles

Management Science

Use mathematics and other analytical methods to help make better decisions of all kinds, including clinical decision-support applications. Methods:

• Forecasting • Decision analysis • Inventory models • Linear programming • Graph theory and network problems • Queuing theory and waiting line problems • Simulation

Systems Theory Studies the properties of systems as a whole. Focuses on the organization and interdependence of relationships. Boundaries:

• Open • Closed

Systems are constantly changing.

• Dynamic homeostasis • Entropy • Negentropy • Specialization • Reverberation • Equifinality

Informatics Specialties within Healthcare In general, informatics, as it applies to healthcare, is comprised of several specialties based on areas of application and inquiry. Historically, two terms were interchangeably used to refer to the field: medical informatics and bioinformatics. These terms reflected either a medical orientation of the profession (e.g., the use of information-technology tools and approaches by medical doctors) or a biological orientation focused on issues around basic biology (e.g., the human genome project that determined the sequence of human DNA and mapped all of the genes). Over time, with emergence of new health-informatics disciplines, such as nursing informatics or imaging informatics, both of the terms were used to refer to the new

M02_HEBD1010_06_SE_C02.indd 30 3/15/18 3:00 PM

Informatics Theory and Practice 31

subfields. These traditional terms were also incorporated into the names of the major health informatics organizations, for example, International Medical Informatics Association (IMIA).

More recently, however, with a growing understating of the expanding body of work within the field, many organizations have revised their agendas and visions to incorporate a broader scope of informatics specialties. For the purposes of a more detailed description of specialties, the general view of informatics suggested by the American Medical Informatics Association (AMIA) will be used (Kulikowski et al., 2012). AMIA now refers to the discipline as biomedical informatics, defined as “the interdisciplinary field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving, and decision making, driven by efforts to improve human health” (Kulikowski et al., 2012, p. 933).

As depicted in Figure 2-5, this definition suggests that biomedical informatics is a core discipline that provides methods, techniques, and theories to its subdisciplines including (1) bioinformatics and structural (imaging) informatics; (2) health informatics, including clinical informatics (with subfields of nursing, medical, and dental informatics) and public-health infor- matics (also referred to as population informatics to incorporate global health informatics); (3) and informatics in translational science with subfields of translational bioinformatics and clinical-research informatics. AMIA’s definition also suggests that biomedical informatics lends its approaches to solve problems across the spectrum, ranging from molecular and cellular levels to the patient and population levels. The following descriptions define each of the subdisciplines:

• Bioinformatics is often defined as studying biology (e.g., physical and/or chemical struc- tures of macromolecules) by applying informatics skills to understand and organize the information associated with these molecules on a large-scale. Bioinformatics is primar- ily concerned with three types of data from molecular biology: macromolecular struc- tures, genome sequences, and the results of functional genomics experimentation (e.g., gene expression data). Additional types of data that are often used in bioinformatics might include the scientific literature (e.g., large collection of articles from Pubmed on genomic associations), taxonomies and standard terminologies (e.g., gene taxonomies),

Figure 2-5 • Biomedical informatics and its areas of application and practice, spanning the range from molecules to populations and society.

SOURCE: From AMIA Board White Paper: Definition Of Biomedical Informatics And Specification Of Core Competencies For Graduate Education In The Discipline by Casimir A Kulikowski, Edward H Shortliffe, Leanne M Currie et.al. in Journal of American Medical Informatics Association. Used by permission of Oxford University Press/ on behalf of the sponsoring society if the journal is a society journal.

Biomedical informatics (BMI) education and research

Methods, techniques, theories

Bioinformatics and structural (imaging)

informatics Applied research

and practice

Molecules, cells, tissues, organs Patients, individuals, populations, societies

Basic research

Health informatics (HI): clinical informatics

and public health informatics

Informatics in translational science: translational bioinformatics (TBI) and clinical

research informatics (CRI)

M02_HEBD1010_06_SE_C02.indd 31 3/15/18 3:00 PM

32 Chapter 2

and protein-protein interaction data. Informatics techniques are applied on these data to achieve clinically meaningful tasks, such as designing new drugs.

• Structural (imaging) information refers to research and practical applications concerned with representing, managing, and using information about the physical organization of the body (Brinkley, 1991). The notion of structural in the subdiscipline name often refers to the structure of objects in space. Informatics methods are used to store, study, and use data from studies about human-body structure. For example, a chest computerized- tomography (CT) image can be classified via image recognition with machine learning to identify or rule out a presence of lung cancer (van Rikxoort & van Ginneken, 2013).

• Nursing informatics is a subdiscipline of clinical informatics included in the general domain of health informatics. Nursing informatics uses nursing knowledge, along with information and communication technology to promote the health of individuals, fami- lies, and entire populations. For more information, see chapter 1 of this book.

• Medical informatics is another subdiscipline of clinical informatics included in the gen- eral domain of health informatics. Medical informatics refers to research and practice in clinical informatics that focuses on disease and predominantly involves the role of physicians. This term was used interchangeably with other terms in the past to refer to the discipline of biomedical informatics as a whole (Kulikowski et al., 2012).

• Dental informatics is yet another subdiscipline of clinical informatics included in the general domain of health informatics. It is defined as a multidisciplinary field that seeks to improve health care through the application of health-information technology and information science to dental-health delivery, information management, healthcare administration, research, and knowledge sharing.

• Public-health informatics, included in the general domain of health informatics, is the science of applying information technology in areas of public health, including preven- tion, preparedness, health promotion, and surveillance. Public-health informatics takes a perspective of groups of individuals and focuses on work, neighborhoods, and envi- ronment of work and living places, among others. Some of the common areas in public- health informatics include biosurveillance (e.g., mentions of new spreading viruses on social media), epidemic-outbreak management, or ranking neighborhoods in one county in terms of health problems.

• Translational bioinformatics, included within the domain of informatics in translational science, combines applications of health informatics, bioinformatics, and structural informatics to identify genomic and cellular mechanisms to explain and predict clinical phenomena. Translational bioinformatics develops innovative techniques for the integra- tion of biological and clinical data to create a more personalized healthcare. The recent emergence of precision medicine, aimed at providing all individuals with access to per- sonalized information for better health, builds heavily on translational-bioinformatics methods to develop accurate and personalized characterization of patient populations based on molecular, clinical, environmental exposures, lifestyle, and other patient infor- mation (Frey, Bernstam, & Denny, 2016).

• Lastly, clinical-research informatics is primarily focused on methods supporting clinical and translational research. Its goals are discovery and management of new knowledge about diseases and health. Clinical-research informatics is often applied to identify ways for secondary research use of clinical data or to manage information related to clinical trials (Kulikowski et al., 2012).

All the subdisciplines of biomedical informatics interact among each other to provide a comprehensive suite of informatics tools for better healthcare practice and research. Nursing

M02_HEBD1010_06_SE_C02.indd 32 3/15/18 3:00 PM

Informatics Theory and Practice 33

informatics draws on informatics disciplines, such as medical or public-health informatics to advance its goals of promoting health worldwide. On the other hand, other informatics sub- disciplines need nursing informatics to achieve their goals; for example, medical-informatics problems will often depend on nursing data to identify appropriate solutions. For instance, physicians prescribing medications need to understand a patient’s adherence status to be able to match complex medication regimes for a specific patient.

Informatics Competencies for Healthcare Practitioners To achieve its goals, biomedical informatics needs to define a set of competencies for its practitioners and academics. However, before diving into competencies, several interesting professional challenges should be addressed. First, biomedical informatics is an inherently interdisciplinary field that draws on theories and problem-solving approaches from healthcare, computer science, statistics, decision science, and other relevant fields. To achieve a common goal, representatives of all the different disciplines need to share a set of common terms and understandings. This set is sometimes referred to as the biomedical-informatics core competen- cies (Kulikowski et al., 2012). Second, some competencies are more geared towards biomedical- informatics practitioners (for example, a nursing informatics specialist working in a hospital system needs to understand specifics of standards for health-information exchange) while other competencies are critical for informatics researchers in academia (e.g., researchers need skills in situation-specific theory development while analyzing data from interviews with nurses who use electronic-health-records systems). These complexities shape the nature of the biomedical-informatics competency recommendations. The next few paragraphs will describe some early and recent work on biomedical-and-nursing-informatics competencies.

Work of Staggers, Gassert, and Curran In the early 2000s, Staggers, Gassert, and Curran (2002), conducted an influential Delphi study that was one of the first to produce a research-based list of informatics competencies for nurses. The study also differentiated the competencies by levels of nursing practice. Nursing informatics experts (n = 72) surveyed in this study agreed on a list of 281 competencies for nurse informaticians.

The study stratified nurses into four categories by which the list of expected competen- cies was organized:

Level 1—Beginning nurse: expected to have fundamental information-management and computer-technology skills and use existing information systems and established information-management practices. Forty-three skills-and-knowledge competencies were identified in the domains of administration (e.g., using applications for structured data entry), system (e.g., using computer technology safely), and impact (e.g., recognizing that health computing will become more common), among others.

Level 2—Experienced nurse: expected to have a specific area of expertise (e.g., public health, education, administration); be skilled in using information management and computer technology; have strong analytic skills to learn from relationships between different data elements; and be able to collaborate with the informatics nurse specialist to suggest improvement to systems. The 35 identified skills and knowledge competencies included domains of desktop software (e.g., using desktop publishing), evaluation (e.g., evaluating the accuracy of health information on the Internet), and systems maintenance (e.g., performing basic trouble-shooting in applications), etc.

M02_HEBD1010_06_SE_C02.indd 33 3/15/18 3:00 PM

34 Chapter 2

Level 3—Informatics specialist: defined as a nurse with advanced skills specific to health- information management and computer technology. Nurse specialist was expected to focus on information needs for the practice of nursing, which included education, admin- istration, research, and clinical practice and use critical thinking, process skills, data- management skills (including identifying, acquiring, preserving, retrieving, aggregating, analyzing, and transmitting data), expertise in the systems development life cycle, and computer skills. One-hundred-eighty-six skills and knowledge competencies were in the domains of data (e.g., constructing data structures and maintaining data sets), design and development (e.g., developing screen layouts, report formats, and custom views of clini- cal data through working directly with clinical departments and individual users), and training (e.g., producing short-term and long-term training plans), etc.

Level 4—Informatics innovator: expected to be educationally prepared to conduct infor- matics research and generate informatics theory and have advanced understanding and skills in information management and computer technology. Forty skills and knowledge competencies were identified in the domains of research (e.g., developing innovative and analytic techniques for scientific inquiry in nursing informatics), practice (e.g., applying advanced analysis and design concepts to the system life cycle process), and fiscal man- agement (e.g., obtaining research funding), among others (Staggers et al., 2002).

The work of Staggers, Curran, and Gassert was used as the basis for many further compe- tency initiatives and full list of competencies can be found at http://himssni.pbworks.com/f/ Delphi+Study+Article.pdf

AMERICAN NURSES ASSOCIATION COMPETENCIES IN NURSING INFORMATICS SCOPE OF PRACTICE In the US, the ANA is one of the largest nursing professional orga- nizations, representing more than 3.4 million nurses. Since the early 1990s, ANA dedicated a significant amount of effort towards development of the specialty of nursing informatics. To accomplish this goal, ANA engaged nursing-informatics leaders from academia and prac- tice to develop a scope of practice. Formal recognition of nursing informatics as one of the specialty practice areas for nursing occurred in 1992 (American Nurses Association, 1994).

In its first edition of the nursing-informatics scope of practice, the ANA defined nursing informatics as “Nursing informatics is the specialty that integrates nursing science, computer science, and information science in identifying, collecting, processing, and managing data and information to support nursing practice, administration, education, research, and the expan- sion of nursing knowledge” (American Nurses Association, 1994, p. 4). A nursing-informatics practitioner, referred to as an informatics nurse, was defined as a nurse with bachelor’s degree in nursing and additional experience and knowledge in informatics. ANA defined 18 com- petencies for the informatics nurse, including systems design and analysis, use of software, and application of computer-programming tools. At the next level of practice, the informatics nurse specialist was defined as a nurse with a masters’ degree in nursing and graduate-level courses in bioinformatics. These nurses were expected to master an additional seven compe- tencies, such as nursing-informatics theory development, consulting skills, and the ability to develop procedures and policies for evaluating and improving nursing-information technol- ogy applied in clinical practice.

Following this initial seminal document, the ANA revised and enhanced the scope of nursing practice throughout the years. Some of the major consecutive changes reflected the growth and maturation of nursing informatics as a discipline. For example, the 2001 revised edition of Scope and Standards of Informatics Practice (American Nurses Association, 2001), placed more focus on management and communication of data, information, and knowl- edge (based on the DIKW framework) in nursing practice compared to the original focus

M02_HEBD1010_06_SE_C02.indd 34 3/15/18 3:00 PM

http://himssni.pbworks.com/f/Delphi+Study+Article.pdf
http://himssni.pbworks.com/f/Delphi+Study+Article.pdf
Informatics Theory and Practice 35

on activities related to identification, collection, processing, and management of informa- tion. This was reflected in the renewed definition of nursing informatics as a “specialty that facilitates the integration of data, information, and knowledge to support patients, nurses, and other providers in their decision-making in all roles and settings.” (American Nurses Association, 2001, p. 122).

In 2008, ANA’s approach was revised, which resulted in a new document—that was better aligned with other nursing specialties—titled Nursing Informatics: Scope and Standards of Practice (American Nurses Association, 2008). In this document, the concept of wisdom (the last part of the DIKW theory) was added to the definition of nursing informatics. Also, the document included a matrix of skills addressing the competencies identified by Staggers et al. (2002).

The 2015 revision of the scope and standards of practice in nursing informatics followed its predecessors with more content about competencies for all nurse informaticians and additional competencies for the informatics nurse specialist. Competencies now are structured to fall under each of the 16 nursing informatics practice standards, presented below:

• Standard 1. Assessment

• Standard 2. Diagnosis, Problems, 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

• Standard 5C. Consultation

• Standard 6. Evaluation and Standards of Professional Performance for Nursing Informatics.

• Standard 7. Ethics

• Standard 8. Education

• Standard 9. Evidence-Based Practice and Research

• Standard 10. Quality of Practice

• Standard 11. Communication

• Standard 12. Leadership

• Standard 13. Collaboration

• Standard 14. Professional Practice Evaluation

• Standard 15. Resource Utilization

• Standard 16. Environmental Health

THE AMERICAN ASSOCIATION OF COLLEGES OF NURSING ESSENTIALS The American Association of Colleges of Nursing (AACN) is a US-based organization that estab- lishes quality standards for nursing education, assists schools of nursing in implementing those standards, and provides accreditation for baccalaureate and graduate nursing educa- tion. AACN’s accreditation process evaluates the curricula of each particular educational organization and ensures that an essential set of professional competencies are addressed. In 2008, AACN published the set of baccalaureate educational-program requirements titled The Essentials of Baccalaureate Education for Professional Nursing Practice where one of the nine essentials is focused explicitly on health- information technology (American Association of Colleges of Nursing, 2008).

M02_HEBD1010_06_SE_C02.indd 35 3/15/18 3:00 PM

36 Chapter 2

AACN identifies several aspects of information management and application of patient- care technology that every baccalaureate graduate should master. For example, graduates are supposed to be able to “use standardized terminology in a care environment that reflects nurs- ing’s unique contribution to patient outcomes” (American Association of Colleges of Nursing, 2008, p. 29). Some other competencies are more general and relate to ethical and high-quality application of health-information technology, ability to evaluate the existing systems, etc. See page 18 of the Essentials report for full list of graduate competencies required (American Association of Colleges of Nursing, 2008). Similarly, the AACN required in 2011 that master’s- prepared nurses are able to use patient-care technologies to deliver and enhance care and use communication technologies to integrate and coordinate care (American Association of Colleges of Nursing, 2011). For the doctoral level of education (AACN regulates the Doctor of Nursing Practice—DNP—education), AACN requires that DNP graduates must be proficient in the use of information-technology resources to implement quality-improvement initiatives and support practice-and-administrative decision-making. Graduates also need to learn and be proficient in selecting and evaluating information systems and patient-care technology, and related ethical, regulatory, and legal issues (American Association of Colleges of Nursing, 2006).

TIGER In 2004, an initiative called Initiative for Technology Informatics Guiding Education Reform— or TIGER—was formed to advance nurses’ competencies related to informatics. TIGER’s primary objective was to develop a US nursing workforce capable of using electronic health records to improve the delivery of health care. The TIGER initiative brought together nursing stakeholders to develop a shared vision, strategies, and specific actions for improving nursing education, practice, and the delivery of patient care through the use of health-information technology (Health Information Management Systems Society, 2013). In 2006, the TIGER initiative published a summary report titled Evidence and Informatics Transforming Nursing: 3-Year Action Steps toward a 10-Year Vision (Skiba et al., 2006).

TIGER’s participants reviewed the existing literature and outlined a minimum set of competencies to focus on for all nurses. TIGER generated three categories of competencies, and each of the three categories had several subcategories.

Basic Computer Competencies: includes areas such as hardware, software, security, Internet, and email use, among others. For each area, several subcategories with spe- cific competencies are offered; for example, users are supposed to understand that some devices are both input and output devices, such as touch screens (hardware domain) or be able to forward an email (email domain).

Information Literacy: a set of abilities allowing individuals to recognize when information is needed and to locate, evaluate, and use that information appropriately, according to the Association of College and Research Libraries (2000). Information literacy builds on computer literacy and refers to a user’s ability to identify information needed for a specific purpose, locate pertinent information, evaluate the information, and apply it correctly.

Information Management: consists of (a) collecting data, (b) processing the data, and (c) presenting and communicating the processed data as information or knowledge. DIKW theory served as the basis for this set of competencies.

In recent years, TIGER’s work was adopted and now is managed by HIMSS (Health- care Information and Management Systems Society), a professional association of health- information-technology stakeholders and venders (Healthcare Information and Management Systems Society, 2013).

M02_HEBD1010_06_SE_C02.indd 36 3/15/18 3:00 PM

Informatics Theory and Practice 37

TANIC and NICA Several instruments exist to assess nurses’ competencies in informatics. For example, Hunter, McGonigle, and Hebda (2013) have developed a set of tools to assess informatics competen- cies at all levels. First, they used the competency recommendations from the TIGER initia- tive to identify and validate a comprehensive list of competencies in the domains of: Basic Computer Skills (e.g., ability to sort files or rename files and folders); Clinical Information Management (e.g., ability to print standardized reports or knowledge of procedures to main- tain security of organizational information); and Information Literacy (e.g., ability to synthe- size conclusions based upon information gathered or understanding of free versus fee-based access to information). The developed instrument is called TIGER-based Assessment of Nurs- ing Informatics Competencies (TANIC)©.

In later work Hill, McGonigle, Hunter, Sipes, and Hebda (2014) also developed an instru- ment for assessing advanced nursing informatics competencies called the Nursing Informat- ics Competency Assessment L3/L4 (NICA - L3/L4)©. The tool is using three domains to assess competencies: Computer Skills (e.g., determine the impact of computerized informa- tion management on manager and executive roles through program evaluation); Informat- ics Knowledge (e.g., use cognitive-science principles and artificial-intelligence theories to participate in the design of technology appropriate to the cognitive abilities of the user); and Informatics Skills (consult with clinical, managerial, educational, and or research entities about informatics).

Future Directions So what is the future of nursing informatics competencies? One possible direction can be found in a recent survey conducted by a group of nursing informatics students with the Inter- national Medical Informatics Association–Nursing Informatics Working Group (IMIA-NI) (Peltonen et al., 2016; Topaz et al., 2015). The survey was focused on the current and future trends in nursing informatics with more than 500 nurse-informatician participants from more than 40 countries. When responding to the question “What should be done (at a country or organizational level) to advance nursing informatics in the next 5–10 years?”. Survey participants’ responses identified five key themes: (a) Education and training; (b) Research; (c) Practice; (d) Visibility; and (e) Collaboration and integration (Topaz et al., 2016).

Several existing nursing-informatics-competency recommendations (e.g., TIGER) can be used to help make progress in the five key areas. However, there are also gaps in existing competencies; for instance, in-service education for practicing nurses and their competencies remain largely unaddressed. Also, there are only a few separate initiatives aimed at identify- ing competencies necessary to promote nursing informatics visibility or ability to collaborate and integrate with other professions. These gaps can help set agendas for future competency development (Ronquillo, Topaz, Pruinelli, Peltonen, & Nibber, 2017; Topaz et al., 2016).

In addition, several new areas for future nurse informaticians have recently emerged and are becoming more prevalent. For example, big data is a recent term referring to large, unstructured datasets that are becoming increasingly available in health-related domains. Examples include millions of social-media postings (e.g., Twitter or Facebook) about new side effects to an established or new medication (e.g., Topaz, Lai, et al., 2016) or patients’ opinions about a certain hospital. All these data can help nurses better understand their cli- ents while, on the other hand, presenting multiple challenges, e.g., noise reduction, signal detection, free-text analytics etc. Thus, big-data science will require a variety of techniques for analyzing inputs, from traditional statistics to visualization techniques, data mining,

M02_HEBD1010_06_SE_C02.indd 37 3/15/18 3:00 PM

38 Chapter 2

and natural-language processing (Topaz & Pruinelli, 2017). With the increasingly growing role of big data in health analytics, nurses are starting to develop new tools and approaches to turn this data into information and wisdom to guide clinical practice.

Summary • Theories serve to guide research and practice. • Nursing theory serves to describe phenomena, explain relationships, predict conse-

quences, and prescribe care. • Nursing theory can be categorized as grand, middle-range, or situation-specific

theory. • Grand theories are broad and not amenable to empirical testing. • Middle-range theories focus on specific phenomena, reflect practice, and lend them-

selves to empirical testing. • Situation-specific theory focus on a specific nursing phenomenon and are often

bound to a specific type of clinical practice and population. • Several theories inform and support informatics including, but not limited to, the

data, information, knowledge, and wisdom (DIKW) theory, the theory of wisdom in action, and transitions theory.

• Communication theory, information sciences, computer science, group dynamics, change theories, organizational behavior, learning theories, management science, and systems theory also contribute to the underpinnings of informatics.

• Several informatics specialties exist within healthcare. These include biomedical informatics, bioinformatics, structural (imaging) informatics, and nursing informatics.

• Competencies must be defined to accomplish informatics goals. • Development and evolution of nursing informatics competencies draw from the

work of diverse contributors. • Informatics competencies have been identified for nurses and other healthcare

professionals by several groups including, but not limited to, the American Nurses Association, the American Association of Colleges of Nursing, the Technology Informatics Guiding Education Reform (TIGER) Initiative, and the Health Informa- tion and Management Systems Society.

• Several instruments exist to assess informatics competencies both at basic and advanced levels. The TIGER-based Assessment of Nursing Informatics Competencies (TANIC)© and Informatics Competency Assessment L3/L4 (NICA - L3/L4)© represent tools developed to test competencies at four levels of practice—beginner and experienced nurses, informatics nurse specialist, and innovator, respectively.

About the Author Maxim Topaz is a postdoctoral research fellow at the Harvard Medical School and Brigham Women’s Health. His passion is applying new technologies to improve people’s health. Maxim’s expertise includes nursing and health informatics theory, clinical decision support, and data and text mining (including natural language processing).

M02_HEBD1010_06_SE_C02.indd 38 3/15/18 3:00 PM

Informatics Theory and Practice 39

References Ackoff, R. (1989). From data to wisdom. Journal of Applied Systems Analysis, 16(1), 3–9. American Association of Colleges of Nursing. (2006). The essentials of doctoral education for

advanced nursing practice. Retrieved from www.aacn.nche.edu/dnp/Essentials.pdf American Association of Colleges of Nursing. (2008). The essentials of baccalaureate education

for professional nursing practice © 2008. Retrieved from www.aacn.nche.edu/ publications/order-form/baccalaureate-essentials

American Association of Colleges of Nursing. (2011). The essentials of master’s education in nursing. Retrieved from www.aacn.nche.edu/education-resources/MastersEssentials11.pdf

American Nurses Association. (1994). Scope of practice for nursing informatics © 1994. Washington, DC: Author.

American Nurses Association. (2001). Scope and standards of nursing informatics practice © 2001. Silver Spring, MD: Author.

American Nurses Association. (2008). Nursing informatics: Scope and standards of practice © 2008. Washington, DC: Author.

American Nurses Association. (2015). Nursing informatics: Scope and standards of practice (2nd ed.). Washington, DC: Author.

Association of College and Research Libraries. (2000). Information literacy competency standards for higher education. Retrieved from www.ala.org/acrl/ilcomstan.html.

Benner, P. (2000). The wisdom of our practice. The American Journal of Nursing, 100(10), 99–105. Blum, B. (1986). Clinical information systems. New York: Springer-Verlag. Brinkley, J. F. (1991). Structural informatics and its applications in medicine and biology.

Academic Medicine: Journal of the Association of American Medical Colleges, 66(10), 589–591. Darwin, C. (1859). On the origin of species by means of natural selection, or, the preservation of

favoured races in the struggle for life. London, UK: Murray. Frey, L. J., Bernstam, E. V., & Denny, J. C. (2016). Precision medicine informatics. Journal of

the American Medical Informatics Association: Journal of the American Medical Informatics Association, 23(4), 668–670. doi:10.1093/jamia/ocw053

Graves, J., & Corcoran, S. (1989). The study of nursing informatics. Journal of Nursing Scholarship, 21(4), 227–231.

Hill, T., McGonigle, D., Hunter, K. M., Sipes, C., & Hebda, T. L. (2014). An instrument for assessing advanced nursing informatics competencies. Journal of Nursing Education and Practice, 4(7), 104. doi:10.5430/jnep.v4n7p104

Health Information Management Systems Society. (2013). The TIGER Initiative. Retrieved from www.himss.org/professionaldevelopment/tiger-initiative

Hunter, K. M., Mcgonigle, D. M., & Hebda, T. L. (2013). TIGER-based measurement of nursing informatics competencies: The development and implementation of an online tool for self-assessment. Journal of Nursing Education and Practice, 3(12), 70–80. doi:10.5430/jnep.v3n12p70

Kulikowski, C. A., Shortliffe, E. H., Currie, L. M., Elkin, P. L., Hunter, L. E., Johnson, T. R., & Williamson, J. J. (2012). AMIA Board white paper: Definition of biomedical informatics and specification of core competencies for graduate education in the discipline. Journal of the American Medical Informatics Association, 19(6), 931–938. doi:10.1136/ amiajnl-2012-001053

Matney, S. (2015). Development of a theory of wisdom in action for clinical nursing. (Unpublished doctoral dissertation). Salt Lake City, UT: The University of Utah. Published by Susan A. Matney, © 2015.

M02_HEBD1010_06_SE_C02.indd 39 3/15/18 3:00 PM

http://www.himss.org/professionaldevelopment/tiger%E2%80%90initiative
40 Chapter 2

Matney, S., Brewster, P. J., Sward, K. A., Cloyes, K. G., & Staggers, N. (2011). Philosophical approaches to the nursing informatics data-information-knowledge-wisdom framework. ANS. Advances in Nursing Science, 34(1), 6–18. doi:10.1097/ANS.0b013e3182071813

Meleis, A. I. (2010). Transitions theory: Middle range and situation specific theories in nursing research and practice. New York: Springer Publishing Company.

Meleis, A. I. (2015). Theoretical nursing: Development and progress (5th ed.). Philadelphia: Wolters Kluwer Health © 2011.

Meleis, A. I., Sawyer, L. M., Im, E. O., Hilfinger Messias, D. K., & Schumacher, K. (2000). Experiencing transitions: An emerging middle-range theory. ANS. Advances in Nursing Science, 23(1), 12–28.

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.

Nelson, R., & Joos, I. (1989). On language in nursing: from data to wisdom. PLN Visions, Fall, 6–7. Published by Elsevier, © 1989.

Orem, D. E. (1985). A concept of self-care for the rehabilitation client. Rehabilitation Nursing: The Official Journal of the Association of Rehabilitation Nurses, 10(3), 33–36.

Peltonen, L.-M., Topaz, M., Ronquillo, C., Pruinelli, L., Sarmiento, R. F., Badger, M. K., & Alhuwail, D. (2016). Nursing informatics research priorities for the future: Recommendations from an international survey. Studies in Health Technology and Informatics, 225, 222–226. Published by IMIA and IOS Press, © 2016.

Riegel, B., & Dickson, V. V. (2008). A situation-specific theory of heart failure self-care. The Journal of Cardiovascular Nursing, 23(3), 190–196. doi:10.1097/01.JCN.0000305091.35259.85

Riegel, B., Dickson, V. V., & Faulkner, K. M. (2016). The situation-specific theory of heart failure self-care: Revised and updated. The Journal of Cardiovascular Nursing, 31(3), 226–235. doi:10.1097/JCN.0000000000000244

Riegel, B., Dickson, V. V., & Topaz, M. (2013). Qualitative analysis of naturalistic decision making in adults with chronic heart failure. Nursing Research, 62(2), 91–98.

Riegel, B., Jaarsma, T., & Strömberg, A. (2012). A middle-range theory of self-care of chronic illness. ANS. Advances in Nursing Science, 35(3), 194–204. doi:10.1097/ ANS.0b013e318261b1ba

Ronquillo, C., Currie, L. M., & Rodney, P. (2016). The evolution of data-information- knowledge-wisdom in nursing informatics. ANS. Advances in Nursing Science, 39(1), E1–18. doi:10.1097/ANS.0000000000000107

Ronquillo, C., Topaz, M., Pruinelli, L., Peltonen, L., & Nibber, R. (2017). Competency recommendations for advancing nursing informatics in the next decade: International survey results. Studies in Health Technology and Informatics, 232, 119–129.

Skiba, D., Delaney, C., Dulong, D., Walker, P., Mcbride, A., Sensmeier, J., & Weaver, C. (2006). The TIGER initiative evidence and informatics transforming nursing: 3-Year action steps toward a 10-year vision. Retrieved from www.tigersummit.com

Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27, 3, 379–423; 27, 4, 623–656. © 1948.

Staggers, N., Gassert, C. A., & Curran, C. (2002). A Delphi study to determine informatics competencies for nurses at four levels of practice. Nursing Research, 51(6), 383–390.

Topaz, M. (2013). The hitchhiker’s guide to nursing informatics theory: Using the data- knowledge-information-wisdom framework to guide informatics research. Online Journal of Nursing Informatics, 17(3). Available at http://ojni.org/issues/?p=2852

M02_HEBD1010_06_SE_C02.indd 40 3/15/18 3:00 PM

http://www.tigersummit.com/
http://ojni.org/issues/?p=2852
Informatics Theory and Practice 41

Topaz, M. (2014). Developing a tool to support decisions on patient prioritization at admission to home health care (Doctoral dissertation). Retrieved from Publicly Accessible Penn Dissertations. 1473.https://repository.upenn.edu/dissertations/1473

Topaz, M., Lai, K., Dhopeshwarkar, N., Seger, D. L., Sa’adon, R., Goss, F., & Zhou, L. (2016). Clinicians’ reports in electronic health records versus patients’ concerns in social media: A pilot study of adverse drug reactions of aspirin and atorvastatin. Drug Safety, 39(3), 241–250. doi:10.1007/s40264-015-0381-x

Topaz, M., & Pruinelli, L. (2017). Big data and nursing: Implications for the future. Studies in Health Technology and Informatics, 232, 165–171.

Topaz, M., Ronquillo, C., Peltonen, L.-M., Pruinelli, L., Sarmiento, R. F., Badger, M. K., . . . Alhuwail, D. (2016). Advancing nursing informatics in the next decade: Recommendations from an international survey. Studies in Health Technology and Informatics, 225, 123–127. Published by IMIA and IOS Press, © 2016.

Topaz, M., Ronquillo, C., Pruinelli, L., Ramos, R., Peltonen, L.-M., Siirala, E., & Badger, M. K. (2015). Central trends in nursing informatics: Students’ reflections from International Congress on Nursing Informatics 2014 (Taipei, Taiwan). CIN: Computers, Informatics, Nursing, 33(3), 85–89. doi:10.1097/CIN.0000000000000139

van Rikxoort, E. M., & van Ginneken, B. (2013). Automated segmentation of pulmonary structures in thoracic computed tomography scans: a review. Physics in Medicine and Biology, 58(17), R187–220. doi:10.1088/0031-9155/58/17/R187

World Health Organization (WHO). (2014). International classification of diseases (ICD). Retrieved from www.who.int/classifications/icd/en/

M02_HEBD1010_06_SE_C02.indd 41 3/15/18 3:00 PM

http://www.who.int/classifications/icd/en/
42

H er

o Im

ag es

/G et

ty Im

ag es

Chapter 3

Effective and Ethical Use of Data and Information Toni Hebda, PhD, RN-C Kathleen Hunter, PhD, RN-BC, CNE

Learning Objectives

After completing this chapter, you should be able to:

• Distinguish between the metastructures of data, information, knowledge, and wisdom.

• Detail prerequisite conditions for effective and ethical use of data and information.

• Provide exemplars of effective and ethical use of data and information within healthcare.

• Relate issues and concerns for effective and ethical use of healthcare data and information.

• Discriminate between the terms big data, data science, data analytics, and data modeling.

• Summarize the current state of big data use within healthcare.

• Differentiate between clinician and informatics roles with big data and analytics.

Overview of Data and Information Before one can discuss effective and ethical use of data and information, it is necessary to define data and information. Nursing informatics: Scope and standards of practice used the classic work of Graves and Corcoran to define data as “discrete entities that are described objectively without interpretation” and information as “data that have been interpreted, organized, or structured” (American Nurses Association, 2015a, p. 2). The scope-of-practice section also referenced the same work to define knowledge as synthesized information that showed for- mally recognized relationships. Knowledge is important to the ability to effectively use data. But it is the fourth metastructure of nursing informatics, wisdom, which is critical to effective

M03_HEBD1010_06_SE_C03.indd 42 3/8/18 10:59 AM

Effective and Ethical Use of Data and Information 43

and ethical use of data, information, and knowledge. Wisdom is the ability to appropriately use knowledge to recognize and handle complex problems.

Ethical Use of Data and Information Effective and ethical use of data and information is consistent with professional standards of practice and essential to safe, efficient healthcare delivery, a learning healthcare delivery sys- tem, attainment of optimal individual and population health outcomes, and the transformation of the current, inefficient healthcare delivery system to one that provides quality, personalized care, at lower costs. As an example, the American Nurses Association’s (ANA) (2015b) code of ethics calls for the nurse to respect the client whether that is one person, a family, a group, or a population. As patient advocate, the nurse is charged with protecting the health, safety, and rights of the patients. This protection extends to information and the use of systems that house patient information. The code of ethics also calls for nurses to actively participate in shaping social and health policy for the benefit of all. The code of ethics for nurses provides a founda- tion for nursing informatics practice. The specialty of nursing informatics then builds upon this foundation with a practice standard for ethics, Standard 7 (American Nurses Association, 2015a). Standard 7 delineates competencies for two levels of informatics practice—informatics nurse and informatics nurse specialist. Informatics nurses are called upon to:

• Evaluate factors related to handling data, information, and knowledge

• Help resolve ethical issues involving consumers, other healthcare providers (HCPs), and stakeholders

• Report or take action when illegal, unethical, or inappropriate behaviors are noted that could harm individuals or organizations

• Question practices as necessary for the purpose of maintaining or promoting safety and quality improvement

• Promote effective workflows

• Advocate for consumer access to their records and work to reduce disparities in access and related issues such as eliteracy.

In addition to the above competencies, Standard 7 calls for the informatics nurse specialist to:

• Actively participate in interprofessional teams that address ethical concerns, consumer benefits, and outcomes

• Apprise administrators of ethical concerns, consumer benefits, and outcomes

• Foster engagement of all stakeholders in the oversight and management of data, infor- mation, and knowledge.

The International Medical Informatics Association (IMIA) approved its updated code of ethics for health information professionals in 2016 (International Medical Informatics Association, 2016). The preamble to the IMIA code calls for flexibility to accommodate an ever- changing environment without sacrificing the application of basic principles. IMIA also notes that health informatics professions interact with, and need to weigh, the needs and sometimes conflicting demands of consumers, HCPs, administrators, healthcare delivery organizations, payers, researchers, governments, and society while adhering to the IMIA code of ethics. The principles and rules of ethical conduct outlined by IMIA, like the ANA Code of Ethics, provide guidance for informatics practice that includes directions for effective and ethical use of data and information. Clearly informatics professionals have major responsibilities related to shap- ing how data and information are used.

M03_HEBD1010_06_SE_C03.indd 43 3/8/18 10:59 AM

44 Chapter 3

Data Sharing versus Data Silos Today’s healthcare system has many databases maintained by different groups—often designed to meet the needs of a specific population. Within a single healthcare delivery system, each entity maintains its own records—the hospital, specialty areas, and the physician. There are also local urgent-care clinics, public health departments, subacute and long-term care facilities, and pharmacies, which may or may not share information with other providers. This situation leads to different versions of data, missing data, redundant data collection, and contributes to poten- tially dangerous errors and wasted resources. As an example, Jane Doe’s primary healthcare record lists the antibiotic Keflex and latex for her allergies, but when Jane is taken to the trauma center unconscious, unaccompanied, and without a medical-alert bracelet, the only allergy listed at the trauma center is antibiotic Keflex. This example of incomplete information could expose Jane to an allergen with potentially deadly results because information was not shared.

Sharing health information, otherwise known as health information exchange (HIE), pro- vides a means to reduce redundant tests, improve quality of care, and improve public confidence (Bailey et al., 2013; Kuehn, 2014). In one example, Bailey et al. (2013) examined longitudinal HIE data for a region that connected 15 hospitals and two clinic systems to find decreased diagnostic testing and improved adherence to evidence-based guidelines for the care of patients evaluated for headaches in the emergency department. But HIE alone is not enough. A 2016 report that interviewed more than 500 clinical EHR users noted that meaningful exchange of data requires data to be available when needed, easy to find, within the workflow, and delivered in an effective way—yet participants reported the presence of all four criteria only 6% of the time (Leventhal & Hagland, 2017). In 2016, several major healthcare information-system vendors committed to a framework for interoperability and data-sharing principles set forth by Carequality, a public- private collaborative and an initiative of the Sequoia Project, which released its interoperability framework in 2015. The Sequoia framework established legal, policy, and technical specifications for sharing data as well as processes for governance. This initiative reflected a major change among vendors which historically had not previously cooperated. The next anticipated break- through is to ensure that shared elements remain the same across vendor platforms.

The issue of sharing data goes beyond HIE to include study findings. Not all research is published (Kuehn, 2014). Some findings are submitted to clinical-trial registries, to regulators as a requirement for marketing approval, or alternately, may never be seen by anyone but the researchers. This uneven access fosters inappropriate assumptions and violates the ethical obli- gation that researchers have to their subjects. In other developments, recent years have witnessed the creation of international collaboratives for research and biobanks, both of which entail exten- sive sharing (Dove, 2015). Biobanks collect human biological material and related data that are stored for research purposes, which may not even be defined at the time that materials are stored.

Sharing data is an ethical and scientific imperative that can expedite health gains, create new public health value, and fulfill patient expectations that data will be used in the best ways (Bauchner, Golub, & Fontanarosa, 2016; Davidson, 2015; Haug, 2017), but no common ethical and legal framework yet exists to connect healthcare providers with regulators, funders, and research projects that will link genomic and clinical data, limiting potential benefits (Knoppers, Harris, Budin-ljosne, & Dove, 2014).

Using Data for Quality Improvement Increasingly, data is viewed as a strategic resource (Otto, 2015). One of the most pressing concerns is the ability to generate and use sufficient data related to quality. Organizations use data to meet regulatory requirements, to facilitate the move from fee-for-service to a

M03_HEBD1010_06_SE_C03.indd 44 3/8/18 10:59 AM

Effective and Ethical Use of Data and Information 45

value-based model of care, to measure quality of care, outcomes, and services, and day-to-day operations, in order to remain solvent (Using data, 2016; Smith, 2013). The ability to meaning- fully measure quality requires the presence of several data attributes. In addition to a full or complete set of data from all necessary sources (as was mentioned under data sharing), data must be clear, accurate, available when needed, precise, verifiable by other means, without bias, current, appropriate to the needs of the user, and in a convenient form for interpreta- tion, classification, storage, retrieval, and updates. High quality data are essential for better information, better decision-making, and better outcomes (Chen, Hailey, Wang, & Yu, 2014; Otto, 2015).

Data Quality Data integrity is a comprehensive term that encompasses the notion of wholeness when data is collected, stored, and retrieved by the user. For data to be complete and orderly, a system- atic approach must be used to ensure preservation of data integrity. Data integrity is crucial in the healthcare environment because data serves as a driving force in determining treatments. Information technology (IT) must ensure that healthcare decisions are based on authentic data. If the quality of data is flawed or incorrect, so are subsequent decisions. If data is faulty or incomplete, the quality of derived information will be poor, resulting in inappropriate and possibly harmful decisions. For example, if the nurse interviewing a client collects data related to allergies but fails to document all reported allergies, the client could be given drugs that cause an allergic reaction. In this case, the data were collected but not stored. Computer systems can be designed to facilitate data collection (although entry of incorrect data through input errors is still possible). Input errors can include hitting the wrong key on a computer keyboard or selecting the wrong item from a list. Input errors may be decreased through staff education, periodic system checks, and providing opportunities to verify data prior to entry.

Although the initial data collection and entry process provides an excellent opportu- nity to verify data accuracy and completeness, it should not be the only time that this is done. Healthcare consumers should be able to review their records at any time and furnish additional information that they believe is important to their care or to dispute portions of their record with which they do not agree. A system check is a mechanism provided by the computer system to assist users by prompting them to complete a task, verify information, or prevent entry of inappropriate information. After data has been collected, its quality may be improved via the process of data cleansing or data scrubbing so that it will be accurate enough to support analysis. These terms are used interchangeably to refer to removing incor- rect, incomplete, duplicate, or improperly formatted items using special software designated for this purpose.

Quality Improvement Quality improvement is a scientific approach to the analysis of performance and ways to improve it (Wilson, 2016). Quality improvement is built upon the following principles:

• Commitment to quality and collaborative efforts. The organization must demonstrate clear and consistent dedication to quality from its mission statement all the way through its policies and actions.

• Quality must be measurable, and measurability allows one to determine if change resulted in improvement.

• Systems thinking, which focuses on processes and the improvement of processes.

• Quality is ongoing and results from rigorously repeated efforts.

M03_HEBD1010_06_SE_C03.indd 45 3/8/18 10:59 AM

46 Chapter 3

The following items, while far from inclusive, provide some specific examples of data that are tracked for the purpose of improved quality:

• The Consumer Assessment of Healthcare Providers and Systems (CAHPS®). This survey instrument collects data on patients’ perceptions of their care, permitting comparison across settings, providers, and financial incentives in the form of increased or decreased Medicare reimbursement for hospitals to improve the quality of care provided (Centers for Medicare and Medicaid Services [CMS], 2017). Results are public.

• Patient falls. Hospitals voluntarily submit patient fall data to the national database of nursing quality indicators (NDNQI), a database created by the American Nurses Asso- ciation. Hospitals can then compare their fall rates against other hospitals of similar type and size (Mennella & Holle, 2016).

• 30-day readmission rates. The hospital readmissions reduction program provides finan- cial incentives to hospitals to reduce patient readmissions within 30 days for Medicare beneficiaries (Mennella & Key, 2016). This is the reason why organizations collect infor- mation on their patients with acute myocardial infarction, heart failure, pneumonia, chronic obstructive pulmonary disease, and elective hip and/or total knee replacement to determine ways that they can improve both patients’ outcomes and the organization’s subsequent reimbursement.

It should be noted that the concept of quality management is not realized until data collected is used to support decisions for the purpose of improvement (Rivenbark, Roenigk, & Fasiello, 2017). The Patient Protection and Affordable Care Act mandates the creation of a national strategy for quality, and more efforts in this area can be expected (Smith, 2013).

Data Management Data management is the process of controlling the collection, storage, retrieval, and use of data to optimize accuracy and utility while safeguarding integrity. Efficient and effective data management optimizes the value of the data for informed decision-making (Shankarana- rayanan, Even, & Berger, 2015). Good data management requires thorough planning (Wills, 2014). On the organizational level, it is critical to consider what information is needed as well as the tools and resources required to manage it and realize its value. This planning should start with the organization’s strategic plan. It should be noted. However, that it may not be possible to anticipate every future need.

Several levels of personnel are involved in data management. Personnel at the point of data entry include employees and, in some cases, clients. System analysts help the users to specify the data that are to be collected and how data collection will be accomplished. Programmers create the computer instructions, or program, that will collect the required data. They also build databases, the file structure that supports the storage of data in an organized fashion and allows data retrieval as meaningful information. Some facilities also employ database administrators, who are responsible for overseeing all activities related to maintaining the database and optimizing its use. Another common strategy used within organizations is the data warehouse. A data warehouse is a repository for storing data from several different databases so that it can be combined and manipulated as needed and to provide answers to various queries (Shankaranarayanan, Even, & Berger, 2015).

Costs and benefits are considerations in the management of data. Organizations must invest in storage systems, software, and personnel to derive the optimal benefit from analyz- ing data that they manage. Data management is cost- and labor-intensive but must be viewed as an investment needed to yield high-quality data.

M03_HEBD1010_06_SE_C03.indd 46 3/8/18 10:59 AM

Effective and Ethical Use of Data and Information 47

Data Types and Formats Data management is complicated by the fact that data comes in different forms. Data may be raw or processed or come in unstructured or structured formats. Unstructured data include documents such as consults, emails, and multimedia resources. Structured data are typically organized into a repository or database for effective processing. An example of structured data can be seen with a pick list on a nursing documentation screen where the user is forced to select an option. Structured data can be used to generate reports while unstructured data do not lend themselves to such quick analysis. While the health- care industry still has some paper documents that it maintains, this situation is becoming uncommon. The widespread conversion of data and information to electronic format so that it can be accessed, processed, stored, or transmitted via the use of computer technol- ogy is known as digitization. Digitization increases the amount of information available electronically.

Data Governance Data Governance is the term used to refer to the collection of policies, standards, pro- cesses, and controls applied to an organization’s data to ensure that it is available to appro- priate persons when and where it is needed, in the format that is needed, and is otherwise properly secured (Dutta, 2016). Data governance is an extremely important part of data management. Data governance may seem fairly straightforward—when one organization or healthcare delivery system is involved—but becomes more complicated in the presence of HIE and other, larger efforts to share data for big data purposes. At the local level, data governance establishes the screens and data that each user class is able to access, config- ures the view for each user class, has oversight for the creation, dispersal, and revocation of individual user names and passwords to log on to the system, and drafts and enforces access policies. With HIE, traditional governance issues include privacy and security of data, liability for inappropriate disclosure, and possible unfair market advantages (Allen et al., 2014). Data-sharing agreements (DSAs) among exchange partners spell out respon- sibilities and meet legal requirements.

Big Data, Data Analytics, and Data Modeling The term big data refers to very large data sets that are beyond human capability to analyze or manage without the aid of technology. These large data sets are then used to reveal pat- terns and discover new learning. The availability of large data sets, decreased storage costs, and increased computing power support the big data phenomenon (Tattersall & Grant, 2016). Although the arrival of big data in healthcare lags behind its use in other industries, it is nonetheless of vital importance due to the increasing complexity of healthcare and its need for informed decision-making (Tharmalingam, Hagens, & Zelmer, 2016; Wills, 2014). Big data includes data of different types, levels of complexity, and formats (structured, unstructured, and semi-structured), as well as processed and unprocessed items from sev- eral sources that is then analyzed for patterns (Jukić, Sharma, Nestorov, & Jukić, 2015; Manerikar, 2016).

The process of examining big data for patterns is known as data mining, and sometimes the term analytics is used interchangeably. Data mining is a process that uses software to uncover relationships within large data sets via the use of artificial intelligence, statistical

M03_HEBD1010_06_SE_C03.indd 47 3/8/18 10:59 AM

48 Chapter 3

computation, and computer technology (Brown & White, 2017). Data mining has been used in marketing and politics to determine buying and voting trends within society. It has even been used to discover financial fraud (Albashrawi, 2016) and has only recently been added to the repertoire of healthcare industry tools to determine a variety of outcomes. Analytics actually goes beyond the discovery of patterns in data as it systematically uses data and insights from models that it incorporates to offer solutions and drive decisions (Wills, 2014).

Analytics is seen in three forms: small data, predictive modeling, and real-time analytics (Wills, 2014). Small data refers to limited data sets such as that seen with EHR information for a select patient population at a single hospital or healthcare delivery system. A specific example might include using EHRs to determine the patients admitted with congestive heart failure for a specific timeframe. Small data is ideal to report benchmarks and can be used very effectively for case-management purposes. Costs and expertise needed to run and use small data are nominal; needed resources are already in place at most facilities (Wills, 2014). Analysis of small data most typically requires the presence of a data repository, staff training, and possibly some changes to existing workflows.

Predictive modeling, also known as predictive analytics, uses past and current data to forecast the likelihood of an event (Kakad, Rozenblum, & Bates, 2017; Wills, 2014). In healthcare, predictive analytics can use medical information derived from EHRs to evaluate health risks for patients, the likelihood that they will utilize services in the future, or predict who is at risk for complications. In one specific example, Vesely (2017) detailed the use of this type of tool to identify which patients were at high-risk for central-line infections, so that that active measures could be employed to prevent infections before they occurred. In addition to improving outcomes, predictive analytics can help eliminate waste (Kakad, Rozenblum, & Bates, 2017). Despite the potential to improve the efficiency and effectiveness of healthcare delivery, adoption of predictive analytics by healthcare organizations has been slow.

Real-time analytics (RTA) examines current data in real-time. RTA is unfettered by the time lag associated with the use of historical data, which may no longer apply and can nega- tively impact decisions (Dobrev & Hart, 2015). RTA allows a move from a reactive to proactive stance and can foster both learning and predictions in administrative and clinical areas. RTA at the point of care use data available through device integration comparing it against data from the EHR, registries, and other information systems and databases, to present immediate, actionable information to clinicians. Examples of actionable information would include alerts of possible drug interactions or complications, and suggested interventions. RTA requires integration of systems, a data repository, a master data management environment, archi- tecture that supports data creation, integration, interception for analysis, a mature business intelligence (historical data helps to provide context and meaning) and data warehouse, the ability to configure and re-engineer processes, information-technology expertise with subject matter/business knowledge, rule definition, established goals and requirements, and deci- sions upon whether to build or contract for RTA services.

Business intelligence (BI) is another term used when discussions of best use of data arise. BI is the integration of data from different sources for the purpose of optimizing its use and understanding (Pinto & Fox, 2016). BI refers to a strategy, processes, and a tool set (Obeidat, North, Richardson, Rattanak, & North, 2015). That is to say that analytics can be, and frequently are, a part of business intelligence. BI is a very important part of healthcare delivery today but is not the primary focus of this chapter.

The knowledge gleaned from large data sets and big data is sometimes referred to as knowledge discovery in databases (KDD). KDD can be defined as a process of an iterative sequence that entails the following steps: understanding the domain; understanding the data used in the domain; data preparation that handles missing values or removes redundant or

M03_HEBD1010_06_SE_C03.indd 48 3/8/18 10:59 AM

Effective and Ethical Use of Data and Information 49

irrelevant data; applying methods to extract data (namely data mining); and finally, data pre- sentation (Afshar, Ahmadi, Roudbari, & Sadoughi, 2015). Clinical databases hold huge amounts of information about patients and their medical conditions. The potential to discern patterns and relationships within those databases that would contribute to new knowledge was recog- nized some time ago, but until recently, discovery of useful information was hampered by a lack of tools to reveal it. Clinical repositories are now available for research and utilization purposes.

A foundational concept to useful data and information is data modeling. Data modeling is a process to define and analyze data requirements to support processes required within an organization. Data modeling is an important step in the design of a database such as an EHR, because it establishes what information must be collected and in what format. On a larger scale, data modeling provides a platform for BI and data analysis and can help to address the gap between data that are available and information that is required ( LaMacchia & Egan, 2017). Data modeling also supports exchange and re-use of data (Goossen & Goossen- Baremans, 2013).

Challenges in Finding and Using Big Data in Healthcare The challenges to finding and using big data in healthcare are many. Among the most notable are:

• Incentives to share data. Without data sharing, the amount of big data is limited, which minimizes its value. When addressing the Zika outbreak, Littler et al. (2017) noted that there were limited incentives for researchers and responders to share data. Reluctance to share may stem from concerns over intellectual property rights or attribution issues.

• Proprietary issues. In the United States, the rivalry among healthcare vendors historically made it difficult to readily share data resident on competing products. And competing healthcare systems have been loathe to share patient information for fear that it would afford their rivals an unfair advantage. These types of issues limit the amount of data shared and, consequently, the amount of big data. Data has value and can be sold for other uses. HIEs sell secondary data. The American Medical Association and US Centers for Medicare and Medicare also sell provider data (Kaplan, 2015).

• Lack of appropriate infrastructure. Creating the infrastructure to share data and support big data is quite involved. First, a governance structure must be created that provides a bal- ance between privacy and access while complying with state, national, and international ethical and legal requirements (Littler et al., 2017; Moorthy, Roth, Olliaro, Dye, & Kieny, 2016). Terms for data use must be clear. It is only after governance issues are addressed that the data repository, special software, and experts can follow.

• Data quality. Wills (2014) noted that much healthcare data is available but not enough has sufficient applicable information accompanying it. Presumably that statement refers to metadata. Metadata is defined as the data that provides information about how, when, and by whom data are collected, formatted, and stored. Without metadata and data dictionaries, the correct or meaningful re-use of information cannot occur. More work is needed to develop data-sharing platforms that can standardize, clean, and curate data into usable forms (Merson, Gaye, & Guerin, 2016).

• Culture. Organizations and their leaders need to adopt new processes.

• Costs. Investment in technology, including the infrastructure and technology required for the aggregation and analysis of big data by healthcare, has been limited with some notable exceptions (Wills, 2014). Not all healthcare systems have the same resources, and costs can be difficult to justify when balanced against many competing needs (Dobrev & Hart, 2015).

M03_HEBD1010_06_SE_C03.indd 49 3/8/18 10:59 AM

50 Chapter 3

• Complexity of healthcare. The nature of healthcare has limited the ability to incorpo- rate the same level of sophistication in analytic tools found in other industries (Wills, 2014).

• Insufficient expertise. Big data presents a steep learning curve for administrators and clinicians, and the data scientists needed to help them understand big data are highly sought-after, making them scarce commodities in the healthcare sector ( Gelinas, 2016).

• A lack of nursing visibility. Gelinas (2016) noted there is a danger that decisions will be made without nursing representation, because there are no posted big data positions for nurses and nurse informaticists. Nurses and nurse informaticists are needed to com- municate clinician needs to data scientists, and nurses must work with data scientists to advance both nursing knowledge and practice using big data. Another visibility issue for nursing relates to the low levels of adoption of standardized nursing language in EHRs, leaving nursing with limited measures of its clinical accomplishments.

• Big promises but limited progress. The potential of big data in healthcare has barely been tapped. There are inequities in the ability to support big data, and gaps in pre-requisite knowledge and skill-sets among administrators and clinicians, that currently hinder the best use of big data.

Information and Knowledge Management Good information management ensures access to the right information at the right time to the people who need it. Vast amounts of information are produced daily. This information may or may not be readily available when it is needed. Its volume exceeds the processing capacity of any single human being. Part of good information management ensures that care provid- ers have the resources that they need to provide safe, efficient, quality care. Some examples of these resources include clinical guidelines, standards of practice, policy and procedure manuals, research findings, drug databases, and information on community resources. IT can help to ensure access to the most recent versions of these types of resources via tools such as intranets and electronic communities. This type of version control within an organization eliminates the uncertainties of what may or may not be available in various locations, and whether or not it is the most recent version. Good information management also eliminates redundant data collection, which wastes resources. In the era of big data, good information management is more important than ever before.

Although the terms information management and knowledge management are some- times used interchangeably, the concepts are different. Knowledge management (KM) refers to the process of selectively applying knowledge gained from previous experiences and decision-making to current and future situations for the express purpose of improved effectiveness (Karlinsky-Shichor & Zviran, 2016). Knowledge management systems are sets of information systems that enable organizations to tap into the knowledge, experiences, and creativity of their staff to improve performance (Karlinsky-Shichor & Zviran, 2016). KM is a structured process for the generation, storage, distribution, and application of both tacit knowledge (personal experience) and explicit knowledge (evidence) in organizations. Knowl- edge is a valued commodity and one that can provide a competitive edge. While many orga- nizations collect and store vast amounts of data, not all are equally successful in discovering hidden knowledge in that data (Dastyar, Kazemnejad, Sereshgi, & Jabalameli, 2017). Data mining can provide a valuable asset to knowledge management.

M03_HEBD1010_06_SE_C03.indd 50 3/8/18 10:59 AM

Effective and Ethical Use of Data and Information 51

Effective KM in an organization requires the presence of the following infrastructure elements: human, process, and IT (Dastyar et al., 2017). As to the human infrastructure, there must be an understanding of foundational concepts such as data and information, and a process to support individual knowledge becoming group knowledge. Process infrastructure includes practices, regulations and laws. And lastly, IT infrastructure would include a network, a data warehouse, individual databases, and data mining tools.

Using Analytics to Support Healthcare Delivery Analytics can support healthcare delivery day-to-day operations and clinical care of health- care delivery systems. As one example at the organizational level, predictive analytics can help determine what services the facility should offer, helping to maintain the bottom line while meeting healthcare-consumer needs (Drell & Davis, 2014). Real-time analytic tools can offer significant and measurable improvements, help organizations remain competitive, and, in the long run, drive strategic business objectives from a grass roots level (Dobrev & Hart, 2015). Many of the larger healthcare delivery systems have been using analytics to improve operations and improve patient care (Wills, 2014).

On the clinical side of operations, big data and analytics provide tools to help deliver care more effectively, efficiently, and at lower cost (Bates, Saria, Ohno-Machado, Shah, & Escobar, 2014). Furthermore, big data findings are a form of evidence used to supplement traditional research findings, or as a source of evidence on their own (de Lusignan, Crawford, & Munro, 2015; Kennedy, 2016).

On a larger scale, big data and analytics can also uncover new learning and evidence to improve patient outcomes and population health. If that sounds familiar, it should be, as that intent precipitated the American Recovery and Reinvestment Act of 2009 that provided financial incentives to providers to adopt electronic health records so that data could be col- lected and shared electronically for analysis and subsequent learning to improve healthcare. This legislation arose from a health policy that helped to establish a framework for big data. Big data and the resulting evidence can then be used to inform policy makers, who in turn make decisions that impact funding and delivery of services.

Clinician Roles in Using Big Data and Analytics Clinicians need to understand the relationship between big data and evidence-informed practice (Brennan & Bakken, 2015). They also need to have a voice in the selection and use of tools, such as real-time analytics at the bedside, and predictive analytics, to ensure that the tools provide value for clinicians and patients. Both activities require the acquisition of new knowledge and skills, although nurses start with a good foundation given their theoretical background, patient-centered focus, basic understanding of standards, standardized lan- guages, and research as a source for evidence-based decisions. Even as many nurses struggle to grasp the concepts of nursing informatics, now in addition they must learn about big data. Data science is the systematic study of digital data (National Consortium for Data Science, 2017, Para. 2)—analytics, business intelligence, knowledge management, and discovery infor- matics so that they have context for the time and place in which they practice. Discovery informatics uses scientific models and theories to create computer-based discovery of new learning in big data, replacing human cognition with the idea that discovery and learning can be accelerated (Honavar, 2014).

M03_HEBD1010_06_SE_C03.indd 51 3/8/18 10:59 AM

52 Chapter 3

Ethical Concerns with Data and Information Use Rapid developments with data sharing, new models for research, secondary use, and big data enable opportunities to improve health and healthcare, yet exacerbate some concerns (Kaplan, 2016), and give rise to new ones, which are briefly addressed here:

Ownership of Patient Data. Ownership of patient data is not clearly addressed from a legal perspective either in the US or abroad (Kaplan, 2016).

Data sharing. There are numerous questions here that include control and implications of when data is shared. Participants in a summit on aligning incentives for data sharing wanted their data quickly so that others could benefit (Haug, 2017). Study participants also wanted results shared directly with them along with explanation of what results meant, a change from current practices. Also, in treatment trials, early sharing may bias results.

The meaning of informed consent. This has several aspects. One is that some patients believe it to mean that they will receive the best treatment in a research trial. There are also differences across the US and Europe in what consent conveys (Haug, 2017). And yet another issue occurs with data mining and biobanking, because consent implies awareness and choice—neither of which are true in this instance (Al-Saggaf, 2015; Meir, Cohen, Mee, & Gaffney, 2014). And finally, big data creates a shift with research so that the informed consent relationship is no longer with a person or research institution.

Secondary use of data. De-identified, aggregate data is commonly sold for purposes other than the original reason for collection. This can pose problems because ownership is not well-defined (Kaplan, 2016). Secondary data has been used to target persons for marketing purposes even though it should be de-identified.

Privacy versus confidentiality. Research findings, biobanks, and big data are typically de- identified, or kept confidential, but there are occasions when personal information can be identified and information released, causing harms that include discrimination in obtaining credit, insurance, housing, or employment, social stigma, and even reuse of DNA collected for research for criminal profiling (Dove 2015; Kaplan, 2016).

Future Directions The amount of data and information produced within healthcare will continue to grow, par- ticularly as new data sources and models of sharing such as biobanks evolve, and data streams from wearable and consumer devices are incorporated. As the amount of data and information increases we look forward to a commensurate increase in knowledge and wisdom created with the use of big data, data science, and the tools supporting big science. Along with that knowledge and wisdom, there will be major changes in the way we diagnose and treat patients.

Informatics professionals, and informatics nurses in particular, must be active participants in advocating for consumers and infusing their knowledge, skills, and experience into the analysis of big data (Booth, 2016). The transformation of healthcare requires evidence, plus the infrastructure provided by informatics, to support knowledge discovery and dissemination gained through effective use of data and information (Delaney, Kuziemsky, & Brandt, 2015). At the same time, the INS must never lose sight of the need to collaborate interprofessionally to achieve this transformation, while working to decrease errors and promote safety.

M03_HEBD1010_06_SE_C03.indd 52 3/8/18 10:59 AM

Effective and Ethical Use of Data and Information 53

Summary • A discussion of effective and ethical use of data and information requires definition

of the concepts of data, information, knowledge, and wisdom, as well as a discussion of responsibilities delineated in professional codes of ethics and practice standards for informatics nursing.

• Healthcare data has long resided in a series of separate silos with limited sharing and benefits to the larger community.

• Sharing data is an ethical and scientific imperative because it can bring great good to the many, yet no common ethical and legal framework exists that will connect health- care providers and data in EHRs with research findings, regulators, collaboratives, and various other databases.

• Data is a strategic resource that can be used to track day-to-day operations, patient outcomes and services, and more.

• Quality improvement and quality management, requires data that is complete, reli- able, without error, and reliable (data quality).

• Data quality can be fostered through the use of computer-system checks that remind users to complete a task, verify information, or that prohibit entry of inappropriate information.

• Data scrubbing, or cleansing, is a process that improves data quality to improve analysis.

• Healthcare follows multiple metrics to determine if improvements have occurred. Some examples include patient satisfaction, patient outcomes, and readmissions.

• Data management is the process of controlling the collection, storage, retrieval, and use of data to optimize accuracy and utility, while safeguarding integrity in the pro- cess of controlling the collection, storage, retrieval, and use of data to optimize accu- racy and utility.

• Data management is complicated by the various formats that data come in. • Digitization is the widespread conversion of data and information to electronic for-

mat so that it can be accessed, processed, stored, or transmitted via the use of com- puter technology.

• Data governance is the collection of policies, standards, processes and controls applied to an organization’s data to ensure that it is available when, where, and by who it is needed, in the format that is needed, and is properly secured.

• Big data is the term used to refer to very large data sets (that are beyond human capa- bility to analyze or manage without the aid of technology) that are now being exam- ined for patterns that can be used to drive decisions.

• Data mining is the process that uses software to uncover relationships within large data sets via the use of artificial intelligence, statistical computation, and computer technology.

• Analytics uses data and insights from models that it incorporates to offer solutions and drive decisions.

• Predictive modeling, or predictive analytics, use past and current data to forecast the likelihood of an event.

• Real-time analytics (RTA) examines current data in real-time. • Business intelligence (BI) refers to the strategy, processes, and tool set that inte-

grate data from different sources for the purpose of optimizing its use and understanding.

M03_HEBD1010_06_SE_C03.indd 53 3/8/18 10:59 AM

54 Chapter 3

• The knowledge gleaned from large data sets and big data is sometimes referred to as knowledge discovery in databases.

• Data modeling is a process to define and analyze data requirements to support pro- cesses required within an organization.

• Challenges to big data use include: limited incentives to share data; proprietary issues; lack of infrastructure; uneven data quality; organizational culture; costs; the complexity of healthcare; limited available expertise; limited nursing visibility; and limited progress toward creation and support.

• Knowledge management is the process of applying knowledge gained from experience to current and future situations for the express purpose of improved effectiveness.

• Analytics can bring value to healthcare delivery. • Clinicians need to acquire knowledge and skills to use big data. • Unresolved ethical issues related to data and information use include unanswered

questions of ownership of patient data and secondary use, control with data sharing, clarifying “informed consent,” and patient harms related to disclosure of information.

• The amount and types of data will continue to grow, providing new opportunities for learning as well as new challenges.

• Nurses, and informatics nurses in particular, must take an active role with all things related to effective and ethical use of data and information.

About the Authors Toni Hebda teaches graduate-level informatics courses at Chamberlain College of Nursing. She graduated from Washington Hospital, earned her BSN from Duquesne University, and her MNEd, PhD, and MSIS from the University of Pittsburgh. She has taught in formal nursing programs, staff development, and instructed hospital staff in the use of information systems. Kathleen (Kathy) Hunter graduated from Church Home & Hospital and served with the Army Nurse Corps. She earned her BSN, MS in nursing, and PHD at the University of Mary- land. Nursing informatics is her area of practice. Dr. Hunter has taught online for several years. Her contributions to nursing informatics include starting the MSN informatics track at Chamberlain College, leadership roles, and research on nursing informatics competencies. Dr. Hunter is a Professor with the Chamberlain MSN Program and has been recognized as an American Academy of Nursing Fellow.

References Afshar, H. L., Ahmadi, M., Roudbari, M., & Sadoughi, F. (2015). Prediction of breast cancer

survival through knowledge discovery in databases. Global Journal of Health Science, 7(4), 392–398. doi:10.5539/gjhs.v7n4p392

Case Study

You have been asked to speak to senior nursing students enrolled in a nursing infor- matics class at the local university on the implications of big data for healthcare deliv- ery and nursing. You are trying to condense your presentation to ten key points—what would they be and what is your rationale for their selection?

M03_HEBD1010_06_SE_C03.indd 54 3/8/18 10:59 AM

Effective and Ethical Use of Data and Information 55

Albashrawi, M. (2016). Detecting financial fraud using data mining techniques: A decade review from 2004 to 2015. Journal of Data Science, 14(3), 553–569.

Allen, C., Des Jardins, T. R., Heider, A., Lyman, K. A., McWilliams, L., Rein, A. L., . . . Turske, S. A. (2014). Data governance and data sharing agreements for community- wide health information exchange: Lessons from the Beacon communities. EGEMS. (Generating Evidence & Methods to Improve Patient Outcomes, 2(1), 1–9. doi:10.13063/2327-9214.1057

Al-Saggaf, Y. (2015). The use of data mining by private health insurance companies and customers’ privacy. Cambridge Quarterly of Healthcare Ethics, 24, 281–292.

American Nurses Association. (2015a). Nursing informatics: Scope and standards of practice (2nd ed.). Silver Spring, MD: Author.

American Nurses Association. (2015b). Code of ethics for nurses with interpretative statements. Retrieved from nursingworld.org

Bailey, J., Wan, J., Mabry, L., Landy, S., Pope, R., Waters, T., & Frisse, M. (2013). Does health information exchange reduce unnecessary neuroimaging and improve quality of headache care in the emergency department? Journal of General Internal Medicine, 28(2), 176–183. doi:10.1007/s11606-012-2092-7

Bates, D. W., Saria, S., Ohno-Machado, L., Shah, A., & Escobar, G. (2014). Big data in health care: Using analytics to identify and manage high-risk and high-cost patients. Health Affairs, 33(7), 1123–1131.

Bauchner, H., Golub, R. M., & Fontanarosa, P. B. (2016). Data sharing: An ethical and scientific imperative. The Journal of the American Medical Association, 315(12), 1237–1239.

Booth, R. G. (2016). Informatics and nursing in a post-nursing informatics world: Future directions for nurses in an automated, artificially intelligent, social-networked healthcare environment. Nursing Leadership (Toronto, Ont.), 28(4), 61–69.

Brennan, P. F., & Bakken, S. (2015). Nursing needs big data and big data needs nursing. Journal of Nursing Scholarship, 47(5), 477–484.

Brown, G. E., & White, E. D. (2017). An investigation of nonparametric data mining techniques for acquisition cost estimating. Defense Acquisition Research Journal: A Publication of The Defense Acquisition University, 24(2), 302–332. doi:10.22594/ dau.16756.24.02

Centers for Medicare & Medicaid Services. (Revised 06/20/2017). Consumer Assessment of Healthcare Providers & Systems (CAHPS). Retrieved from www.cms.gov/ Research-Statistics-Data-and-Systems/Research/CAHPS/

Chen, H., Hailey, D., Wang, N., & Yu, P. (2014). A review of data quality assessment methods for public health information systems. International Journal of Environmental Research & Public Health, 11(5), 5170–5207. doi:10.3390/ijerph110505170

Davidson, A. J. (2015). Creating value: Unifying silos into public health business intelligence. Frontiers in Public Health Services & Systems Research, 4(2), 1–13. doi:10.13063/2327-9214.1172

Dastyar, B., Kazemnejad, H., Sereshgi, A. A., & Jabalameli, M. A. (2017). Using data mining techniques to develop knowledge management in organizations: A review. Journal of Engineering, Project, and Production Management, 7(2), 80–89.

Delaney, C. W., Kuziemsky, C., & Brandt, B. F. (2015). Integrating informatics and interprofessional education and practice to drive healthcare transformation. Journal of Interprofessional Care, 29(6), 527–529.

de Lusignan, S., Crawford, L., & Munro, N. (2015). Creating and using real-world evidence to answer questions about clinical effectiveness. Journal of Innovation in Health Informatics, 22(3), 368–373.

M03_HEBD1010_06_SE_C03.indd 55 3/8/18 10:59 AM

http://nursingworld.org/
56 Chapter 3

Dobrev, K., & Hart, M. (2015). Benefits, justification and implementation planning of real- time business intelligence systems. Electronic Journal of Information Systems Evaluation, 18(2), 105–119.

Dove, E. S. (2015). Biobanks, data sharing, and the drive for a global privacy governance framework. The Journal of Law, Medicine & Ethics: A Journal of The American Society of Law, Medicine & Ethics, 43(4), 675-689. doi:10.1111/jlme.12311

Drell, L., & Davis, J. (2014). Getting started with predictive analytics. Marketing Health Services, 34(3), 22–27.

Dutta, A. (2016). Ensuring the quality of data in motion: The missing link in data governance. Computer Weekly, 1–4.

Gelinas, L. (2016). Big data: Big roles in big demand for nurses. American Nurse Today, 11(2), 1–1. Goossen, W., & Goossen-Baremans, A. (2013). Clinical professional governance for

detailed clinical models. Studies in Health Technology & Informatics, 193, 231–260. doi:10.3233/978-1-61499-291-2-231

Haug, C. J. (2017). Whose data are they anyway? Can a patient perspective advance the data-sharing debate? The New England Journal of Medicine, 376(23), 2203–2205. doi:10.1056/NEJMp1704485

Honavar, V. G. (2014). The promise and potential of big data: A case for discovery informatics. Review of Policy Research, 31(4), 326–330.

International Medical Informatics Association (IMIA). (2016). The IMIA code of ethics for health information professionals. Retrieved from http://imia-medinfo.org/wp/ wp-content/uploads/2015/07/IMIA-Code-of-Ethics-2016.pdf

Jukić, N., Sharma, A., Nestorov, S., & Jukić, B. (2015). Augmenting data warehouses with big data. Information Systems Management, 32(3), 200–209.

Kakad, M., Rozenblum, R., & Bates, D. W. (2017). Getting buy-in for predictive analytics in health care. Harvard Business Review Digital Articles, 2–5.

Kaplan, B. (2015). Selling health data: de-identification, privacy, and speech. Cambridge Quarterly of Healthcare Ethics, 24(3), 256–271.

Kaplan, B. (2016). How should health data be used? Cambridge Quarterly of Healthcare Ethics, 25(2), 312–329.

Karlinsky-Shichor, Y., & Zviran, M. (2016). Factors influencing perceived benefits and user satisfaction in knowledge management systems. Information Systems Management, 33(1), 55–73.

Kennedy, M. A. (2016). Adaptive practice: Next generation evidence-based practice in digital environments. Studies in Health Technology and Informatics, 225, 417–421.

Knoppers, B. M., Harris, J. R., Budin-ljøsne, I., & Dove, E. S. (2014). A human rights approach to an international code of conduct for genomic and clinical data sharing. Human Genetics, 133(7), 895–903.

Kuehn, B. M. (2014). IOM outlines framework for clinical data sharing, solicits input. The Journal of the American Medical Association, 30(7), 665.

LaMacchia, C., & Egan, W. (2017). Smart data modeling. Proceedings for the Northeast Region Decision Sciences Institute (NEDSI), 466.

Leventhal, R., & Hagland, M. (2017). Healthcare’s latest interoperability push. Healthcare Informatics, 34(1), 35–37.

Littler, K., Wee-Ming, B., Carson, G., Depoortere, E., Mathewson, S., Mietchen, D., . . . Segovia, C. (2017). Progress in promoting data sharing in public health emergencies. Bulletin of the World Health Organization, 95(4), 243–244. doi:10.2471/BLT.17.192096

Manerikar, S. (2016). Big data. Aweshkar Research Journal, 21(2), 95.

M03_HEBD1010_06_SE_C03.indd 56 3/8/18 10:59 AM

http://imiamedinfo.org/wp/wp%E2%80%90content/uploads/2015/07/IMIA%E2%80%90Code%E2%80%90of%E2%80%90Ethics%E2%80%902016.pdf
http://imiamedinfo.org/wp/wp%E2%80%90content/uploads/2015/07/IMIA%E2%80%90Code%E2%80%90of%E2%80%90Ethics%E2%80%902016.pdf
Effective and Ethical Use of Data and Information 57

Mennella, H. A., & Holle, M. O. (2016). Falls, accidental: Incident reports—risk management. CINAHL Nursing Guide. Glendale, CA: EBSCO.

Mennella, H. A., & Key, M. C. (2016). Case management: Readmissions. CINAHL Nursing Guide. Glendale, CA: EBSCO.

Meir, K., Cohen, Y., Mee, B., & Gaffney, E. F. (2014). Biobank networking for dissemination of data and resources: An overview. Journal of Biorepository Science for Applied Medicine, 229–242. doi:10.2147/BSAM.S46577.

Merson, L., Gaye, O., & Guerin, P. J. (2016). Avoiding data dumpsters—Toward equitable and useful data sharing, The New England Journal of Medicine, 374(25), 2414–2415.

Moorthy, V. S., Roth, C., Olliaro, P., Dye, C., & Kieny, M. P. (2016). Best practices for sharing information through data platforms: Establishing the principles. Bulletin of the World Health Organization, 94(4), 234, 234A.

National Consortium for Data Science. (2017). About the National Consortium for Data Science. Retrieved from http://datascienceconsortium.org/about/

Obeidat, M., North, M., Richardson, R., Rattanak, V., & North, S. (2015). Business intelligence technology, applications, and trends. International Management Review, 11(2), 47–56.

Otto, B. (2015). Quality and value of the data resource in large enterprises. Information Systems Management, 32(3), 234–251. doi:10.1080/10580530.2015.1044344

Pinto, B., & Fox, B. I. (2016). Clinical and Business Intelligence: Why it’s important to your pharmacy. Hospital Pharmacy, 51(7), 604. doi:10.1310/hpj5107-604

Rivenbark, W. C., Roenigk, D. J., & Fasiello, R. (2017). Twenty years of benchmarking in North Carolina: Lessons learned from comparison of performance statistics as benchmarks. Public Administration Quarterly, 41(1), 130–148.

Shankaranarayanan, G., Even, A., & Berger, P. D. (2015). Optimizing data management with disparities in data value. Journal of International Technology & Information Management, 24(3), 1–24.

Smith, H. L. (2013). Public reporting of Medicare quality data. PT in Motion, 5(10), 44–47. Tattersall, A., & Grant, M. J. (2016). Big Data—What is it and why it matters. Health

Information and Libraries Journal, 33(2), 89–91. doi:10.1111/hir.12147 Tharmalingam, S., Hagens, S., & Zelmer, J. (2016). The value of connected health

information: perceptions of electronic health record users in Canada. BMC Medical Informatics & Decision Making, 161–169. doi:10.1186/s12911-016-0330-3

Using data for quality management and care collaboration: PART 1. (2016). Long-Term Living: For the Continuing Care Professional, 65(4), 54–55.

Vesely, R. (2017). Predictive analytics: IU health knows the patient in room 103 is at high- risk for CLABSI. Would you? H & HN: Hospitals & Health Networks, 91(2), 20–25.

Wills, M. J. (2014). Decisions through data: Analytics in healthcare. Journal of Healthcare Management, 59(4), 254–262.

Wilson, D. M. (2016). Quality model for improvement: Plan-do-study-act. CINAHL Nursing Guide, Nursing Reference Center Plus. Accessed Februaury 4, 2017