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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
The Relevance of Informatics for Healthcare 3
Creating an Informatics Culture 8
Caring for the Patient Not the Computer 12
Future Directions 13
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
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
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
Using Information Technology to Organize and Use Information Effectively 68
Future Directions 70
5 Using Informatics to Support Evidence-Based Practice and Research 73 Melody Rose
Levels of Evidence 75
Applying Information Literacy to Find the Highest Levels of Evidence 77
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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
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
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
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
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
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
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
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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
14 Information Networks and Information Exchange 271 Jane M. Brokel
Health Information Network Models 272
Clinical Data Networks or Health Information Networks 273
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
Future Directions 286
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
11 System Implementation, Maintenance, and Evaluation 191 Sue Evans
System Implementation 192
System Installation 203
System Evaluation 206
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
13 Information Security and Confidentiality 238 Ami Bhatt, Patricia Mulberger
Privacy, Confidentiality, Security, and Consent 239
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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
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
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
18 Consumer Health Informatics 370 Melody Rose, Toni Hebda
Driving Forces 372
Consumer Health Informatics Applications 377
The Role of the Informatics Nurse with CHI 385
The Future of CHI 388
19 Connected Healthcare (Telehealth and Technology-Enabled Healthcare) 398 Lisa Eisele
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
20 Public Health Informatics 418 Marisa L. Wilson
Exploring Public Heath 419
Public Health Mandate 419
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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
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
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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
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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
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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.
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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.
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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.
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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.
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.
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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
As you continue reading remember that our top and qualified writers are here to help with any of your assignment. All you need to do is place an order with us.
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
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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.
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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
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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
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An Overview of Informatics in Healthcare Jennifer A. Brown, MSN, RN, HNB-BC Taryn Hill, PhD, RN Toni Hebda, PhD, RN-C
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
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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.
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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
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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.
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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,
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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.
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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
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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
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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,
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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).
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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
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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
O�ce of public a�airs and communications
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O�ce of the National Coordinator
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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
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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.
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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
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
Clinical decision support
Public health policy
Practice Population Public
HIT for quality and safety in care
Population health management and regional
Big data and analytics
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• 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.
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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.
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Informatics Theory and Practice Maxim Topaz, PhD, MA, RN
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
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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
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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);
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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
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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.
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
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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.
Personal Factors (Nurse/Patient/Family/Provider)
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
Setting Type Setting Culture Nurse Familiarity with Setting Collaborative Team Electronic System Information Decision Support System
Setting Related Factors
Evaluation Information Processing
Person Related Factors
General Wisdom in Action
Personal Wisdom in Action (post situation)
Stressful or Uncertain Situation
Discovery of Meaning
Formulation (in context)
Insight and Intuition
l In 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.
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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
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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 dierence (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
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).
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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
Beyond study scope
Interpreting and Understanding Knowledge on
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
Transitions Theory will assist:
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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
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.
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Theory/Science Key Ideas
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
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
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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
Molecules, cells, tissues, organs Patients, individuals, populations, societies
Health informatics (HI): clinical informatics
and public health informatics
Informatics in translational science: translational bioinformatics (TBI) and clinical
research informatics (CRI)
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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
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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.
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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
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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).
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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).
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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,
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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).
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Effective and Ethical Use of Data and Information Toni Hebda, PhD, RN-C Kathleen Hunter, PhD, RN-BC, CNE
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
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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.
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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
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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.
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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.
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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
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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
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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).
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• 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.
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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).
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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.
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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.
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• 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.
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Electronic Resources for Healthcare Professionals Brenda Kulhanek, PhD, MSN, MS, RN-BC
After completing this chapter, you should be able to:
• Choose methods to locate reliable information online.
• Propose ways to determine the veracity of online information.
• Demonstrate responsible use of social media for healthcare professionals.
• Identify reliable resources for health information and services.
• Discuss the benefits of online services for healthcare professionals.
• Name specific professional organizations and watchdog groups focused on electronic resources.
• Discuss the benefits and drawbacks to eLearning.
• Describe technology used to organize and use electronic information.
This chapter provides a brief overview of electronic resources available for healthcare profes- sionals, including online resources for information and learning. Also included is informa- tion about how to determine the veracity of information, services available for healthcare providers, organizing information, and future directions of electronic resources for healthcare professionals.
Information Literacy The Internet contains many sources of information, some credible and some not credible. Effective use of the Internet requires information literacy. Information literacy involves both the ability to locate information and to interpret information in a manner that is relevant to the user.
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Historically, database and literature searches involved a trip to a library and hours of time to comb through paper-based literature. Databases are now easily accessible and avail- able through the Internet as public libraries, colleges and universities, subscriptions, and open access database engines—such as Google Scholar—allow the user to access journals, publications, and periodicals quickly and easily from any device with an Internet connec- tion. Databases that contain information that can be used for research—such as de-identified patient records or self-reported patient databases—allow for the ability to study specific patient populations.
Although some databases are open to any user, most databases containing scholarly publications require a subscription or access through a school or organization. There are sites that provide pay-per-article access to literature; however, these sites may not have the full spectrum of literature found in a scholarly database.
Internet Searches The average computer user can quickly and easily access large amounts of information on the Internet. However, the ability for consumers to identify and utilize health information for positive change remains uncertain (Diviani, van den Putte, Giani, & van Weert, 2015; Medlock et al., 2015), although accurate use of online health information by health profession- als appears to be related to improve patient knowledge and outcomes (Laugesen, Hassanein, & Yuan, 2015). More concerning is the ability of both the consumer and healthcare profes- sional to identify and recommend valid and reliable health-information websites (Buultiens, Robinson, & Milgrom, 2012).
Critical Assessment of Online Information The number of websites and resources on the Internet is ever increasing. Healthcare profes- sionals must possess the skills to critically evaluate information and guide healthcare con- sumers to accurate information sources. The National Library of Medicine provides tutorials that can be used to assist in determining the validity of health information found on the Internet, in magazines, and on television. Table 4-1 provides some tips that can be used for evaluating health information. In addition to the general guide for evaluating information, another good rule of thumb is that larger organizations—such as hospitals, the government,
Table 4-1 Evaluating Online Health Information
Evaluation Criteria Questions to Ask
Provider Who is in charge of the website? Why are they providing the site? Can you contact them?
Funding Where does the money to support the site come from?Does the site have advertisements? Are they labeled?
Where does the information come from? How is the content selected? Do experts review the information that goes on the site? Does the site avoid unbelievable or emotional claims? Is it up-to-date?
Privacy Does the site ask for your personal information? Do they tell you how it will be used? Are you comfortable with how it will be used?
SOURCE: Based on Evaluating Internet Health Information: A Tutorial from the National Library of Medicine. Retrieved from www.nlm.nih.gov/medlineplus/webeval/webeval_start.html#
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universities and other health organizations—will have the most reliable sites. The owner of the website can often be verified by clicking on the “about us” link, or the publisher informa- tion at the bottom of the web page.
It is important to assume that information is not accurate until it has been verified as com- ing from a reputable source that accurately cites information and research contained on the site. Research studies cited in a magazine or on a website should contain all of the details of the research study, rather than a general statement that information is clinically proven or from a recent research study. Information should be thorough, without any noticeable gaps or omissions.
Information should contain a date, although this rule is not always followed. General information published on websites by the government does not always contain dates written or published; yet, these are reputable sites. The quality of the writing and the look of the website should be professional and well-organized. A website with misspelled words, poor organization, or a confusing design may contain information that is not reliable, especially if the website is also selling a product or service.
The Health on the Net Foundation (www.healthonnet.org) was formed in 1995 as a nonprofit, private organization dedicated to ensuring quality health information on the Internet through a code of ethics that guides websites to develop objective and high-quality information that is carefully designed to meet the unique needs of the identified audience. The HONcode principles as identified on the HON website are shown in Table 4-2.
When a website has been certified, it receives the HONcode designation (www .healthonnet.org). Any website that applies for HONcode certification undergoes an initial expert review process, as well as annual expert reviews. The HONcode certification does not appear on the web page unless the HONcode toolbar has been installed on the end user’s web browser. Once the toolbar has been installed, the HONcode seal will appear in color if the website has been certified.
The type of certification granted by HON for quality content differs from a certificate that indicates the security of the data passing between the end user’s browser and the website’s server, as exemplified with URL addresses that begin with HTTPS rather than HTTP typi- cally seen to submit financial information. HTTPS refers to the hypertext transfer protocol over secure socket layer used to transfer and display content securely. An organization must go through a validation process in order to obtain a certificate that commences with verification that the domain is owned by the purchaser of the certificate. At this level, there is no guarantee of the legitimacy of the business. The highest level of certification—extended validation—provides proof of legitimacy of the business and may be designated by a lock icon on the URL toolbar (Figure 4-1).
Table 4-2 HONcode Principles
Principle Evidence of Support
Principle 1—Authority Give qualifications of authors
Principle 2—Complementarity Information to support, not replace
Principle 3—Confidentiality Respect the privacy of site users
Principle 4—Attribution Cite the sources and dates of medical information
Principle 5—Justifiability Justification of claims/balanced and objective claims
Principle 6—Transparency Accessibility, provide valid contact details
Principle 7—Financial disclosure Provide details of funding
Principle 8—Advertising Clearly distinguish advertising from editorial content
SOURCE: “Health on the Net Foundation (HON), (2014). The HONcode: Principles. Retrieved from www.healthonnet.org/HONcode/ Patients/ Visitor/visitor.html. Used by permission. Copyright © 2014 by Health On the Net Foundation.”
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Health information abounds on the Internet, on television, and in magazines. Information may be presented in order to sell a product or service, information may be incorrect, or the website may exist to capture personal information for other uses. It is important to validate the accuracy of health and medical information by using certified or secured websites and through careful evaluation of key characteristics of the website.
Social Media—Responsibilities and Ethical Considerations The first social-media site was created in the late 1990s, with minimal use or notice. The next iteration of social media to emerge was Myspace (www.myspace.com), which captured the attention of young people in 2003. However, it wasn’t until Facebook and Twitter were implemented in 2004 and 2006 that the potential of social media captured the attention of the business and healthcare community. Since that time, new approaches to social media emerged on a regular basis, some based on sharing photos and some based on common interests. While use of social media has become second nature for many healthcare providers, and this use provides some valuable support of healthcare information, there are also many legal and ethical considerations to understand and apply.
One of the most widely used social-media sites is Facebook (www.facebook.com), which can be accessed on smartphones, tablets, and web browsers. Launched in 2004 for Harvard students, the rapid growth in popularity of the application resulted in a world-wide launch shortly after the original launch. Facebook allows users to connect with family, friends, and colleagues and provides the ability to share photos, thoughts, videos, web links, blogs, and other information. Facebook supports private user groups, public groups, and semi-private groups with common interests or relationships. Because Facebook is such an accepted part of the social structure for many people, it becomes a risk for HIPAA violations as healthcare- worker behavior has ranged from posting patient pictures, to describing patient situations, to discussing the interesting patient that they worked with that day. Despite the potential for oversharing of private and protected patient information, Facebook has become a powerful tool used for promoting businesses and healthcare organizations, for sharing healthcare infor- mation, and for advertising health products, both valid and not valid. An important consider- ation for users of social media is to assume that anyone may be able to access any information that is posted; therefore, best practice is to not share any professional information on this site. In addition, posting questionable pictures of one’s own social life may backfire when seeking employment or even when a patient may choose to look up a caregiver on a site.
Based on a post of 140 or fewer characters, Twitter (www.twitter.com) is a widespread social-media tool that allows users to share small bits of information, web links, photos, and videos. Accessible through smart phones, tablets, and web browsers, Twitter is frequently used by businesses and healthcare organizations to share health information, provide remote live access to conferences or events, links to blogs, and to quickly share the latest news or information. Although Twitter users have experienced fewer issues with HIPAA violations, care should be taken to share only information that is not private or protected or that could be traced back to a patient.
Figure 4-1 • Extended Validation SOURCE: 2015 Google Inc. All rights reserved. Google and the Google Logo are registered trademarks of Google Inc.
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LinkedIn (www.linkin.com) is another social-networking application that was designed to facilitate communication among business professionals. LinkedIn allows for posting and sharing of resumes, job opportunities, work experience, educational background, and other professional and social activities. LinkedIn is also used to enhance business awareness and advertising through sharing of white-paper-type articles, blogs, educational events, and advertisement of products.
Blogs (a derivation from “web log”) provide an Internet-based forum for lectures, journ- aling, or discussions about specific topics or personal thoughts. These web logs may or may not be interactive, depending on the topic, format, and audience. Blogs may be found in text or video format or a combination of the two. The blogger or audience is often able to share comments, images, files, and videos on the blog site. News articles may also include the ability to comment and respond to the article content. Blogs may also be linked to Facebook, Twitter, LinkedIn, or other social-media formats, in order to broaden the reader base.
A wiki is another web application that allows users to write, collaborate, and edit infor- mation and documents on a shared site. Wikipedia.org is the most widely known example of a wiki site. Because wiki collaborators are typically providing experience and opinion rather than scholarly information based on literature, a wiki may not be a highly reliable source of information. Wikis can be used in a private capacity where a higher degree of trust and confidence in the contributors can provide the format for an evolving body of information. Typically, wikis can support audio, video, images, text, and other types of files to be shared. Wikis may be used for the ongoing work of committees as the software provides a means for tracking history and changes.
RSS, which stands for “really simple syndication” is a code that provides for a streamlined delivery of web content to subscribers to the information feed. When a user subscribes to an RSS feed, the content is often viewed through an RSS aggregator, an application designed to post all feeds in one location. Some web browsers, such as yahoo.com, contain feeds from many different sites; however, an RSS aggregator allows the user to determine the sites and content for the feed rather than relying on the information that Yahoo has chosen to share with its readers.
In contrast to the term social media, social networking refers to the process of using online technologies, such as Twitter, Facebook, LinkedIn, and other sites, to expand the number of contacts for either business or social purposes (Merriam Webster, www. merriam- webster.com/dictionary/social%20networking). Both social-media tools and the process of social networking are being increasingly used in the healthcare community to help patients connect with each other, to allow healthcare providers to connect with each other, to allow the government and other regulatory bodies to remain connected with their healthcare con- stituents, for recruiting, and for students to connect with each other and with instructors. In addition, information and education are distributed to a broader audience through the use of blogs, wikis, podcasts, Twitter, and other social-media tools.
Healthcare Information and Services Navigating and locating data on the web can be difficult without the use of web-based tools designed to help locate desired information and sort search results. The use of information tech- nology to obtain health information, generate and identify evidence for practice, or to inter- act with patients varies by discipline (Ventola, 2014). Tools used to search for information may range from commonly used search engines to academic databases. As the number-one-ranked search engine, Google experiences more than 40,000 searches per second (www.Internetlivestats .com/google-search-statistics/). Despite the popularity of search engines such as Google or
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Yahoo, the user may not be aware that many search engines will rank search results based on either relevance to the keywords used or weight results according to the popularity of the sites containing matching information. Ranked or weighted search results may result in placing the articles of interest towards the end of the search results, providing top search results that have less relevance to the search topic because of their use as marketing tools (Glick, Richards, Sapozh- nikov, & Seabright, 2014).
In contrast with popular and commonly used web-search engines, browsers designed to search for healthcare-related information can help narrow search results and decrease the time spent sorting through search results. Healthcare-specific browsers include such sites as Springer Publishing’s biomedcentral.com, the metasearch engine Omni Medical Search (www.omnimedicalsearch.com), and the Consumer and Patient Health Section (CAPHIS) of the Medical Library Association. CAPHIS provides a ranking of the best websites for topic- specific health information searches that is available to members for a nominal fee. Often, peer-reviewed health research is only accessible through academic or publisher resources such as EBSCO, CINAHL, Ovid, and other sites. Some search engines, such as Medline, will provide the abstracts and locations for articles of interest, but full-text access is only obtained through a paid subscription site.
One of the most important parts of a data search on the Internet is the effective use of keywords to narrow and direct the search. A keyword search utilizes the most important words for the topic of interest; for example, if the information of interest was related to the average age of the nursing workforce, the keywords used might include nurse, age, and workforce. Changing the search terms can provide additional results, such as nurse, average age, and United States. To eliminate extraneous terms that the search engine may assume are valid, the search terms can be limited to only keywords with the use of quotation marks before and after the words.
For convenience and search speed, many publisher search engines index common search terms; these can often be seen as suggested searches that display as keywords are entered into the search-criteria fields. In order to increase the visibility of articles and publications, authors often identify keywords that are most closely related to the article topic; these keywords enhance or speed the search.
To locate the most relevant information for a web-based data search, it is usually neces- sary to use several different search tools to locate all relevant information. When subsequent Internet searches begin to produce the same information as prior searches, it can be assumed that most of the available information has been located. Additional challenges for web-based data searches include security filters that may block valid information.
Metasearch engines are search engines that utilize existing search engines to locate information, bypassing the indexing that is normally present. Search engines, such as Una- bot, utilize other metasearch engines to conduct an Internet search. Some other metase- arch engines include mamma (mamma.com), iBoogie (www.iboogie.com), Vroosh (www .vroosh.com), Turbo Scout (www.turboscout.com), Dogpile (www.dogpile.com), and more. The same concepts can be found in publisher search engines such as EBSCO’s Aca- demic Search Complete (www.ebscohost.com/academic/academic-search-complete), which searches through multiple databases for literature that would otherwise only be found within database-specific searches. OmniMedicalSearch (www.omnimedicalsearch .com) was developed to aggregate multiple sources of medical and patient information together into one searchable location. The site focuses on information that is regarded as reliable for users of the site. Medical World Search (www.mwsearch.com) is a search tool for selected medical sites that requires a subscription fee. It uses indexing and a the- saurus of uniform healthcare terms, which users can view before conducting a search.
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Additional sites include PubMed (www.pubmed.com), a free database of medical and healthcare-related journal articles managed by the National Library of Medicine, a part of the National Institutes of Health. Medline (www.medline.com) contains over 22-million biomedical articles. The Cochrane Database of Systematic Reviews (www. cochranelibrary.com/cochrane-database-of-systematic-reviews/) provides access to meta- analyses and systematic reviews of research studies. Google Scholar (https://scholar .google.com/) is a free search engine able to retrieve medical and healthcare-related journal articles from multiple databases (HealthWriterHub, www.healthwriterhub.com/ medical-journal-search-engines/).
Online Services for Healthcare Professionals There are many online services and educational websites available for healthcare profes- sionals. A rich source of health-information-technology (HIT) information for healthcare providers and consumer is www.healthit.gov. The site contains HIT information ranging from foundations of HIT to implementations and effective use of information technology for providers to consumer information about reliable health information and the role of informa- tion technology in improving personal health. The site also provides information for those working with health policy and the latest changes in the HIT landscape. The government’s Health Resources and Service Administration (www.HRSA.gov) provides information about healthcare initiatives throughout the United States, along with funding opportunities and strategic plans for this organization.
The American Nurses Association (www.nursingworld.org) provides links to continu- ing education, certification, and nursing information. Much of the information on this site is available free to non-members; however, some resources are for members only.
There are many commercial sites on the Internet that advertise free CE-credit courses for nurses. Care should be taken to ensure that the education provided by any of these sites is approved by the appropriate credentialing body for the particular licensure or certification requirements.
Most professional organizations offer links to further information on licensure and cer- tification applications and renewals. State boards of nursing maintain websites providing information on practice regulations, licensure requirements, and necessary application and renewal forms. Nursys (www.nursys.com) is a website that provides licensure information for all members of the National Council of State Boards of Nursing as a single source for verification of licensure. Nursys may also be used by individuals to verify the nursing license of any nurse. Nurses can opt to use the Nursys renewal reminder service to alert them when licenses are due to expire, a convenience when nurses are tracking licenses in multiple states.
Online services have added a level of transparency, efficiency, and convenience to infor- mation and credentialing access that was previously not possible when records were kept on paper.
Online Publication and Journals Online journals provide the ability to cost-effectively publish literature that is available to a broad audience without the costs of printing and postage. A fully online journal may be printed by the reader but is not offered in a paper-based format. Some journals may be offered
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in a print format at a different cost than the identical online version. Some of the benefits of online publication include:
• Efficient peer-review process. Distribution of content for peer review can be conducted quickly via email or other computer technology, and responses from peers are received instantly.
• Decreased cost. While costs for editing and review may remain the same, costs for printing and mailing are eliminated.
• Technology facilitates collaboration. Electronic tools, such as email, wiki sites, and document- sharing sites, allow for near-synchronous collaboration.
• Enhanced graphics. Expensive color printing and static images can be replaced with full color images, moving images, or interactive diagrams.
• Instant availability. The most recent journal edition is available immediately to any sub- scriber with an Internet connection.
• Enhanced information sharing. Journal articles may be available in PDF format for printing and sharing, and many online journals contain links that provide easy email sharing of selected articles.
• Enhanced searching. Journal readers can search electronically within or across issues for specific words or phrases and may be able to link to references and other related materials.
• Eliminated storage space. Electronic journals do not require space to store printed materials.
Informatics journals available in an online format include the Online Journal of Nurs- ing Informatics (www.ajni), the Journal of Informatics Nursing (www.ania.org/publications/ journal), CIN: Computers, Informatics, Nursing (journals.lww.com/cinjournal/Pages/ default.aspx), and Journal of the American Medical Informatics Association (www.amia .org/news-publications/jamia) are some examples of fully or partially online informatics- related journals. A web search using the terms online+journal+nursing will produce a list of many additional online journals. It is important to remember that some journals identified through this type of search may require a subscription to view the contents.
Professional Organizations and Watchdog Groups In response to the immense amount of information to be found on the Internet, both reliable and unreliable, numerous organizations and watchdog groups have arisen to monitor and promote the quality of healthcare information posted on the Internet. Despite the issues pre- sented by false and misleading information to be found on the Internet, none of these watch- dog organizations possess any authority to control content. Rather, watchdog groups function by review and validation of information and education of the public to increase awareness. The Health on the Net Foundation (HON) places a symbol on health content that it deems valid and reliable (www.healthonnet.org/). URAC (www.urac.org) was founded in 1990 to provide standards for healthcare quality, including accreditation services for healthcare web- sites. Websites that have been accredited by URAC will display a seal on the web page. The federal Department of Health and Human Services (DHHS) provides information for con- sumers about how to assess the accuracy and validity of online health information through the healthit.gov website (www.healthit.gov/patients-families/find-quality-resources).
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The eHealth Code of Ethics was developed in 2000 (Rippen & Risk, 2000), and the eight points in this document have been used by many other organizations for accreditation and educational purposes. The presence of a seal on a website can promote trust in the content; however, the proliferation of eHealth information on the Internet far outstrips the ability of any number of organizations to monitor or accredit. One of the best ways to determine the veracity of online health information remains vigilance and education, for both consumers and healthcare providers.
Healthcare Websites of Interest for Healthcare Providers Nurses and other healthcare providers rely heavily on the Internet for information on health and healthcare related topics. The following websites noted in Table 4-3 were designed to service nurses and other healthcare providers by placing related information into easily
Table 4-3 Healthcare Websites of Interest for Healthcare Providers
Organization Web Link Mission and Purpose
Agency for Healthcare Research and Quality (AHRQ)
www.ahrq.gov The mission of AHRQ is to produce evidence that will make healthcare safer and improve quality, accessibility, and affordability.
American Nurses Credentialing Center (ANCC)
www . nursecredentialing .org
The mission of ANCC is to promote excellence in nursing and healthcare throughout the world through credentialing programs and educational materials for nursing specialty- practice areas. In addition, ANCC recognizes healthcare organizations that meet standards of nursing excellence, quality patient outcomes, and safe, positive work environ- ments. Healthcare organizations that provide and approve ANCC continuing nursing-education programs are accred- ited through this organization.
Centers for Disease Control (CDC)
www.cdc.gov The CDC’s focus is to protect America from health, safety, and security threats, both foreign and in the United States, through critical science and health information that responds to, and protects our nation against, expensive and dangerous health threats.
Centers for Medicare and Medicaid Services (CMS)
www.cms.gov CMS is part of the Department of Health and Human Ser- vices (DHHS) and is focused on health insurance and improv- ing the quality of healthcare and the healthcare system.
Food and Drug Administration (FDA)
www.fda.gov As a part of DHHS, the FDA is responsible for protecting public health by assuring the safety, efficacy, and security of human and veterinary drugs, biological products, medical devices, our nation’s food supply, cosmetics, and products that emit radiation. In addition, the FDA advances public health through innovations that make medicines more effec- tive, safer, and more affordable and by helping the public get the accurate, science-based information it needs to use medicines and foods to maintain and improve health. The FDA ensures the security of the food supply and fosters the development of medical products designed to respond to deliberate and naturally emerging public health threats.
HealthIT.gov www.healthit.gov Managed through the Office of the National Coordinator for Health Information (ONC), healthit.gov assists healthcare pro- viders to better manage patient care through secure use and sharing of health information, including the use of electronic health records (EHRs) to manage people’s health information.
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searchable locations. Although the following list is not comprehensive, the websites are widely used and accepted as primary resources for health-information-technology topics. Information about each of these resources is based on the mission or purpose statements on their websites.
ELearning The Internet is increasingly used as a resource for lifelong learning, either as a course- delivery method for degree programs, for healthcare-facility-delivered training, for work-related knowledge delivery, and for continuing-education opportunities. Learning obtained through the use of computer technology is referred to as eLearning, web-based learning, computer- based learning, or, when combined with classroom learning elements, technology-assisted learning is referred to as blended learning or distance learning.
Studies have shown that blended learning can be just as effective as a full classroom- learning program, with reports of cost savings ranging from nothing (Cook, 2014) to a reported 24% decrease in training costs (Maloney et al., 2015). eLearning presents benefits unrelated to cost savings such as the ability to deliver education asynchronously in order to reach learners at the times they are available to learn, increased convenience in the learning process by eliminating a commute to the classroom, flexibility to allow the learner to man- age learning time around family time and other commitments (Neal, 2013), and consistency of the educational content (content that does not vary based on instructor style and prefer- ences). Studies have shown little difference between classroom and eLearning outcomes for the learner (Delf, 2013).
Successful eLearning is dependent on the ability of the learner to be motivated and self-directed. Because the learner is not relating to fellow learners or instructors in real time,
Organization Web Link Mission and Purpose
Health Resources and Services Administration (HRSA)
www.hrsa.gov HRSA’s mission is to improve health and health equity through development of a skilled health workforce, innova- tive programs, and access to quality services.
National Institutes of Health (NIH)
www.nih.gov The mission of the NIH is to seek fundamental knowl- edge about living systems and to apply that knowledge to enhance health, lengthen life, and reduce illness and disability.
National Library of Medicine-National Institutes of Health (NLM-NIH)
www.nlm.nih.gov NLM manages the world’s largest biomedical library and produces electronic information resources on a wide range of topics. It also supports and conducts research, develop- ment, and training in biomedical informatics and health- information technology.
Quality and Safety Education for Nurses (QSEN) Institute
www.qsen.org The Quality and Safety Education for Nurses Institute was founded to build the capacity for nursing to continuously improve the quality and safety of healthcare.
The National Academies of Sciences, Engineering, Medicine; Health and Medicine Division (HMD)
www . nationalacademies .org
Formerly known as the Institute of Medicine, the HMD helps those in government and the private sector to make informed health decisions through evidence. Each year, more than 3,000 individuals volunteer their time, knowledge, and expertise to advance the nation’s health through the work of the HMD.
Department of Health and Human Services (DHHS)
DHHS.gov The mission of the Dis to enhance and protect the health and well-being of all Americans through quality health and human services and through promoting advances in medi- cine, public health, and social services.
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the learner can experience feeling of isolation or disconnectedness when first experiencing online learning. In addition, the eLearning participant must have a degree of technical skills and ability to be able to navigate and manage the various elements within the course environment.
Similar to classroom-based learning, eLearning typically utilizes a course schedule that students must follow in order to remain in synchronization with the rest of the class, with the course topics, and with their assignments. A certain number of communication entries in the course room by certain times each week is the expectation for most academic eLearning programs, and grades are based on both participation and the quality of the course work presented.
Online learning environments are designed to provide learning opportunities that reflect best practices for adult learning and maximize interactivity in an asynchronous environment. Typically, learners interact through electronic discussion boards where responses to specific questions are posted and discussed among the learners and instructors. Many learning plat- forms are designed so that there are additional online spaces where learners can hold con- versations not related to the course topics; these are created so the learner does not become distracted by other discussion. Online learning platforms also provide the opportunity for links to electronic materials that can be shared and downloaded. In addition, colleges and universities are increasingly utilizing electronic textbooks, which eliminates the need for learners to obtain and access printed materials and books and provides for easy updates to the most recent textbook version.
Virtual Learning Environments Virtual online worlds provide the opportunity for students to practice and demonstrate skills, knowledge, communication abilities, and critical thinking. Virtual environments can be designed to reflect real-world environments or any variation in the spectrum from real- istic to fantasy. Virtual worlds provide the opportunity for many users in different locations to interact synchronously or to interact asynchronously with elements of the virtual world. Virtual worlds have been used for playing games for many years, and most recently, virtual worlds are being used for training and educational purposes. An example of a virtual world designed for nursing informatics is the TIGER Virtual Learning Environment (VLE) spon- sored by HIMSS (Healthcare Information and Management Systems Society, 2016). Another popular virtual world is Second Life, which hosts multiple sites devoted to healthcare.
Using Information Technology to Organize and Use Information Effectively Advances in information technology provide the ability to add efficiency to daily work through communication and organization tools such as email. The average employee in the United States spends one quarter of his or her day sorting through emails (Smith & Giang, 2014). Although email presents a potentially efficient way to communicate, the volume of email can produce a loss of productivity, and the quality of email communication can either enhance or degrade a professional image. Email’s functional structure can provide efficiency through the use of folders and rules for incoming messages.
Rules are a function within many email programs that allow the system to automatically perform functions that would otherwise be done manually, taking up valuable work time. A rule can be configured to enable incoming messages conforming to the created rule to
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undergo automatic action, based on the rule specifications. Examples of this might be emails from a certain sender that would be automatically placed into a subject-specific folder or into the delete-mail folder.
Folders provide a way to sort emails by sender or by topic. Folders are easily set up in an email program and provide the ability for quick search and storage of key information. Examples of folders might be a folder set up for informatics topics, a folder set up for mes- sages from co-workers, or folders set up by tasks that need to be completed.
An additional function found in some email programs is the ability to link an email to a task or action that can be scheduled for a certain date. This process becomes handy when an action is required from an email but the date for action is in the future. If the email is left in the common inbox, it will soon move towards the bottom of the list, out of sight and out of mind. Linking the email to a task will produce a reminder and keep the original email attached to the reminder until the task is satisfied.
Effective use of email can also enhance or decrease a professional image, based on how email is used. An article in Business Insider (Smith & Giang, 2014) provides recommendations for professional use of email.
• Include a clear, direct subject line. This allows the recipient to quickly identify important emails for timely review.
• Use a professional email address. Emails received from nonprofessional email addresses such as “sexynurse.com” will not convey a professional message to the recipient and may be deleted before being read.
• Do not use “reply all.” Using the reply-all function can clutter inboxes with information not relevant to a major of the people on the original email thread.
• Use professional salutations. Do not use nicknames, colloquial expressions, or slang when addressing emails in a professional environment.
• Minimize use of exclamation points. Overuse of exclamation points can add emotional drama or immaturity to an email message.
• Use humor cautiously. Much of humor is conveyed through body language. Unless the sender is certain of how the recipient will receive and understand humor, it should be left out of emails.
• Be culturally sensitive. Email use may differ by culture; be aware of social norms for other cultures when formatting an email message.
• Reply to emails. Good email etiquette dictates that emails should get a response, even when sent to the wrong person. A short acknowledgement is appropriate and polite.
• Proofread your email message. Mistakes in spelling and grammar can discredit the sender’s reputation, in addition, spellcheck can sometimes substitute very inappropriate words into a professional email.
• Double check the recipient. Many organizations have employees with similar names; it is important to ensure the correct recipient is identified.
Spreadsheets Spreadsheets are electronic tools that can be used to organize data such as lists of related data. The row and column format support calculations, but spreadsheet software can also be used to create staff schedules, track information, and make simple reports. A spreadsheet can perform some simple functions of a database but cannot provide the complex relationship linking and reporting that is found in a true database.
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Databases A database is an electronic format for storing, organizing, and retrieving data in an organized manner. Databases provide the backbone for any organizations that need to store, retrieve, and organize data—including EHR systems—and other uses for data in healthcare. Some com- monly seen types of databases include relational databases, distributed databases, operational databases, and hierarchical databases. The most common type of database is the relational database, which provides the ability to sort and retrieve data through its relationship with other data fields such as linking a name with an address or a gender with a diagnosis. At the simplest end of the spectrum, a spreadsheet can store, organize, and retrieve data in a limited fashion. On the other end of the spectrum, a data warehouse can store and associ- ate data from multiple systems in one location so that information from a billing system, a pharmacy system, patient-care documentation, data from physician records, and even state immunization records can all be linked together. The more complex the database, the more work is involved in designing the database and ensuring that the data is valid, reliable, and internally consistent.
Future Directions The rise of information technology has changed healthcare in many ways, by providing quick and easy access to reliable health information, by enhancing the speed of dissemination of new information, by providing availability of information to anyone with an Internet con- nection, by facilitating communication and networking through social-media tools, and by increasing access to learning and information. Some challenges associated with the explosion of information accessed through technology include the need to validate information found on the Internet, enforcement of responsible use of the Internet and social media by healthcare workers, development and promotion of reliable information sources for healthcare workers and consumers, and protection of consumers from unethical uses of information technology and information.
Glimpses into the future of information technology produce visions of improvements in the usability and information-sharing abilities of electronic health records, development and use of standards for the efficient use of health-information technology, and increased sharing and use of health data to improve patient care and population health (DeSalvo, Dinkler, & Stevens, 2015; Department of Health and Human Services, 2015). Although health- information technology has advanced tremendously in the past decade, further enhancement and sharing of information will be needed to fully realize the benefits that have been promised.
Summary • Information literacy, the ability to locate and interpret information in a manner that
is relevant to the user, is key to effective use of the Internet. • Online information can increase access to information and decrease search time. • The US National Library of Medicine provides tutorials that can be used to help
determine the validity of health information. • In general, large organizations, such as hospitals, the government, universities, and
other large health organizations, have the most reliable online information. • When evaluating information quality, look for qualified, credible sources, and
specific details and dates that enable independent verification of information.
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• Health on the Net Foundation is a nonprofit, private organization dedicated to ensuring quality health information on the Internet through an accreditation process; once accreditation is achieved, the HONcode seal appears on the website and may be verified by browser searches.
• Social media can provide valuable information for healthcare professionals but must be used with caution to avoid compromising patient information or organization policies.
• Key search terms and healthcare-specific browsers can yield more relevant results. • Metasearch engines send out simultaneous queries to other search tools, bypassing
indexing structures for faster results than independent searches of the individual tools yield.
• HealthIT.gov is a rich source of HIT information. • Professional organizations and government agencies provide valuable links to con-
tinuing education, relevant legislation, certification and licensure information, and practice updates.
• Professional online publications shorten traditional timelines to disseminate informa- tion, help to hold down costs, support search capability, and save space.
• elearning, inclusive of virtual learning environments, provides additional flexibility for both traditional and continuing education.
• Technology opens us to an overwhelming amount of information but also provides tools to manage information that include, but are not limited to: • Email rules • Email folders • Linking email to a task or conversation stream • Clear, concise professional use of electronic communication limiting copies to indi-
viduals with a need to know • Really simple syndication (RSS) • Spreadsheets • Databases.
About the Author Brenda Kulhanek is the AVP of Clinical Education for HCA in Nashville, Tennessee. She has served on the board of the American Nursing Informatics Association since 2012 and cur- rently fills the role of president elect. Dr. Kulhanek is an adjunct and visiting professor for graduate and postgraduate nursing informatics programs at Walden University and Cham- berlain College of Nursing.
References Buultiens, M., Robinson, P., & Milgrom, J. (2012). Online resources for new mothers:
Opportunities and challenges for perinatal health professionals. Journal of Perinatal Education, 21(2), 99–111. doi:10.1891/1058-1243.21.2.99
Cook, D. A. (2014). The value of online learning and MRI: Finding a niche for expensive technologies. Medical Teacher, 36(11), 965–972. doi:10.3109/0142159X.2014.917284
Delf, P. (2013). Designing effective eLearning for healthcare professionals. Radiography, 19(4), 315–320. doi:http://dx.doi.org/10.1016/j.radi.2013.06.002
DeSalvo, K. B., Dinkler, A. N., & Stevens, L. (2015). The US Office of the National Coordinator for Health Information Technology: Progress and promise for the future
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at the 10-year mark. Annals of Emergency Medicine, 66(5), 507–510. doi:http://dx.doi. org/10.1016/j.annemergmed.2015.03.032
Department of Health and Human Services. (2015). ONC releases interoperability roadmap. Journal of AHIMA, 86(4), 10.
Diviani, N., van den Putte, B., Giani, S., & van Weert, J. C. (2015). Low health literacy and evaluation of online health information: A systematic review of the literature. Journal of Medical Internet Research, 17(5), e112-e112. doi:10.2196/jmir.4018
Glick, M., Richards, G., Sapozhnikov, M., & Seabright, P. (2014). How does ranking affect user choice in online search? Review of Industrial Organization, 45(2), 99–119. doi:10.1007/ s11151-014-9435-y
Healthcare Information and Management Systems Society (HIMSS). (2016). TIGER Virtual learning environment. Retrieved from www.himss.org/professional-development/ tiger-initiative/virtual-learning-environment
Laugesen, J., Hassanein, K., & Yuan, Y. (2015). The impact of Internet health information on patient compliance: A research model and an empirical study. Journal of Medical Internet Research, 17(6), e143–e143. doi:10.2196/jmir.4333
Maloney, S., Nicklen, P., Rivers, G., Foo, J., Ooi, Y. Y., Reeves, S., . . . Ilic, D. (2015). A cost-effectiveness analysis of blended versus face-to-face delivery of evidence-based medicine to medical students. Journal of Medical Internet Research, 17(7), e182–e182. doi:10.2196/jmir.4346
Medlock, S., Eslami, S., Askari, M., Arts, D. L., Sent, D., de Rooij, S. E., & Abu-Hanna, A. (2015). Health information-seeking behavior of seniors who use the Internet: A survey. Journal of Medical Internet Research, 17(1), e10–e10. doi:10.2196/jmir.3749
Neal, J. (2013). Innovation in education: Using elearning to improve the quality of education for practice nurses. Practice Nurse, 43(6), 40–43.
Rippen, H., & Risk, A. (2000). eHealth code of ethics. Journal of Medical Internet Research, 2(2). doi:http://doi.org/10.2196/jmir.2.2.e9
Smith, J., & Giang, V. (2014). 11 email etiquette rules every professional should know. Careers. Retrieved from www.businessinsider.com/email-etiquette-rules-everyone-should- know-2014-9
Ventola, C. L. (2014). Social media and health care professionals: Benefits, risks, and best practices. Pharmacy and Therapeutics, 39(7), 491–520.
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Using Informatics to Support Evidence-Based Practice and Research Melody Rose, DNP, RN
After completing this chapter, you should be able to:
• Identify the role of outcomes research and transformational research in modern healthcare.
• Explain the need for defining levels of evidence in healthcare research.
• Identify existing models of integration for evidence-based practice into clinical practice.
• Define the need for research databases and data repositories related to research.
• Identify ethical and legal principles of research related to patient privacy and security.
• Identify and discuss methods of research dissemination based on current government funding practices.
• Identify key government agencies that influence healthcare research through management and funding.
• Discuss the role of nursing in the future of healthcare research.
Evidence-based practice comes from the culmination of many years of research and research styles that have been modeled into empirical databases feeding best clinical practices. His- torically, quantitative and qualitative models of research have been followed and utilized. Studies suggest an average qualitative/quantitative research project takes 17 years to dis- seminate results (Munro & Savel, 2016). The need for a quicker, more healthcare-specific model of research was met in the last half of the 20th century when outcomes research (OCR) became the standout model that strove to measure outcomes through quality indicators
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(In & Rosen, 2014). The ultimate goal of OCR is to improve quality of care through examina- tion of outcomes (In & Rosen, 2014).
The main concepts of OCR are to minimize variations in practice by developing qual- ity guidelines, providing ways to systematically measure the functioning and well-being of patients, linking treatments and outcomes data through databases, and analyzing databases so results can be quickly and easily disseminated (In & Rosen, 2014). Measurable outcomes are defined as biological endpoints, survival, quality-of-life, functional status, costs, cost- effectiveness, and patient satisfaction (In & Rosen, 2014).
History In the last half of the 20th century, the terms comparative effectiveness research (CER), health services research (HSR), and outcomes research (OCR) were all types of research used in healthcare. Within the decade of the 1990s, the importance of OCR was realized through par- ticipation from several major organizations. Of those, the Agency for Health Care Policy and Research (AHCPR) (National Archives, 2016), which later became the Agency for Healthcare Research and Quality (AHRQ), established OCR programs to sponsor trial research pro- grams and develop research networks (Agency for Healthcare Research and Quality, 2002; In & Rosen, 2014).
In 2001, the Institute of Medicine’s (IOM) report Crossing the Quality Chasm: A New Health System for the 21st Century indicated that information technology (IT) was integral to achiev- ing substantial quality improvement for the delivery of high-quality healthcare (Institute of Medicine, 2001). As a result of this recommendation, the AHRQ supported multiple projects that explored the potentials of IT.
Integrated Delivery System Network The AHRQ operated the Integrated Delivery System Research Network (IDSRN) from 1999–2005. This project used a model of field-based research and joined with top research- ers from some of the nation’s largest healthcare systems (Agency for Healthcare Quality and Research, 2002). The uniqueness of this program was the strategy of establishing part- nerships between researchers and consumers of the research (Gold & Taylor, 2007). This approach put relevant research into the hands of users that had typically been outside of the research community (Gold & Taylor, 2007). As a result of the IDSRN project, many clini- cal databases have been developed such as the National Comprehensive Cancer Network (NCCN) and Oncology Outcomes Database. National registries have also been developed. Examples of cancer registries include American College of Surgeons (ACS), Commission on Cancer (CoC), and Centers for Disease Control and Prevention’s (CDC) National Pro- gram of Cancer Registries (NPCR) (Centers for Disease Control and Prevention, 2016).
Accelerating Change and Transformation in Organizations The IDSRN project was followed by the Accelerating Change and Transformation in Organiza- tions and Networks (ACTION) program. The overall goal of the ACTION project was accel- erating the diffusion of research into practice (AHRQ, 2015a). The ACTION project continued to use the field-based collaborative relationship model established within the IDSRN project. ACTION focused on finding results, both successful and unsuccessful, to improve healthcare outcomes and disseminating results (AHRQ, 2006). Topics of study for the ACTION project were
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suggested by participating members and approved by AHRQ. Topic research timeframe was an average completion of 15 months (AHRQ, 2006). ACTION was a five-year initiative that began in 2005. Since its initiation, ACTION II and ACTION III have been launched as con- tinuations of this initiative. All ACTION III projects are scheduled to be complete by 2018 (AHRQ, 2016).
Practice-Based Research Networks In the 1970s, primary care physicians in the United States followed the lead provided by European counterparts and began developing research networks. Through the AHRQ, federal grant funding has been available for practice-based research networks (PBRN) since the early 21st century (AHQR, 2012a). Information aggregated from PBRN improves practice for the primary care provider and patient. Through this network, information becomes relevant to the clinician and can be introduced quickly into practice (AHQR, 2012a).
Translating Research into Practice Translating research into practice (TRIP) and, later, TRIP II were studies funded by AHRQ beginning in 1999 and designed to look at the way primary-care teams were using health- information technology (HIT) (AHRQ, 2013b). The TRIP and TRIP II studies looked at Practice Partner Research Network (PPRNet) participants for project-specific electronic health data and the effect on practice in improving quality that came from this translated information (AHRQ, 2013b). A total of 27 projects were funded through TRIP and TRIP II (AHRQ, 2015).
Levels of Evidence Finding the best quality of research to follow and implement has been a problem for health- care. In a still-relevant article, Ebell et al. (2004) discussed this problem. Sorting out what is credible and what is not can be time-consuming and resource-intensive. Discovery of level- of-evidence has been the topic of discussion and a point of deliberation for many nurses and nurse administrators. By the early 2000s, many evidence-grading scales had been developed by a variety of organizations. Adding confusion to the mix, various scales that were in use were inconsistent in what was scored, used no standardized criteria for comparison, and sometimes offered opinion of reviewer rather than scientific review (Ebell et al., 2004). Early scales were reported in many different formats, A through E, one through five, or V through I. Many of the grading scales were so complex that the clinical user would not or could not take time to review the results in depth for application value (Ebell et al., 2004). Research that is not translatable is not viable research. This lack of consistency left the clinical healthcare and research communities looking for a better way to evaluate and translate the level of evidence in healthcare research.
Grading of Recommendations Assessment Development and Evaluation In 2000, a collaboration of people formed the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) work group with a goal to address levels of evi- dence (Grade Working Group, 2016). The GRADE approach was to get input from many
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contributors including representatives of other grading systems. The thought behind this strategy was to bring forward previous work and ideas and promote the development of a transparent approach for grading research evidence. Among the international organizations represented in the initial work group were the World Health Organization, Centers for Dis- ease Control and Prevention, and Oxford Center for Evidence-Based Medicine. Upon con- clusion of the original product from this workgroup, groups that actively supported the use of the result of GRADE workgroup included the Cochrane Collaboration, the World Health Organization, and Up To Date. The most recent update to GRADE was made in April of 2016 (GRADE Working Group, 2016).
The GRADE approach provides four levels of rating of evidence—high, moderate, low, and very low. An algorithm of concrete rules was developed that can be applied to evidence, guiding research into the appropriate rating. Four areas of criteria were constructed for the algorithm: (1) number of participants, (2) risk of bias of trials, (3) heterogeneity, and (4) meth- odological quality of the review. As the algorithm is applied, the reviewer determines if downgrades to the level of evidence should be applied. The total number of downgrades applied determines the level rating the evidence receives.
Agency for Healthcare Research and Quality Methods Guide The Agency for Healthcare Research and Quality (AHRQ) recognized that healthcare deci- sion-making should be done through systematic reviews. The Evidence-Based Practice Council (EPC), initiated in 1997, has worked with a variety of organizations and agencies to establish a comprehensive method of comparative effectiveness reviews. The Effective Health Care (EHC) Program, initiated in 2005, was challenged to improve healthcare delivery in the United States by improving quality, efficiency, and effectiveness (AHRQ, 2014). Both groups are sponsored by the AHRQ.
There are 14 EPCs throughout the country. In 2004 to 2005, the councils developed meth- odologies to assess the level of evidence within research. From this preliminary work, a Methods Guide was developed. The initial Methods Guide was based on the approach devel- oped by the GRADE working group (AHRQ, 2014). Differences include terminology: EPC refers to strength of evidence, GRADE refers to quality of evidence. EPC identifies three domains as directness, consistency, and precision while GRADE identifies three domains as indirectness, inconsistency, and imprecision. EPC design is intended more for individual studies that are not necessarily compared to other studies. This methodology is intended for a more diverse type of user than the researcher. EPC also does not make or grade clinical recommendations (AHRQ, 2013a).
American Association of Critical-Care Nurses Levels of Evidence The American Association of Critical-Care Nurses (AACN) taskforce developed an evidence- rating scale in 1995. The original rating scale used Roman numerals and rated evidence with lower numbers representing lower levels of evidence (Armola et al., 2009; Peterson et al., 2014). This system was considered confusing as other rating systems used a reverse order. The AACN established a volunteer workgroup in 2007 to focus on developing resources for the bedside clinician. This group was called the Evidence-Based Practice Resources Work- group (EBPRWG) and developed many valuable tools for clinicians. In 2008, the EBPRWG
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was challenged to review and make recommendations for improving the AACN rating tool (Armola et al., 2009; Peterson et al., 2014). The end product was an alphabetical rating scale using A for the highest level of evidence and moving down through the alphabet to letter E as reliability decreased. The letter M was reserved for evidence that was recommended only by the industry or manufacturer. In 2011 to 2012, the EBPRWG revised the rating tool to discriminate between randomized control trials and reviews of other studies (Armola et al., 2009; Peterson et al., 2014).
Strength of Recommendation Taxonomy Through encouragement and directives from the Federal government, primary-care phy- sicians and providers have been collecting and storing patient data in practice-based research networks (AHRQ, 2012a). Physicians and providers collected data but did not have a reliable way to qualify evidence-based outcomes from outside sources. Several journals and the Family Practice Inquiries Network (FPIN) developed a taxonomy for this purpose (Ebell et al., 2004). The Strength of Recommendation Taxonomy (SORT) provides a strength of recommendation based on a body of evidence. The strength of recommenda- tion is based on an A, B, C scale with A indicating consistent, good-quality patient-oriented evidence; B indicating inconsistent or limited-quality patient-oriented evidence; and C indicating consensus, disease-oriented evidence, usual practice, expert opinion, or case series for studies of diagnosis, treatment, prevention, or screening (Essential Evidence Plus, 2016). The body of evidence is evaluated by four elements: (1) study quality, (2) diag- nosis, (3) treatment/prevention/screening, and (4) prognosis.
Applying Information Literacy to Find the Highest Levels of Evidence Searching and researching for evidence has changed exponentially since the 1990s with the introduction of the Internet and the personal computer. The Internet has made millions of data bits available to researchers, and the development of the computer and the personal computer has brought accessibility to the level of the individual in their home. For the curi- ous, if you have a question and resources to access the Internet, you can probably find some form of an answer.
Meta-analysis of multiple controlled studies, systematic reviews, and randomized controlled trials (RCT) are considered potentially high-level evidence resources (Peterson et al., 2014; AHRQ, 2014; AHRQ, 2012b; Facchiano & Snyder, 2012). Searching for this level of resources can be accomplished in several ways. If using a search engine from the Inter- net, please use a scholarly search engine. Electronic libraries provide access to databases that specifically house scholarly medical resources. Many of the electronic databases have restricted access and can only be used by members. Entities that house electronic libraries include colleges/universities, healthcare institutions, service organizations, and professional organizations. Databases specific to health and healthcare provide methods to help narrow searches. Primary searches can be done from a word or phrase. Advanced searches allow the user to expand or restrict a search in many different areas. Examples of expanding search criteria are searches through Boolean phrases (connectors such as “and,” “or,” “not”), searches for partial match of phrase, or related words. Examples of restricting search crite- ria are limiting publish date ranges and specifying full text, peer-reviewed articles. Clinical
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practice guidelines can be searched for by using the key term “clinical practice guideline” in search criteria and naming the specific topic desired.
Filtered resources are resources that have been preappraised for quality and content. Filtered resources will often make practice recommendations (Facchiano & Snyder, 2012; Walden University, 2016). Unfiltered resources are typically primary or original research stud- ies that have not been appraised as a filtered resource has (Facchiano & Snyder, 2012; Walden University, 2016).
When searching for a specific topic using a keyword or term, add additional defining terms through an advanced search as necessary to narrow the search in the desired direction. If a specific type of research is desired, this can be included in the search criteria. Examples are qualitative, quantitative, peer-reviewed, randomized-controlled study, and population- intervention-comparison-outcome (PICO) (Facchiano & Snyder, 2012).
The following databases are specific to systematic research reviews:
• Cochrane Library.
• Joanna Briggs Institute EBP Database.
• Database of Abstracts of Reviews of Effects (DARE).
The following databases are commonly used when searching for original research articles:
• Proquest Nursing & Allied Health.
Integration of EBP into Clinical Systems and Documentation Knowledge translation (KT) from the point of valid research to implementation at the bedside can take place through many well documented models. Most strategies agree there has to be an existing culture that is willing to accept a change of practice, appropriate facilitation of implementation methods, key stakeholders that include bedside clinicians with admin- istration involvement, and some form of evaluation post implementation conducted (Roe & Whyte-Marshall, 2012; Schaffer, Sandau, & Diedrick, 2013; Hubbel & Greenbaum, 2014; Kitson & Harvey, 2016). Multiple models exist for the purpose of integrating evidence-based practice (EBP) into use. While many of these models were developed several years ago, they remain relevant. Top models include the Stetler model, the Iowa model of evidence-based practice to promote quality care, the ACE star model of knowledge transformation, and the PARIHS framework (Schaffer et al., 2013). Other viable models exist and are well documented in scholarly literature.
Promoting a culture of change is necessary for successful implementation of evidence- based practice (Hubbell & Greenbaum, 2014). Change cannot be successfully implemented unless there is agreement—from the very top levels of administration through the clinical caregivers—that change in practice is necessary and will happen. The same message has to be delivered throughout the project to all facility members affected by the coming change. Transparency and consistency are paramount when implementing change.
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Regardless of the implementation strategy (method) chosen, several key points of rep- resentation have been identified in documented methods. Identified stakeholders need to include those being impacted most significantly by the change (Roe & Whyte-Marshall, 2012; Schaffer et al., 2013; Hubbel & Greenbaum, 2014; Kitson & Harvey, 2016). Senior administra- tion needs to be present as a stakeholder to encourage ownership and commitment through- out the project and also to be aware of any long-term risks that arise as the project continues (Dogherty, Harrison, Graham, Vandyk, & Keeping-Burke, 2013). Potentially, multiple or addi- tional projects can present a risk to a long-running project through impacts on resources and commitment. Having senior administration as a stakeholder provides a clear focus of priori- ties for project resources and commitment.
Evaluation is the process used to determine if the evidence-based intervention(s) implemented are/have been successful. Evaluation should be planned based on the model of implementation. Immediate evaluation at the time of implementation should be done to determine if patient safety issues or major financial issues exist. Barring these, no changes should be made to the newly implemented system for approximately 30 days, allowing the end user learning curve to level off. At 30 days—or in many cases prior to this mark—the true value of the new system can start to be realized. At this point, an evaluation process can begin.
Stetler Model The original Stetler model was developed in 1976 and was revised specifically to the needs of EBP in 1994 and again in 2001 (Gray, Grove, & Sutherland, 2017). The model is divided into five phases: (1) preparation, (2) validation, (3) comparative evaluation/decision-making, (4) translation and application, and (5) evaluation. The Stetler model can be oriented toward the individual or team approach (Schaffer et al., 2013).
Iowa Model of Evidence-Based Practice to Promote Quality Care The Iowa model of evidence-based practice (EBP) was originally developed in 1994 and revised in 2001 by Titler and colleagues (Gray et al., 2017). The Iowa model stresses the importance of prioritizing triggers based on the need for change and the needs of the clinical agency. Once supporting evidence of change is found, the change is introduced in a pilot environment. Evaluation of the pilot leads the facility to the decision of adopting the change or not (Gray et al., 2017; Schaffer et al., 2013). If the change is implemented facility-wide, ongoing evaluation with dissemination of results will be continuing com- ponents of the model (Schaffer et al., 2013).
ACE Star Model of Knowledge Transformation The Academic Center for Evidence-Based Practice (ACE) developed the ACE star model to address translation and implementation aspects of the evidence-based practice process. The model applies evidence to clinical nursing by considering factors that determine the likeli- hood of adoption of practice. There are five steps in the ACE star model: (1) discovery of new knowledge; (2) rigorous review process of evidence followed by summary; (3) translation of evidence for clinical practice; (4) integration of intervention into practice; and (5) evaluation of the practice change (Schaffer et al., 2013).
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PARIHS Framework The PARIHS (Promoting Action on Research Implementation in Health Services) frame- work is considered less structured than a true model of practice, providing a theory of practical application (Schaffer et al., 2013). PARIHS framework defines three key elements that, when used together, will mutually influence each other for a successful EBP imple- mentation (Schaffer et al., 2013). The three elements are: (1) evidence—sources of knowl- edge from multiple stakeholders, (2) context—quality of the environment EBP is to be implemented, and (3) facilitation—how change will be supported (Kitson & Harvey, 2016; Schaffer et al., 2013). The PARIHS framework had been redefined to i-PARIHS utilizing F-facilitation, I-innovation, R-recipients, and C-context (Kitson & Harvey, 2016).
Managing Research Data and Information Organizing captured data and making it accessible has become a topic of great interest with the increase of health information technology (HIT). Part of the success of outcomes research is combining researchers with consumers of research (clinicians) so results can become more transparent. A bigger picture arises when determining what to do with all of the data that is being collected. How can this data be saved, archived, and made available to researchers and consumers as needed?
A newer approach, through translational science, attempts to understand the scientific and operational principles that support each step of generalizing research from one environ- ment to the next. The aim of translational science is to move data gained from the translational research process to the level of the clinician and patient, quickly (NCATS, 2015). The National Center for Advancement of Translational Science (NCATS), part of the National Institutes of Health (NIH), was established in 2012 to improve strategies of developing and disseminating research for translations science (NCATS, 2015).
Data collected for research and subsequently stored is subject to the Office of Human Research Protections policy providing guidance to institutional review boards (IRB) regarding anonymity. The IRB review also validates protection of the rights and welfare of human subjects within the research project (Food and Drug Administration, 2106). Patient information that has not been de-identified is also protected by Health Insurance Portability and Accountability Act (HIPAA) rules and cannot be shared without patient consent (Bardyn, Resnick, & Camina, 2012).
Databases The National Library of Medicine (NLM) sponsors the National Network of Libraries of Medicine (NN/LM) program with a goal to improve access of biomedical information to US health professionals and the public’s access to information (National Institutes of Health, 2016). Eight regional medical libraries and five national centers provide outreach efforts to researchers, health professionals, the public workforce, and the public, providing access to quality health information through research databases. Access to NN/LM information is by membership, which can include libraries, information centers, or other types of organizations.
The White House Office of Science and Technology Policy (OSTP) required that federal agencies make results of federally funded scientific research available to the public, industry, and scientific communities. The AHRQ established guidelines for this compliance to be com- plete by October 2015. The guidelines apply to all scientific publications and data in a digital format (AHRQ, 2015). The intent of these guidelines is to provide public access to scientific research results, including field data, lab data, quality-control samples, sample ID data, and
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instrument-calibration data, in a digital format. Final result formats can be viewed or down- loaded without charge (AHRQ, 2015). Data that has personal identifiers is out of scope for this reporting. AHRQ stores data through a commercially contracted data repository. AHRQ research submitted for storage will be cross-referenced using the AHRQ-funded identifi- cations number (PubMed Central Identification—PMCID) providing linkage into PubMed records (AHRQ, 2015).
Data Mining Scientific research that involves searching and researching large networks and databases of information for collaborative purposes can be defined as escience (Bardyn, Resnick, & Camina, 2012). Escience can span many types of digital data sources including (but not limited to) academic, institutional, private, public, government, and research databases/ repositories. Digital curation maintains data long-term for retention of value and risk of digital obsolescence (Bardyn et al., 2012). Data sets of clinical research can be stored in many different formats and are/will be used for retrospective analysis. The ability to recall these formats must be maintained as part of the curation process.
Long-Term Management Preservation and sustainability balanced with cost-effective data management brings the proj- ect plan of contracting to a commercial repository and periodic reviews to identify coverage gaps and needs (AHRQ, 2015). The Office of Extramural Research, Education and Priority Populations (OEREP) has been tasked with this responsibility. A data management plan must now be submitted with any grant application for project funding through the AHRQ process (AHRQ, 2015).
Creating and Maintaining the Infrastructure to Support Research Creating and maintaining an environment to house research is multifaceted in nature. Orga- nizations that maintain a sustainable health-services-research infrastructure have consider- ations in some, if not all, of the following areas: research staff with expertise and experience; financial considerations that include contracts and grants; research facilities and equipment; and partnerships for dissemination of information (AHRQ, 2011).
Research Staff Although the researcher is typically thought of as the only necessary person for research, this statement is not true. A competent research office requires a team of multiple people, depen- dent on the size of the facility. The facility director and/or manager will help select research projects, organize the daily running of the office, validate credentialing of researchers, orga- nize validation of project time lines, and generally keep the office running. Researchers, research students, and staff may be permanently assigned to the facility or work on condi- tional/contractual projects. All people involved with research projects have to be documented to the project. Daily, weekly, and ongoing reporting is necessary for projects. This work may be designated by the director/manger to researchers or other staff.
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Financial contracts and grant applications are an essential part of research. Writing and reviewing these documents are roles of the research staff. Both researchers and grant writers can author contracts and grant applications. In some organizations, these documents will be diverted through a legal department for review before submission to an intended recipient. This can be done by a designated contract/grant role or be left to the individual researcher, but follow-through on this process has to be complete.
Every research project has to follow predetermined rules of ethics and compliance. Research proposals have to be approved by an Food to validate protection of the rights and welfare of human subjects within the research project (Food and Drug Administration, 2016). If the research facility is associated with an IRB, this may provide an easier portal of entry for this process. If not, an IRB site will need to be established for review of research projects. All elements of the HIPAA have to be observed for privacy and confidentiality.
Appropriate policies and procedures (P&Ps) must be in place for a research facility. These P&Ps will range from mission statements to personnel statements and include all facets of the functioning of the facility. It is the responsibility of the research staff to make sure appropriate P&Ps are in place and reviewed on a routine schedule.
Financial Considerations Research is an expensive proposition. Many research projects are funded by government grants, nonprofit agencies, and academic institutions (Mick, 2015; AHRQ, 2015). Another method of supporting research is encouraging corporate entrepreneurship strategies, creating an environment that supports competitive yet collegial research that will return profit for the corporation (Holmes, Zahra, Hoskisson, DeGhetto, & Sutton, 2016).
Budgeting and project management are ways to monitor cost, check expense, and project status of the project at hand. Outside of the actual research, reporting on total amount spent, amount spent by category, total labor hours, labor hours by category, indirect costs incurred, percentage of total budget remaining, and percentage of time remaining on grant or contract are reportable items (AHRQ, 2011).
Research Facility The actual research facility is a consideration. The research office (physical location of research- ers, managers, directors, grant writers, etc.) may or may not be located where the information technology (IT) servers are located. In the situation of academic health services databases, the physical location of the research office may be on the same campus but logistically distant from the IT servers. In a professional or corporate environment, these entities may be united in the same location. IT server storage space can also be commercially contracted and located remotely in comparison to the actual research office.
Technology infrastructure is a consideration. The only guaranteed factor in tech- nology is that it will change. The known facts are that data must be stored, data must be retrieved, and data must be aggregable. The physical technology has evolved and is capable of processing collected data. Within the data collection process, it is important to define data parameters for extracting data (Cole, Stephens, Keppel, Hossein, & Baldwin, 2016). Data is more easily collected and aggregated from discrete data fields rather than narrative fields. Rigorous documentation of parameters and processes should be stored with research data.
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Partnerships Multiple facets of research exist. Researchers may need a relationship with an organization to do research. Organizations may have an identified need and resources for research. Gov- ernment agencies may have grant monies available for research projects. Independent data warehouses may have servers that can store, aggregate, and report data. Forming a partner- ship between any of these entities could be mutually beneficial. Partnerships can be formed with academic institutions, individuals, businesses, foundations, think tanks, nonprofit organizations, different levels of government—the list is endless. Partnerships can also be formed nationally and internationally. Partnerships should have well-defined roles, be mutu- ally beneficial, and promote the culture of research.
Ethical and Legal Principles for Handling Data and Information in Research Evidence-based practice is driven by research. The way research data is completed and pro- cessed must be done ethically and with legal precision. Accessibility through Internet and research networks has made research materials much more available and accessible. Elements that must be considered in the ethical and legal handling of data for research are the use of informed consent, respect of privacy and confidentiality for the patient and protective laws, the respectful and appropriate use of intellectual property, and the potential for multiple or conflicting roles of the researchers and/or writers.
Informed Consent Informed consent is the modern basis for medical treatment. Patients expect transparency in care and treatment. This begins with conversation and education from the first point of con- tact and can include signing legal documents that give permission (consent) for treatments, surgeries, blood transfusions, and so on. This process of transparency in care and treatment transfers from caregiver to caregiver as the level of care changes. If the patient, procedure, or any part of the procedure is specifically used in a research project, the patient must be made aware and consent must be obtained.
Privacy and Confidentiality The Health Insurance Portability and Accountability Act (HIPAA) became public law in 1996 (DHHS, 2016). The purpose of HIPAA was multi-focused but has generally become known for its effect on patient privacy and confidentiality within the electronic health record (EHR). The American Psychological Association (APA) indicates that maintaining confidenti- ality within the research environment is a priority (APA, 2010). Any breach of confidentially within the healthcare environment is subject to the penalties of HIPAA. This can lead to fines for the facility involved and fine and jail sentences for individual violators. The Office of Civil Rights (OCR) enforces HIPAA (DHHS, 2016).
Intellectual Property Transformational science exists because of the research communities’ ability to network and share information. Digital information transferred electronically through networks and Inter- net capabilities has made national and international sharing a reality. With this capability
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comes the need to define and protect intellectual property. Intellectual property (IP) can be defined in many contexts and somewhat unclearly for writing research. National and inter- national copyright laws govern this area. The APA indicates authorship credit is determined by substantial contribution to a published work (APA, 2010). Primary source references are reports written by the original researcher(s) regarding their work, while secondary source references summarize or quote primary sources (Grove, Gray, & Burns, 2015). Acknowledg- ment of IP can be done through appropriate citations and references.
Ghostwriting, the practice of a professional writer paid to write books, articles, sto- ries, etc. that are credited to someone else, has been identified as an issue (Wnukiewicz- Kozlowska, 2011; Stretton, 2014). Ghostwriters are not acknowledged in writing credits but medical writers are acknowledged. The ghostwriter (a person with no medical education) does not operate as a contributor to content but rather an interpreter of literature, opening the opportunity for misrepresentation of results. Attention to this topic has gained attention. Sug- gestions of using peer reviewers with expertise, closely monitoring the internal and external validity of study data, and noting if data is appropriately interpreted in context to current practices will help isolate a true author from a ghostwriter.
Conflicting Roles Researchers must be aware of and clearly state any economic or commercial interests in a product or service used in a research project. Providing full disclosure of activities, interests, relationships, or conflicts that might have an influence or present a bias should be defined within the research definitions (APA, 2010). This will not always be defined a conflict of inter- est, but without doing this, transparency of the research may never be achieved (APA, 2010).
The commercial contribution to research by industry, corporations, and business cannot be denied. Ethical standards require full disclosure of financial interests. Research and develop- ment is an expensive business and sometimes takes years of work to meet federal safety regula- tions before a product can be delivered safely to the public. The commercial element of research and development has to be allowed to be profitable but not at the expense of monopoly or safety (Moses, Matheson, Cairns-Smith, Palisch, George, & Dorsey, 2015). Government regula- tions have been put in place and carefully maintained through checks and balances to provide safety for the public, fair compensation for the commercial industries, and a somewhat speedy process of development (Holmes et al., 2016). This is an ongoing and ever-developing system.
Practices for Collecting and Protecting Research Data The Office of Research Integrity (ORI), part of the DHHS, identifies eight key components in the practice of responsible data collection, management, and protections. These are data ownership, data collection, data storage, data protection, data retention, data analysis, data sharing, and data reporting (DHHS, 2016).
Data Ownership Although many contributors may work on a research project, the legal rights of ownership for the data resulting from the project may not belong to the researchers. Ownership should be established at the time the project is initiated and can be based on employment and/or funding status. If researchers work for the sponsor of the research, unless otherwise stated, the sponsor
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will be the owner of the data. If the research project is funded by outside organizations, the out- side organizations can also expect ownership of research data. The principal investigator (PI) can also be granted stewardship of project data, providing some level of ownership. In some projects, research subjects may be considered partial owners of project data (DHHS, 2016). Ownership of the research data should be established at the beginning of the project and included in the description of the project that is sent before the IRB. This will provide transparency in the project and identify any potential conflicts of interests (Food and Drug Administration (FDA), 2016).
Data Collection Data collection, in this reference, is the documented process of the project. Providing a frame- work, map, or model of the project design along with the processes used for data collection will help reviewers validate rigor of the project and help future researchers recreate the study (Grove et al., 2015).
Data Storage Data storage has been addressed throughout this chapter. AHRQ now requires a data storage plan as part of project grant applications (AHRQ, 2015). Data storage considerations include the amount of data to be stored/retained and the method of storage (electronic or paper). When considering the data to be stored, the means to recreate the project, notes, relevant observations from the researchers, and relevant statistics should be stored. It may not be necessary to store raw data (DHHS, 2016).
Electronic storage can be accomplished several ways. Funded research projects may have a stipulation that project results become accessible through some form of public or member- ship access (AHRQ, 2015). This has to be considered when storage is determined. Backup storage can also be done with static storage through CDs and removable drives.
Data Protection Protecting stored data can be approached from multiple directions. Data stored in a physical form (paper or on a physical server) should be kept safe from elements and local damage and secured from theft or loss (DHHS, 2016). A backup of this type of stored data is also rec- ommended. Data stored in an electronic format that is open to outside access should also be protected from outside attacks such as virus, worms, and hackers (DHHS, 2016). An offline backup of this type of stored data is recommended.
Data Retention Retention of data can have several dependencies. If a project has been funded or contracted, the terms of the funding/contract may define the period of retention (DHHS, 2016). If there are no dictated time parameters, cost of retention may be the determining factor. Continued storage of confidential information has security risks and can also be a factor in discontinuing storage. If data is determined to be no longer needed for retention, it should be completely destroyed through proven and documented methods (DHHS, 2016).
Data Analysis Methods for data collection are defined in the framework of the research project. Methods for data analysis are not as clearly defined until raw data has been collected and evaluated. Data analysis can include the researcher but also often includes the services of statisticians
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and biostatistical services. The project investigator, researchers, and statisticians need to work together to make sure data is analyzed in the context and scope of the project (DHHS, 2016).
Data Sharing The fields of OCR and TR have developed because of the delay in sharing data. Both OCR and TR expedite the timeline of sharing data by including research at the clinical level. Research that has been funded by outside organizations may have reporting and sharing parameters written into the funding agreements creating an obligation to report (AHRQ, 2015; DHHS, 2016). Commercial and industrial research may be completed under proprietary domain, however, the results will typically be translated into commercial products for consumers.
Data Reporting A vital part of the scientific process is data reporting. Reporting establishes an institution/ organization/researcher’s contribution to a field of study. A published report allows a proj- ect’s outcome to be reviewed for accountability. Published reports also promote discussion and ideas that may lead to additional research (DHHS, 2016).
Supporting Dissemination of Research Findings Dissemination is simply a term that indicates communication of information, in this case research knowledge, from a source (Primary Health Care Research and Information Service, 2016). A facet of the Affordable Care Act (ACA) mandated that research findings would become more accessible to the public, when it was enacted in 2010 (PCORI, 2016). The ratio- nale for this was to allow for better decisions based on available evidence at the levels of patients, providers, caregivers, insurers, and others involved as stakeholders in healthcare.
Patient and public involvement (PPI) in dissemination of research knowledge improves communications between the patient and caregivers, including the risks of different treat- ments (PCORI, 2015). McNichol and Grimshaw (2014) suggested the relationship between research and PPI be encouraged to expand the continuum of scope of dissemination. Pub- licly funded research that fits the ACA mandate is required to submit peer-reviewed pre- liminary findings to the Patient Centered Outcomes Research Institute (PCORI) (Merino & Loder, 2014). A potential downfall of this is that—as this material continues to be analyzed and reported—journal reviewed versions of these results may provide different or expanded results (Merino & Loder, 2014).
Dissemination at the level of the caregiver continues to translate knowledge into prac- tice (Kelly, Wicker, & Martin, 2016). Because translational research has been taken to the clinical level, changes in practice can be quicker. By making research results available pub- licly, stakeholders at the clinical level can identify research activities that would be appro- priate for their institutions. This practice can encourage contribution and participation by having broader access to research outcomes (Kelly et al., 2016). Dissemination of research findings through mandated public access supports this concept of broader access. Hospi- tals that have achieved Magnet Recognition demonstrate quality improvement through research, evidence-based practice, and innovation (American Nurses Credentialing Center, 2016). Research should lead clinical areas in a direction that promotes meaningful change for the patient, caregivers, and organization.
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Patient Centered Outcomes Research network (PCORnet) is a public database of clini- cal effectiveness research (CER) data from publically funded projects. This information is available to the public and to healthcare workers (PCORI, 2015). The goal of this network is to make researching specific topics easier (one location instead of many). PCORnet aggregates research information from hospitals, health plans, and practice-based net- works for funded projects. This project has been divided into phase I and II. As phase II nears completion, funding is expected to be shifted to agencies such as the National institutes of Health, the Food and Drug Administration, and potentially private industry (PCORI, 2015).
Selected Software The quest for a single software application that will search and compile all research has gone unanswered. What has developed or become more specific is the importance of advanced search techniques of current search engines used, such as MEDLINE, CINAHL, PubMed, PsychINFO, and so on. As research projects are designed, submitted, enacted, completed, and published, authors should create keywords for easy search and submit to known databases that are included in medical search engines.
Effecting Practice Change Health information technology (HIT) has been an increasingly active part of healthcare for many years. Until the ACA in 2010, there were few measurable designs to standardize the type of HIT used by healthcare (Murphy, 2010). Since we are a country of private industry, the federal government has made no mandates on specific software systems to purchase and put in place. What have been dictated are the capabilities of electronic health record systems (EHRSs), by setting system standards, capabilities, and accountability (Appari, Johnson, & Anthony, 2013). Through a series of financial incentives that turn into financial penalties as time passes without compliance, eligible providers are requested to meet minimum EHRS compliance. As EHRS compliance achieves a higher level of reportable documentation, reportability occurs, promoting evidence-based data capture.
Prior to the ACA, HIT systems were not always enterprise-wide systems that communi- cated with each other. Free-standing systems in the emergency department, operating room, laboratory, radiology, inventory control, and other areas were not uncommon, making com- munication between these systems unlikely (Lyon et al., 2016). As HIT systems evolved and regulations were written, the push for enterprise-wide systems was felt. An enterprise-wide system is an information system by one vendor that houses modules for all (or most) areas of the facility with the ability to share information between modules. This element is key for promoting patient safety and better patient care.
The Centers for Medicare and Medicaid Services (CMS) developed the meaningful use (MU) criteria that EHRSs must meet to be identified as certifiably capable EHR systems (CMS, 2016). The Office of the National Coordinator for Health Information Technology (ONCHIT) has created a voluntary program for testing and certifying health IT systems. Certified HIT systems, including EHRSs meet the criteria for meaningful use (ONCHIT, 2016). As healthcare facilities come into MU standards, even though they may not be using the same software, the software being used should have the same capabilities.
As eligible providers and facilities (those that take payments from CMS) come on line with MU criteria, the groundwork for standardized, reportable, and evidence-based practice becomes available. From this comes the standardized ability to document, collect, and report
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clinical quality measures (CQMs) (Wilson & Newhouse, 2012). HIT also offers the ability to place clinical decision support (CDS) mechanisms in place based on the patient’s current condition. The ability to exchange electronic health information also becomes available, which aides in the transfer and communication of information (Thurston, 2014).
The ability to effectively change practice at the level of the facility can be supported by several different models of change, which have been previously discussed. The need for change can be identified through multiple mechanisms. Burns and Grove (2011) identified the clinical nurse and nurse researcher as key stakeholders in identifying needs for evidence- based changes in practice. Additional identifiers include financial indicators and quality indi- cators. The necessary elements of change that need to be respected include a culture that is ready and willing to accept change, appropriate stakeholder representation and buy-in, adequate resources and planning, and a comprehensive evaluation-of-change tool.
Future Directions Evidence-based practice is the direct result of research and has a documented effect on the way clinical care is provided. Research can be chronicled through many styles and methods leading to the more efficient outcomes research and translational research used to promote evidence-based practice. The evolution of health information technology has enabled the capabilities of data collection, data aggregation, data reporting, data storage, and data link- age to become more efficient.
Nursing, as a profession, has the ability to change practice, promote education, and influ- ence health policy. This has to be done credibly through steps of evidence-based research. Through nursing research, value has already been demonstrated. Addressed through national patient safety goals, clinical quality measures, and meaningful use criteria, the impact of nursing research has helped identify patient-centered issues with positive outcomes (Appari et al., 2013; The Joint Commission, 2016; CMS, 2016).
Nursing research can influence the impact of cost containment. Although this topic is bigger than nursing alone, nursing research an have impact by providing solid analytical data that supports patient care issues such as re hospitalization within 30 days. Lundmark (2015) indicates cost-benefit questions are an area that helps drive healthcare systems. Nursing research can be an interdisciplinary effort and often includes nurse clinicians, nurse research- ers, nurse informaticists, and nurse leaders (Sousa, Weiss, Welton, Reeder, & Ozkaynak, 2015). The level of credentialing for nursing research has been evaluated by multiple credentialing organizations with a focus on the value of individual nurse certification related to improving patient outcomes (Lundmark, 2015).
Storage of research data has been previously described as public, private, academic, commercial, and in some cases international. There is no one spectacular data repository that houses all data. Retrieval of data is done through search engines and the typical search methods allowed by search engines. The researcher finds catalogued information based on methods of search and the way information is stored. There does not seem to be an alternative for this methodology at this time.
Several methods of quantifying the level of evidence have been identified. Each method has value and solid support in its approach. Researchers are provided guidance on methods of critique to establish validity for research materials if these levels of evidence methods have not been applied.
Multiple models of integration for evidence-based practice have been identified in an over- view fashion. Evidence-based practice is the goal of evidence-based research with a continuing
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quest of improved outcomes for the patient and caregivers. As EBR is implemented, careful documentation of changes should be noted for evaluation, based on the chosen models criteria. The collected data from an EBR implementation can be the beginning of the next EBR project. Changes put in place from EBR can be transmitted to users through presentations, poster proj- ects, and potential journal articles, resulting in an effective dissemination of knowledge. Holsti, et al. (2013) suggested the role of nursing researchers should be developed and encouraged in patient-outcomes research by increasing nursing research programs.
Summary • The time lag between research findings and integration into practice may average
17 years. • Comparative effectiveness research (CER), health services research (HSR), and
outcomes research (OCR) are types of research used in healthcare. • The potential of information technology (IT) to support research and improve
healthcare has been recognized and has given rise to multiple projects resulting in: • The establishment of clinical databases and national registries • Research and dissemination of findings related to improved healthcare outcomes • The establishment of practice-based research networks • Research on how primary-care teams use health information technology • A decrease in the length of time from research completion to dissemination of
results • A key factor in the integration of research into practice is the ability to determine the
strength or level of evidence and to critique the quality of the study. • Levels of evidence refer to the strength of evidence or confidence placed in the find-
ings related to the approach used. Confidence in findings is critical to the process of translating findings into practice. Meta-analysis of multiple controlled studies, systematic reviews, and randomized controlled trials (RCTs) are considered to be high-level evidence resources.
• Evaluation of levels of evidence has been complicated by inconsistencies in scales and factors to evaluate the strength of evidence.
• Information literacy is critical to find the highest levels of evidence. • Knowledge translation (KT) from the point of valid research findings to implemen-
tation at the bedside can take place through many well-documented models. KT requires a culture that is willing to accept a change of practice, facilitation of imple- mentation methods, key stakeholders that include bedside clinicians and administra- tors, and post implementation evaluation.
• Some models for change include the Stetler model, Iowa model of evidence-based practice, ACE star model, and PARIHS framework.
• HIT provides the potential to improve the management of research data inclusive of data management and curation.
• Translational science is an approach that seeks to expedite the move of data gained from the translational research process to the clinician and patient.
• The White House Office of Science and Technology Policy (OSTP) requires that federal agencies make results of federally funded scientific research available to the public, industry, and scientific communities.
• Infrastructure to support research includes staff, budgets and funding, institutional review board approval, project management, appropriate policies and procedures,
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physical space and accommodations to house staff, information technology and data, and partnerships with organizations that have an identified need.
• Handling research data and information must conform to ethical and legal principles that include informed consent, protection of privacy, confidentiality, and intellectual property, and full disclosure of activities, interests, relationships, or conflicts that might influence or bias study results.
• Practices for collecting and protecting research data need to consider the following components: data ownership, data collection, data storage, data protection, data retention, data analysis, data sharing, and data reporting.
• The Affordable Care Act mandated that research findings would become more acces- sible to the public as a means to facilitate better decisions based upon evidence.
• HIT has enabled the capabilities of data collection, data aggregation, data reporting, data storage, and data linkage to become more efficient.
As the informatics nurse specialist at your facility, you are responsible for implement- ing the infrastructure to collect and protect research data. How would you go about implementing the eight key components identified by the Office of Research Integrity at your facility? Where would you start?
About the Author Melody Rose, DNP, RN is a 2014 graduate of Duke University School of Nursing DNP pro- gram, 2011 graduate of Walden University School of Nursing MSN—Informatics Specialty, 1986 graduate of Illinois Central College, Associate Degree of Nursing program. Dr. Rose lives in Murfreesboro, Tennessee, and is an Assistant Professor of Nursing at Cumberland University Jeanette C. Rudy School of Nursing, a Visiting Professor of Nursing at Chamber- lain College of Nursing, and works PRN as a Nursing Supervisor at Southern Hills Medical Center in Nashville, Tennessee.
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Policy, Legislation, and Regulation Issues for Informatics Practice Sunny Biddle, MSN, RN
Jeri A. Milstead, PhD, RN, NEA-BC, FAAN
After completing this chapter, you should be able to:
• Differentiate policy, legislation, and regulation through components, processes, outcomes, and relationship to informatics.
• Contrast how value-based reimbursement differs from pay-for-performance or volume-based delivery models.
• Analyze the implications of health information technology (HIT) and informatics on diagnostic and billing codes.
• Determine points of access for making decisions in the federal rule-making process related to informatics issues.
• Identify the impact of informatics on at least three specific federal regulations.
• Identify the relationship among accreditation decisions, reimbursement, quality of care, and informatics.
• Discuss the pros and cons of working in interprofessional teams related to informatics and public policy-making.
• Discuss ethical issues that impact healthcare providers and organizations in the electronic age.
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Policy, legislation, and regulation are related but separate processes that convey values, ideals, and significance to our lives. Students in the healthcare professions must understand the process of policy making and, especially, their obligation to participate in the process. Governmental laws and rules direct the practice of healthcare workers in every aspect of their scopes of practice. This chapter helps the reader differentiate law from rule and policy from procedure. The reader begins to understand how informatics impacts and influences these processes. Presentation of specific legislation and regulations provides examples that directly affect electronic processes that are fundamental in healthcare delivery.
Although central concepts in this chapter are related to governmental processes, poli- cies that have been developed in the private sector also are presented. Policies from private accreditation organizations such as The Joint Commission (TJC) have enormous impact on reimbursement and continued operation of a healthcare agency. Finally, this chapter examines the recent impact of informatics and policy-making on interprofessional learning and teams, evidence-based care, and ethics.
The Policy Process What Are Policies? Policies are written documents that reflect the values of administrators of organizations. They should and ought to express what the chief executive officers (CEOs) and their inner circles and confidantes believe. Policies cover a variety of components of an organization. For example, policies at the federal level of the United States reflect the scope of the cabinet- level departments such as commerce, defense, health, transportation, etc. Policies indicate the direction that an organization will take and, very importantly, signal where funding will be directed. For example, after the United States was attacked on September 11, 2001, the poli- cies of President George W. Bush and his cabinet were focused on defense, and funding was concentrated on the military. When Barack Obama was running for president, he campaigned on the promise of healthcare for all Americans, and that is where funding appeared.
Policies can be found in mission statements, organizational objectives, strategic plans, and other documents. The process of developing policies is fairly straightforward but is fluid and does not always follow a straight course. That is, since policies mirror leaders’ values, policies will change whenever there is a change in administration. Throughout the processes and components addressed in this chapter, the reader should consider how informatics and the use of data can provide information for decisions that will affect healthcare professionals in their work.
Components Generally, policy-making is simple: take problems to government and obtain a response. We all know that there is much more to it than that. Policy-making is not a sequential, linear process—it is messy and often takes a zig-zag course. There are several components that can be identified for the purpose of analysis: agenda setting, government response (often law and regulation [rule]), program design and implementation, and evaluation. A model developed by Cohen, March, and Olsen (1972) in their early work on organizational choice depicted the policy-making process as resembling a garbage can into which problems are floated, solutions are proposed, and politics sway choices. This classic model holds up
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well today. Getting a problem to the attention of government may not be easy, and an idea can be catapulted onto the policy agenda by a catastrophe (e.g., the attack on the Twin Towers and the Pentagon in 2001) or a change in an indicator (e.g., the emergence of the Zika virus in 2016). The federal agenda is always on the mind of the president and his/her confidantes, so having a relationship with a senior congressperson or senator who has the president’s ear can help bring about a change. In Kingdon’s (1995, 2001) classic research on agenda-setting, he noted that a policy entrepreneur is a person who commits interest, time, and money to moving a proposal forward. This person often keeps a proposal from fading on the agenda.
Government response usually occurs in two ways: legislation and regulation. How a bill becomes a law is a standard discussion in every junior-high or senior-high school in this country. A member (Member) of the House or Senate (remember: the president is CEO of the executive branch, and only members of the legislative branch can attend to legislation) introduces a bill. The bill is given a number and sent to a committee or sub-committee for discussion. This group is significant because they actually determine the extent of interest in the problem. Committees determine whether a bill moves forward or dies. Opponents and proponents are given voice through a series of hearings, proposed language is altered or amended, or ideas from bills may be consolidated into a substitute bill. Remember: legisla- tive staff receive and analyze ideas, talk with staff from other offices and federal agencies, and update the Member on the scope and implications of a problem. Informatics experts, including nurses, physicians, pharmacists, and so on, have a tremendous opportunity to provide relevant research, statistical data, results of surveys, and other facts that staff can share with the Member.
If a senator or representative introduces a bill, the healthcare professionals must monitor the progress, suggest amendments if needed, and continuously inform the Member of new information or changes. Bills can authorize (create) a new program; other bills appropriate money to implement a program. One must watch for unfunded mandates; this is a program that has been authorized, but funds have not been appropriated. Bills have a two-year life span. That is, a legislative session is two years; if a bill has not been voted by both the House and Senate and sent to the president within the legislative session, the bill dies; the process must be repeated in the next legislative session.
Often, a bill designates a program that will address a national problem. Bardach’s (1977) book remains a classic about games played by officials during policy implementation. Many of the games had to do with how funds would be applied. A program may specify eligibility requirements, that is who can participate. For example, if a free school-lunch program is pro- posed, who can take part? Children in all grades? All children or just those in public schools? Those whose parents are below the poverty level? Only those with a disability or other speci- fied condition? Program design can assign the government agency that will be responsible for implementing the program. The choice of agency is a very, very political decision. Does the agency have staff with the skills to carry out the program? Does the agency have too many programs already to take on one more? Does the agency want or need the funding that accompanies the program? Does the agency have a record of successful implementation of other programs? Does this program fit within the agency’s mission? Does this program bring jobs to a state that the Member who championed the bill represents, which could be seen as pork-barreling or unduly influencing funding decisions? Members are elected to represent the US not just their own constituencies. However, since House members are elected for two- year terms, they always are mindful of the folks back home and try to convince constituents that they are working in the constituents’ behalf, which often involves bringing programs and money to their districts.
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Influencing Policies and Legislation through Informatics Politics is the process of persuading a person to accept your perspective and take action on bills, policies, and programs that you support. We all practice politics at some time. Did you ever try to convince your parents to buy you something or allow you to go somewhere that you thought they might not approve? You used political means to make your point. Politics is using communications techniques that come naturally to some and must be learned by others.
Lobbyists are experts at encouraging others to act in a predetermined way. Lobbyists use well-honed communications methods such as active listening, clarification (“Did I hear you say . . . ”), reframing (“Let’s look at it from another perspective”), and even silence. Nurses, physicians, pharmacists, dentists, and psychologists use these methods in their daily practice. Using these skills with legislators and their staff is exactly what lobbying is about. Individuals also can lobby, and healthcare professionals are convincing when they use data from the Member’s own district to personalize a story.
Registered lobbyists are employed by organizations to win favor for a particular bill or political position. Federal lobbyists register with the Clerk of the US House of Representatives and the Secretary of the US Senate and must file reports quarterly that list all gifts, travel, and contributions (Registration with the Clerk, 2016). Lobbyists rely on data from many electronic sources to underpin their work. Informatics specialists can help lobbyists find sources and translate statistical data into relevant meaning for members and their staff.
Policy and program evaluation vary depending on how the law or rule is written and the inclination and expertise of the agency head and staff. Often, evaluation either does not take place or reflects a cursory attempt to justify cost. Data obtained from program outcomes are essential for evaluation. Even a cursory examination of data may establish whether or not a program is judged successful.
Example: US National Health Information Technology Policy
The National Health Information Technology Policy (NHITP) is an example of how public and private-sector policies work together. Through the American Recovery and Reinvestment Act of 2009, HIPAA created the Office of the National Coordinator for Health Information Technology (ONCHIT) under the aegis of the Department of Health and Human Services (DHHS). As a result, two federal advisory committees focused on IT were established: the Health Information Technology Policy Committee (HITPC) to develop a framework for a nationwide infrastructure and the Health Information Technology Standards Committee (HITSC) to develop standards, certification, and implementation strategies. These committees gathered together public and private stakeholders who serve as advisors on how to develop and implement a national HIT system. The National Coordinator presented a report to the Congress in 2016 that highlighted the barriers and challenges facing such an undertaking.
The policy process is visible in this example: the idea was put before the Administra- tion but was not high on the agenda. Legislation was passed but as an unfunded mandate. Implementation so far has been sluggish—due to lack of funding? Other DHHS priorities? Lack of a policy entrepreneur? Problems with the Affordable Care Act (ACA)? For the future, will a crisis move the need for a national infrastructure back on the agenda? Will the Congress a ppropriate funding so that an integrated infrastructure will be developed? Will the advisory committees reach agreement on criteria for standards and certification?
Public policy is incremental. Time is needed to define a problem in a way that policy makers can accept and can begin to visualize possible solutions. Legislation may take several ses- sions, and drafting regulations for a single program may be very complex, especially when it involves other departments. Implementation often takes years, as bureaucrats refine programs so that the outcomes meet the objectives. Evaluation, unless it is built into the program at the beginning, often is either lost or weak. Persistence is the major requirement for working in the policy arena. So stay tuned as ONCHIT evolves.
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Legislation and HIT/Informatics Healthcare informatics and the associated technologies influence and are impacted by national legislation. Changes in reimbursement for healthcare services can support the use of new informatics strategies, such as electronic data interchange (EDI), support for new healthcare delivery models, validate clinical trials and the use of alternative therapies, improve monitor- ing of prescriptive practices, and facilitate provision of burden-of-proof evidence. Accurate and complete generation and transmission of diagnostic and billing codes results from the work of informaticists and HIT.
Reimbursement One of the significant outcomes of legislation involves the issue of reimbursement for ser- vices performed. Two major philosophies guide how organizations and physicians are paid: volume-based and value-based.
VOLUME VERSUS VALUE REIMBURSEMENT For many years, US federal policies set payment to hospitals for certain procedures based on how many procedures were completed in a specified time frame. This is known as volume-based compensation. Some diagnostic tests and types of surgeries were reimbursed at higher rates than others, which led to those being used more often.
Proponents of healthcare reform developed a schema known as value-based reimburse- ment in which diagnostic tests and treatment options were based on the value of those tests and treatments to patient and organizational outcomes. The hope of reformers is to reduce the number of unnecessary or limited-valued tests and treatments. Accountable Care Organi- zations (ACOs) are examples of entities developed across the public-private sectors in which physicians are reimbursed for coordinated care, and hospitals are paid through fee schedules for physicians (as employees) and per diems or diagnostic-related groups (DRGs) (Berenson, Upadhyay, Delbanco, & Murray, 2016a). The goal is to decrease enterprise costs and improve performance targets that measure quality (Berenson, Upadhyay, Delbanco, & Murray, 2016b).
ELECTRONIC DATA INTERCHANGE Data tracking and retrieval are essential in pro- viding healthcare providers (physicians and healthcare organizations) with useful informa- tion. The old system of merely entering paper-based documentation into computers was widespread in the 1970s and 1980s; this highlighted a flaw in both policy and infrastructure when users realized they could not retrieve information that had meaningful use. This term took on major implications in the healthcare industry. For example, a physician may have an e chart for each patient but is unable to access a list of patients with the same diagnoses such as diabetes or congestive heart failure. Many years and much money have produced better systems, but many providers still do not have adequate data exchanges.
The Center for Medicare and Medicaid Services (CMS) has mandated that all providers and insurers submit all claims for reimbursement—for any service or for verification or certification of services—in a consistent format (Department of Health and Human Services, 2014). This order compelled all providers to buy or contract with electronic data interchanges (EDIs) so billing is complete and uniform. When a government agency, such as CMS, prods a lagging healthcare system, this indicates a commitment to reform that has implications for electronic data storage and retrieval and for improved patient care.
HEALTHCARE DELIVERY MODELS The need for accurate and comprehensive data and the principles of healthcare reform have stimulated new models of delivering healthcare. A patient-centered medical home (PCMH) is a model or philosophy that is
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patient-centered, team-based, coordinated, accessible, and focused on quality and safety (Berenson, et al. 2016b). A team approach and a focus on population health differentiate this model from primary care and specialist care. PCMH practices are being evaluated for efficiency and positive health outcomes; data are imperative in this process.
ALTERNATIVE AND EXPERIMENTAL TREATMENTS AND CLINICAL TRIALS Alter- native treatments are those not considered mainstream medical actions and often are not reimbursed by insurance companies. Data may be cited to support a particular treatment; data sources and conclusions must be scrutinized before and during treatment. Developing new treatment approaches and medications takes many years. Clinical trials, a critical element of development, often are supported by research funding, not reimbursement. Accurate and accessible data are absolutely critical during clinical trials. Information systems must capture not only anticipated positive outcomes but also must record trends that may indicate negative patient outcomes. Clinical trials rely on blind, randomized studies and often involve giving patients either a newly developed drug or a placebo. Interpretation of trial results requires astute attention to not only statistical data but to anecdotal data. The latter are qualitative responses from patients, caregivers and family, researchers, and physicians who may note symptoms, reactions, or behavioral responses to the drugs being administered. The informat- ics specialist should be involved very early in the development of the tools to measure and evaluate statistical and anecdotal data. A knowledgeable, experienced informatics specialist can direct clinical teams on which data to collect, how to collect it, and how and when to interpret it. A robust information system can detect outliers and other data points that may guide researchers to slow, halt, or discontinue a clinical trial. Incomplete data or absent veto points may lead to disastrous effects.
PRESCRIPTIVE PRACTICES The federal Prescriptive Drug Monitoring Program (2016) provides tools for states to collect data about prescription practices. Nearly all state agen- cies (usually the Boards of Pharmacy) have developed tracking systems. For example, Ohio politicians have been working with pharmacists, physicians, and advanced-practice nurses to strengthen the Ohio Automated Prescription Reporting System (OARRS), due to widespread drug abuse throughout the state. Tracking drug use and prescriptive practices within a state is not enough when drug trafficking crosses many state borders. Informatics specialists must be part of the team that creates tools to reduce and eliminate this social problem by providing expertise in what data to collect, how to collect, how to archive, how to retrieve, and how to interpret the data. Healthcare professionals know the medical and social effects of drug use and abuse but often do not have the skills to develop information systems that can offer data in ways that provide meaningful use.
BURDEN OF PROOF The need to protect patient confidentiality is always juxtaposed with a need to know certain patient information. Is sharing information about a patient among a team of healthcare professionals (and insurers) in direct opposition to privacy? The provider of services shoulders the burden of proof that all criteria required for reimburse- ment have been met, even those services that were or should have been provided prior to the current practice. For example, in order to obtain reimbursement for treatment in an acute-care hospital, a physician holds responsibility for assuring that a patient has been hospitalized in a freestanding, separate facility before being admitted to a skilled nursing facility. EDI allows patient records to be tracked so the burden of proof can be established. Sharing data in a secure manner will assure better coordination among providers with fewer duplicated services.
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Implications of Diagnostic and Billing Codes for HIT and Informatics Practice Healthcare diagnostic and billing codes can be overwhelming and confusing but are essential to ensuring the success of HIT such as electronic health record systems (EHRSs). Reimbursement for healthcare services is composed of a diagnosis and all of the correspond- ing treatments used to cure, improve, or monitor that disorder. The International Classification of Diseases (ICD) codes are evolving rapidly to adjust to the growing complexity in medical science, technology, society, and healthcare policy (Moriyama, Loy, & Robb-Smith, 2011). An ICD code consists of a combination of numbers and letters (three-to-seven characters) that designate specific diagnoses, and as the number of digits increases, so does the specificity of the diagnosis. In relation to billing and informatics, the ICD-10 codes, the latest upgrade as ruled by the ACA, categorize an injury or illness and describe in detail the cause, anatomical location, and severity. For example, an ICD-10 code for any injury to a shoulder begins with M75; to denote a right-shoulder injury, the code becomes M75.10; and to further classify the injury as nontraumatic, the code becomes M75.101 (Centers for Medicare and Medicaid Ser- vices, 2011). The informatics nurse specialist would be an important asset in analyzing data for healthcare systems.
ACCURACY AND SPECIFICITY The accuracy and specificity of coding directly affects a healthcare organization’s billing and reimbursement. Reimbursement for services is essential for the survival of individual healthcare organizations. Accuracy and specificity of coding ultimately relies on the data entered into an EHRS. If a nurse simply documents that the patient has pain in an arm and does not specify duration, intensity, exact location, mecha- nism of injury, and so on, it becomes increasingly difficult to properly code for a procedure or service, which may lose revenue for the organization. Each piece of data entered into an EHRS should be specific, measurable, and accurate, so it can be compared, audited, and used to show meaningful use. At this point, the informatics nurse would be a valuable asset in training and mentoring personnel on the importance of accurate data entry. This nurse also should be utilized to coach the billers and coders on the nuances of data collection. For example, a misplaced decimal point for a given medical code could have a negative impact on reimbursement. Data collected through EHRSs also assist organizations in increasing quality, safety, and efficiency, which also has an end result of helping healthcare organizations remain open for providing continued care to their communities.
DO-NOT-PAY LIST The Deficit Reduction Act (DRA) of 2006 required that the Centers for Medicare and Medicaid Services (CMS) oversee the Hospital Acquired Conditions-Present on Admission (HAC-POA) Program. The HAC-POA evaluates payment, based on whether there is documented clinical evidence that a condition was present during the hospitalization and if it was noted to be present on admission to the healthcare facility (Snow et al., 2012). If conditions are not properly documented within the EHRS as being present on admission, the condition is assumed to be hospital acquired and, therefore, will most likely fall into the do-not-pay category of CMS reimbursement. Examples of the HAC-POA conditions that CMS has placed on a do-not-pay list include: stage III-and-IV pressure ulcers, falls and trauma, catheter-associated urinary tract infection, vascular-catheter associated infection, surgical-site infection, and deep-vein thrombosis (Centers for Medicare and Medicaid Services, 2015a).
Impact of HIT System Usability Usability is a term that refers to the quality of a user’s experience when interacting with a product, system, software, or other applications. Usability is about effectiveness,
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efficiency, and overall user satisfaction (Department of Health and Human Services, 2016a). HIT has offered some benefits for healthcare providers, payers, and businesses but still has usability faults that may have negative consequences. Currently, no federal regulation mandates that usability guidelines must be followed when developing new software (Reider, 2014). EHRS-related errors, such as data being lost, incorrectly entered, displayed or transmitted, leads to a loss of integrity and potential errors, harm, or even death (Bowman, 2013). In cases where HIT usability is inadequate, informatics nurse spe- cialists could offer expert knowledge for redesigning software to obtain more meaningful use from the data.
Regulation (Rule-Making) and Implications for Informatics Introduction: The Federal Regulatory Process The legislative and regulatory processes are parallel and powerful methods of establishing laws and implementing them through rules (Loversidge, 2016). Federal agencies write rules in an orderly manner. First, a notice of proposed rulemaking (NPR) is published in the Federal Register (FR). The FR is published daily, except for federal holidays. The NPR includes the name of the agency, the language (i.e., a narrative of the intended rule), contacts, and deadline dates for receiving comments.
Comments from the public are invited in the form of phone calls, face-to-face meetings with staff, letters, faxes, and emails. The agency must review all comments, respond to each inquiry, and consider seriously each suggestion. Changes to the proposed rule are noted in the FR as a notice of final rule (NFR). There still are opportunities to amend rules, even after the final rule is published. New data, re-interpretation of data, a change in philosophy about a program, or a change in administration can prompt changes in programs. All changes must go through the original process. The openings for inserting comments are numerous; there are hundreds of agencies and many proposals, so it is imperative to review the FR frequently.
Impact of Specific Rules HIT use has grown exponentially over the past decade in response to new policies, laws, and regulations for healthcare reform. It is important to understand that each entity listed below has been instrumental in changing how healthcare organizations utilize patient data. While the overall goal of each rule is to improve quality, safety, and efficiency, each rule addresses a different facet of HIT reform.
THE MEDICARE ACCESS AND CHIP REAUTHORIZATION ACT OF 2015 The Medicare Access and CHIP Reauthorization Act of 2015 (MACRA) changed the way providers are reimbursed for services rendered to Medicare beneficiaries. Prior to this reform, healthcare providers were paid according to the sustainable growth rate (SGR) provision, which was estimated by four factors including: percentage change in physician-service fees, percentage change in the average number of Medicare beneficiaries served, an estimated 10-year average- percentage change in real gross domestic product, and percentage of change in expendi- tures due to changes in healthcare policy. MACRA ended the SGR, created a new framework to reward healthcare providers for providing quality care, and combined existing quality- reporting programs into one system. The goal of MACRA is to drive healthcare reform toward providing improved reimbursement for care, based on value and quality rather than quantity.
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The MACRA quality payment program consists of the merit-based incentive payment system (MIPS) and alternative payment models (APMs). MIPS is a new program that combines other incentive programs, such as the physician quality reporting system (PQRS) and the Medicare EHRS, that will be measured based on quality, resource use, clinical-practice improvement, and meaningful use of EHRS technology. APMs offer new ways in which providers will be paid for the services they provide to Medicare beneficiaries. Two payment methods include lump-sum incentives that will begin in 2019 and increased annual payment rates for providers utilizing APMs, beginning in 2026. Currently, provider enrollment is completely voluntary, but the implications of MACRA are vast. Providers who practice under the MACRA and show quality improvement through meaningful use of their electronic health-information technologies will be compensated accordingly, which will elevate the individual success of these providers and the healthcare systems for which they work (Centers for Medicare and Medicaid Services, 2016a).
MACRA regulations were published in the Federal Register in May 2016. (Don’t let the 900 pages of the NPR put you off—it is organized into sections that address EHRSs, reimburse- ment, etc.) All comments were accepted and the final rule was released in November 2017. This was an example of a real-life opportunity for informatics nurses to comment on and possibly improve a rule.
THE HEALTH INSURANCE PORTABILITY AND ACCOUNTABILITY ACT The Health Insurance Portability and Accountability Act (HIPAA) of 1996 required the Department of Health and Human Services (DHHS) to develop regulations protecting the privacy and secu- rity of designated health information. HIPAA includes a privacy rule and a security rule. The privacy rule established national standards for protecting health information, and the security rule established national security standards for protecting health information being held or transferred in electronic form (Department of Health and Human Services, 2016b). A major goal of the security rule is to protect individuals’ health information while ensuring that the healthcare workforce has secure access to that same information through portable devices, such as cell phones, tablets, and/or computers. A key tenet is that protected health information (PHI) must not be disclosed to unauthorized persons. HIPAA violations within healthcare organizations can result in disciplinary action, fines, civil and criminal action, and even termination.
ESIGNATURE As HIT becomes a larger part of healthcare, questions arise regarding the acceptability of electronic signatures (esignature). What is an esignature? Are there federal regulations regarding esignatures? What public policies are there in place to guide healthcare organizations in implementing esignatures? Is the process of electronic signatures valid or acceptable? Electronic signatures are becoming standard in healthcare documentation as the use of EHRSs continues to grow. Healthcare staff are required to log in to their computers with specific user names and passwords that are reset multiple times throughout a year. This log-in can also be used to verify orders entered into a patient’s health record, sign a note made by a provider, or authenticate prescriptive entries. Another form of esignatures includes the use of biometrics. Many EHRSs utilize biometric fingerprinting to place orders, administer medications, or verify complete documentation to a record. It is important that healthcare workers keep their log-in information private, completely log out of their computers when not in use, and periodically change their passwords in order to increase the validity and acceptability of the EHRS, including their esignatures (Downing, 2013). Although the Food and Drug Administration (FDA) accepts signatures that are written, scanned, or digitalized, the informatics nurse should be familiar with the FDA regulations for updates regarding electronic signatures.
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MEDICARE IMPROVEMENTS FOR PATIENTS AND PROVIDERS ACT In 2008, Con- gress enacted the Medicare Improvements for Patients and Providers Act (MIPPA), which contained several provisions that changed the Medicare program and allocated federal funding for state-based assistance programs directed at providing outreach to low-income Medicare beneficiaries (Community Living Administration, 2015). Low-income Medicare beneficiaries tend to be sicker than those on the higher end of the Medicare spectrum and often require more services for their treatment. Informatics played a role in MIPPA as it changed the way federal and state agencies shared data. Prior to MIPPA, there was limited coordination between the federal Social Security Administration (SSA), which determines low-income eligibility, and state Medicaid agencies. There are still many hurdles to conquer to streamline the data sharing between the federal and state entities, but since the enactment of MIPPA, millions of individuals have been enrolled into the low-income-subsidy group. The data collected has enabled low-income beneficiaries to gain access to local support agencies, utilize statewide assistance programs, and have greater access to healthcare.
THE AMERICAN RECOVERY AND REINVESTMENT ACT The American Recovery and Reinvestment Act (ARRA) was enacted in 2009 as an attempt to revive the nation’s economy, create jobs, and address widely neglected challenges that impact the future. Many of the provisions to the ARRA were directed toward improving healthcare such as reduced COBRA health-insurance premiums, a tax credit for health coverage to encourage greater compli- ance, and federal funding or incentives for healthcare organizations to upgrade HIT systems. In 2011, healthcare systems began the transition from paper documentation to electronic documentation with the intent to show meaningful use and receive incentives for successful EHRS adoption. Organizations that adopted EHRSs were given funding for the system itself, for staff education, and for evaluation tools to ensure successful implementation. EHRSs have enabled providers to connect, share information, evaluate patient data, and work as a team to obtain higher quality health outcomes for each patient (Adler-Milstein, Everson, & Lee, 2015; Centers for Medicare and Medicaid Services, 2009; Cresswell, Bates, & Sheikh, 2016; Tharmalingam, Hagens, & Zelmer, 2016; Yanamadala, Morrison, Curtin, McDonald, & Hernandez-Boussard, 2016).
The Health Information Technology for Economic and Clinical Health (HITECH) Act is a provision of the ARRA that aimed to ensure that healthcare organizations were not only adopting EHRSs but also validating their implementation by showing meaningful use. HITECH included approximately two billion dollars in funding for loans, grants, EHRS adop- tion, technical assistance in process implementation, workforce training, and new technology research and development (Centers for Disease Control and Prevention, 2016). Healthcare systems were faced with a momentous job of planning for new technologies, establishing training programs, ensuring competency of basic computer skills, implementing a go-live date once training was complete, and transferring healthcare information from paper docu- mentation into the EHRS. HIT has changed every aspect of healthcare. No longer will a patient room be without some type of electronic device, or staff be without mobile devices equipped with HIT applications, or patients be without immediate access to their healthcare charts through the use of electronic health websites. As new information technologies are implemented within healthcare systems, the specifics of the HITECH Act may change in order to evolve with the technology.
AFFORDABLE CARE ACT In 2010, the Affordable Care Act (ACA) became one of the most significant healthcare-reform acts in the US (Rak & Coffin, 2013). One of the many goals of the ACA was to improve healthcare quality through the use of information technology. The ACA
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also aimed to ensure affordable healthcare by reducing the cost and increasing the number of individuals insured. The ACA was instrumental in ensuring that healthcare organizations implemented EHRSs and that these records could provide meaningful use to improve upon quality, safety, and efficiency. As the ACA implements new types of healthcare reform, clinical informatics will be needed to measure quality and compliance. Clinical informatics works to assess specific treatment options by providing visibility into how each patient responds to different approaches. The visible data, stored within electronic devices, then can be used to uncover areas for improvement. Clinical informatics provides the power to evaluate data and make necessary improvements to comply with the quality and safety standards set forth by the ACA.
MEDICAID Medicaid is administered by states, according to federal requirements. Medicaid provides health coverage to millions of Americans including low-income adults, children, pregnant women, the elderly, and those living with a disability (Centers for Medicare and Medicaid Services, 2016b). Each state manages the Medicaid provider payment that is usually based on fee-for-service rates or managed care arrangements. Approximately 70% of indi- viduals receiving Medicaid are served through managed care, which means that providers are paid monthly based on specific trending factors and rates. Where informatics is concerned, healthcare providers must be aware of the continually changing supply costs, trending costs for services, advanced technology fees, and coding upgrades in order to produce both higher quality outcomes and increase revenue for the organization.
Accreditation Legislation and regulation are government (i.e., public) responses to problems. Accreditation agencies, most of which are from the private-sector, also have impact on healthcare. These agencies develop national standards through which reimbursement and quality of care are determined. Documentation is so extensive that robust electronic information systems are required to capture data and issue prompts when standards are not met. Making sure that healthcare agencies and providers document correctly and in a timely manner mandates that a team of billers, coders, programmers, data analysts, and nursing and fiscal officers are competent in their skills and knowledge.
Standards address privacy and confidentiality, uniform definitions, training of users, integration of clinical systems, direct patient information, and comparative data. There are many accreditation agencies but all are intended to help the healthcare agencies keep track of the quality of care provided, patients’ and care providers’ satisfaction, and safety of care. Public reports are made and the reputation of healthcare organizations is measured against the reports. Organizations with highly rated scores tend to attract qualified professionals who are eager to practice in such organizations.
Seeking accreditation is lengthy and time-consuming. Much effort is expended in com- piling data and evaluating internal processes. Most organizations provide a mock accredita- tion visit prior to an actual visit. This allows all employees to be knowledgeable about what is in the application. Every employee is expected to be able to speak with an accreditation visitor and answer inquiries about the services. Although this expectation puts pressure on individuals, the organization can be assured that it is prepared for the visit. Decisions from the accreditation agency are sent in writing, and the organization has a time frame in which to respond. Accreditation is issued for a specified time and can be re-issued if requirements are met.
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Nongovernmental Agencies THE JOINT COMMISSION The Joint Commission (TJC) is an independent, not-for-profit organization that accredits and certifies approximately 21,000 hospitals and other healthcare organizations nationwide (TJC, 2016). TJC accreditation and certification is recognized as a symbol of quality and reflects an organization’s commitment to meeting or exceeding iden- tified performance standards. TJC aims to accredit those facilities working to continuously provide and value safe, effective, high-quality care.
Informatics plays an important role during the accreditation process. TJC members often conduct interviews with frontline staff; complete an on-site visit; and review safety data, inci- dent reports, and adherence to quality standards set by the institution along with many other data sets. This data is only as accurate as the information input into the system. For example, if a safety measure is to perform safety time-outs prior to every surgical procedure and the time-outs are not documented or are documented incorrectly, even one time, the surgical department falls out on that metric and may not pass accreditation.
HEALTHCARE FACILITIES ACCREDITATION PROGRAM The Healthcare Facilities Accreditation Program (HFAP) is authorized by CMS to survey all hospitals for compliance with Medicare Conditions of Participation and Coverage (Centers for Medicare and Medicaid Services, 2015b). The goal of HFAP is to meet or exceed the expectations set forth for CMS compliance. The accreditation process consists of an application, survey, deficiency report, plan of corrective action, and accreditation action. As with TJC, HFAP also conducts an onsite survey that consists of inspecting the facilities, interviewing employees, and reviewing medi- cal records. Inconsistencies or lack of documentation can impede an organizations ability to become accredited which could lead CMS to withdraw reimbursement.
THE ACCREDITATION COMMISSION FOR HEALTHCARE The Accreditation Commis- sion for Healthcare (ACHC) is a nongovernmental, proprietary corporation established to ensure high-level, clearly written standards for in-home aide services (Tavenner, 2015). ACHC is responsible for accrediting healthcare specialties such as home health, hospice, pharmacies, and behavioral-health facilities. ACHC is also a deeming authority for CMS that means that CMS has granted ACHC the power to determine the accreditation guidelines and status for those organizations noted. ACHC must ensure that specific conditions are met, standards are being set and achieved, and that high quality care is being provided. Complete, real-time documentation, whether it is paper or electronic, is essential for providing evidence of adher- ence to the standards set by the ACHC and CMS. The informatics nurse specialist is expertly prepared to document and oversee documentation of others.
AMERICAN NURSES CREDENTIALING CENTER MAGNET PROGRAM The American Nurses Credentialing Center (ANCC) is a member of ANA Enterprise, which is the overarch- ing organization for the American Nurses Association, American Nurses Foundation, and the American Nurses Credentialing Center (ANA Enterprise, 2016). The ANCC’s mission is to promote excellence in nursing and healthcare globally through credentialing programs (American Nurses Credentialing Center, 2016). ANCC programs certify and recognize health- care organizations for promoting safe, positive work environments, along with several other areas of recognition. The ANCC is of particular interest because the ANCC Magnet Recogni- tion Program recognizes healthcare organizations for quality patient care, nursing excellence, and innovation in nursing practice. Magnet hospitals are famous for meeting and exceeding high standards for safety, quality, and efficiency. Magnet recognition empowers staff to take ownership of their work and to go beyond simply doing a job.
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