Infs discussion 6

Health Care

Information Systems

Health Care

Information Systems

A Practical Approach for Health

Care Management

Fourth Edition

Karen A. Wager

Frances Wickham Lee

John P. Glaser

Cover design by Wiley

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Contents

Tables, Figures, and Exhibits …………………………………………………………………….. xi

Preface …………………………………………………………………………………………………. xv

Acknowledgments ……………………………………………………………………………….. xxiii

The Authors ………………………………………………………………………………………… xxv

Part 1 Major Environmental Forces That
Shape the National Health Information
System Landscape ………………………………………………. 1

1 The National Health Information
Technology Landscape …………………………………………………………… 3

Learning Objectives
1990s: The Call for HIT
2000–2010: The Arrival of HIT
2010–Present: Health Care Reform and the Growth of HIT
Summary
Key Terms
Learning Activities
References

2 Health Care Data …………………………………………………………………. 21

Learning Objectives
Health Care Data and Information Defi ned
Health Care Data and Information Sources
Health Care Data Uses
Health Care Data Quality
Summary
Key Terms
Learning Activities
References

3 Health Care Information Systems …………………………………………. 65

Learning Objectives
Review of Key Terms
Major Health Care Information Systems
History and Evolution
Electronic Health Records
Personal Health Records
Key Issues and Challenges

v

vi · C O N T E N T S

Summary

Key Terms

Learning Activities

References

4 Information Systems to Support Population
Health Management ……………………………………………………………. 99
Learning Objectives
PHM: Key to Success
Accountable Care Core Processes
Data, Analytics, and Health IT Capabilities and Tools
Transitioning from the Record to the Plan
Summary
Key Terms
Learning Activities
References

Part 2 Selection, Implementation, Evaluation, and
Management of Health Care Information
Systems ………………………………………………………….. 139
5 System Acquisition …………………………………………………………….. 141

Learning Objectives
System Acquisition: A Defi nition
Systems Development Life Cycle
System Acquisition Process
Project Management Tools
Things That Can Go Wrong
Information Technology Architecture
Summary
Key Terms
Learning Activities
References

6 System Implementation and Support …………………………………. 179

Learning Objectives
System Implementation Process
Managing Change and the Organizational Aspects
System Support and Evaluation
Summary
Key Terms
Learning Activities
References

7 Assessing and Achieving Value in Health Care
Information Systems ………………………………………………………….. 215
Learning Objectives
Definition of IT-Enabled Value

C O N T E N T S · vii

The IT Project Proposal

Ensuring the Delivery of Value

Analyses of the IT Value Challenge

Summary

Key Terms

Learning Activities

References

8 Organizing Information Technology Services ………………………. 251

Learning Objectives
Information Technology Functions
Organizing IT Staff Members and Services
In-House versus Outsourced IT
Evaluating IT Effectiveness
Summary
Key Terms
Learning Activities
References

Part 3 Laws, Regulations, and Standards That
Affect Health Care Information Systems ………….. 285

9 Privacy and Security …………………………………………………………… 287

Learning Objectives
Privacy, Confidentiality, and Security Defi ned
Legal Protection of Health Information
Threats to Health Care Information
The Health Care Organization’s Security Program
Beyond HIPAA: Cybersecurity for Today’s Wired Environment
Summary
Key Terms
Learning Activities
References

10 Performance Standards and Measures ………………………………… 323

Learning Objectives
Licensure, Certification, and Accreditation
Measuring the Quality of Care
Federal Quality Improvement Initiatives
Summary
Key Terms
Learning Activities
References

11 Health Care Information System Standards ………………………… 357

Learning Objectives

HCIS Standards Overview

Standards Development Process

viii · C O N T E N T S

Federal Initiatives Affecting Health Care IT Standards
Other Organizations Influencing Health Care IT Standards
Health IT Standards
Vocabulary and Terminology Standards
Data Exchange and Messaging Standards
Health Record Content and Functional Standards
Summary
Key Terms
Learning Activities
References

Part 4 Senior-Level Management Issues Related
to Health Care Information Systems
Management ………………………………………………….. 393

12 IT Alignment and Strategic Planning ………………………………….. 395

Learning Objectives
IT Planning Objectives
Overview of Strategy
The IT Assest
A Normative Approach to Developing Alignment and IT Strategy
IT Strategy and Alignment Challenges
Summary
Key Terms
Learning Activities
References

13 IT Governance and Management ……………………………………….. 427

Learning Objectives
IT Governance
IT Budget
Management Role in Major IT Initiatives
IT Effectiveness
The Competitive Value of IT
Summary
Key Terms
Learning Activities
Notes
References

14 Health IT Leadership Case Studies ………………………………………. 467

Case 1: Population Health Management in Action
Case 2: Registries and Disease Management in the PCMH
Case 3: Implementing a Capacity Management

Information System
Case 4: Implementing a Telemedicine Solution
Case 5: Selecting an EHR For Dermatology Practice
Case 6: Watson’s Ambulatory EHR Transition

C O N T E N T S · ix

Case 7: Concerns and Workarounds with a Clinical
Documentation System

Case 8: Conversion to an EHR Messaging System
Case 9: Strategies for Implementing CPOE
Case 10: Implementing a Syndromic Surveillance System
Case 11: Planning an EHR Implementation
Case 12: Replacing a Practice Management System
Case 13: Implementing Tele-psychiatry in a Community Hospital

Emergency Department
Case 14: Assessing the Value and Impact of CPOE
Case 15: Assessing the Value of Health IT Investment
Case 16: The Admitting System Crashes
Case 17: Breaching The Security of an Internet Patient Portal
Case 18: The Decision to Develop an IT Strategic Plan
Case 19: Selection of a Patient Safety Strategy
Case 20: Strategic IS Planning for the Hospital ED
Case 21: Board Support for a Capital Project
Supplemental Listing of Related Case Studies and Webinars

Appendixes
A. Overview of the Health Care IT Industry …………………………….. 525

The Health Care IT Industry

Sources of Industry Information

Health Care IT Associations

Summary

Learning Activities

References

B. Sample Project Charter, Sample Job Descriptions,
and Sample User Satisfaction Survey ………………………………….. 539
Sample Project Charter

Sample Job Descriptions

Sample User Satisfaction Survey

Index ………………………………………………………………………………………………….. 559

Tables, Figures, and Exhibits

TABLES

1.1 Stages of Meaningful Use ……………………………………………………….. 9

1.2 Differences between Medicare and Medicaid EHR

incentive programs ……………………………………………………………….. 11

1.3 MIPS performance categories…………………………………………………..13

2.1 Ten common hospital statistical measures ………………………………….47

2.2 Terms used in the literature to describe the fi ve common

dimensions of data quality ……………………………………………………..52

2.3 Excerpt from data dictionary used by AHRQ surgical site infection

risk stratifi cation/outcome detection …………………………………………56

3.1 Common types of administrative and clinical information systems ….68

3.2 Functions defining the use of EHRs ………………………………………….76

3.3 Sociotechnical dimensions ………………………………………………………92

4.1 Key attributes and broad results of current ACO models …………….. 106

5.1 Sample criteria for evaluation of RFP responses ……………………….. 161

7.1 Financial analysis of a patient accounting document

imaging system …………………………………………………………………..227

7.2 Requests for new information system projects ………………………….. 230

9.1 HIPAA violation categories …………………………………………………… 302

9.2 Top ten largest fines levied for HIPAA violations as of

August 2016 ………………………………………………………………………. 303

9.3 Resources for conducting a comprehensive risk analysis …………….. 309

9.4 Common examples of vulnerabilities and mitigation strategies …….. 310

10.1 2015 approved CMS accrediting organizations …………………………..329

10.2 Major types of quality measures …………………………………………….336

10.3 Excerpt of CQMs for 2014 EHR Incentive Programs ……………………338

10.4 MIPS performance categories…………………………………………………349

11.1 Relationships among standards-setting organizations …………………. 361

11.2 Excerpt from CVX (clinical vaccines administered) …………………….374

11.3 Excerpt from NCPDP data dictionary ……………………………………… 380

11.4 X12 TG2 work groups …………………………………………………………. 381

11.5 Excerpt from the HL7 EHR-S Functional Model …………………………386

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xii · T A B L E S , F I G U R E S , A N D E X H I B I T S

12.1 IT initiatives linked to organizational goals ………………………………397

12.2 Summary of the scope of outpatient care problems …………………… 402

12.3 Assessment of telehealth strategic opportunities ……………………….. 413

12.4 Summary of IT strategic planning ………………………………………….. 414

13.1 Target increases in an IT operating budget ……………………………….442

14.1 List of cases and corresponding chapters …………………………………469

A.1 IT interests of different health care organizations ………………………526

A.2 Health care provider market: NAICS taxonomy …………………………527

A.3 Changes in application focus resulting from changes

in the health care business model ………………………………………….528

A.4 Major health care IT vendors, ranked by revenue ……………………… 530

B.1 Revision history …………………………………………………………………. 541

B.2 Issue management ………………………………………………………………549

FIGURES

1.1 Milestones for a supportive payment and regulatory environment ….15

2.1 Health care data to health care knowledge …………………………………23

2.2 Sample EHR information screen ………………………………………………33

2.3 Sample EHR problem list ……………………………………………………….34

2.4 Sample EHR progress notes …………………………………………………….34

2.5 Sample EHR lab report …………………………………………………………..35

2.6 Sample heart failure and hypertension query screen …………………….45

3.1 History and evolution of health care information systems

(1960s to today) …………………………………………………………………..70

3.2 Sample drug alert screen ………………………………………………………..73

3.3 Sample patient portal …………………………………………………………….74

3.4 Percent of non-federal acute care hospitals with adoption of at

least a basic EHR with notes system and position of a certifi ed

EHR: 2008–2015 ……………………………………………………………………75

3.5 Office-based physician practice EHR adoption since 2004 ……………..77

3.6 The ONC’s roadmap to interoperability ……………………………………..84

4.1 Percent of nonfederal acute care hospitals that electronically

exchanged laboratory results, radiology reports, clinical care

summaries, or medication lists with ambulatory care providers

or hospitals outside their organization: 2008–2015 ……………………. 118

5.1 Systems development life cycle ………………………………………………144

5.2 System usability scale questionnaire ……………………………………….163

5.3 Cost-benefi t analysis ……………………………………………………………164

5.4 Example of a simple Gantt chart ……………………………………………167

T A B L E S , F I G U R E S , A N D E X H I B I T S · xiii

6.1 Project timeline with project phases ……………………………………….189

7.1 IT investment portfolio …………………………………………………………237

7.2 Days in accounts receivable ………………………………………………….239

7.3 Digital intensity versus transformation intensity ………………………..246

8.1 IT organizational chart: Large health system …………………………….257

10.1 Screenshot from NQF ………………………………………………………….. 341

10.2 Projected timetable for implementation of MACRA ……………………. 350

12.1 Overview of IT strategy development ……………………………………… 400

12.2 IT initiative priorities ………………………………………………………….. 415

12.3 IT plan timetable and budget ……………………………………………….. 416

12.4 Hype cycle for emerging technologies, 2014 …………………………….. 422

13.1 IT budget decision-making process …………………………………………443

13.2 Gross margin performance differences in high IT–use industries ….. 461

13.3 Singles and grand slams ……………………………………………………….463

EXHIBITS

2.1 Excerpt from ICD-10-CM 2016 ………………………………………………….38

2.2 Excerpt from ICD-10 PCS 2017 OCW …………………………………………40

2.3 Patient encounter form coding standards …………………………………..41

5.1 Overview of System Acquisition Process …………………………………. 147

9.1 Sample release of information form ………………………………………..294

9.2 Cybersecurity framework core ………………………………………………. 318

10.1 Medical Record Content: Excerpt from South Carolina Standards

for Licensing Hospitals and Institutional General Infi rmaries ……….326

10.2 Medical Record Content: Excerpt from the Conditions of

Participation for Hospitals …………………………………………………….328

11.1 Excerpt from ONC 2016 Interoperability Standards Advisory ………..366

11.2 X12 5010 professional claim standard……………………………………… 382

12.1 IT initiatives necessary to support a strategic goal for a provider …. 410

12.2 IT initiatives necessary to support a strategic goal for a

health plan ……………………………………………………………………….. 411

12.3 System support of nursing documentation ………………………………. 412

In memory of our colleague Andy Pasternack

Preface

Health care delivery is in the early stages of a profound shift in its core strat­
egies, organization, financing, and operational and care processes.

Reactive sick care is being replaced by proactive efforts to keep people
well and out of the hospital. Fragmented care delivery capabilities are being
supplanted by initiatives to create and manage cross-continuum systems
of care. Providers that were rewarded for volume are increasingly being
rewarded for quality and effi ciency.

New forms of reimbursement, such as bundles and various types of cap­
itation, are causing this shift. To thrive in the new era of health care delivery,
providers are creating health systems, such as accountable care organizations,
that include venues along the care spectrum.

In addition providers are introducing new processes to support the
need to manage care between encounters, keep people healthy, and ensure
that utilization is appropriate. Moreover, as reimbursement shifts to incent-
improved provider performance these organizations will have a common
need to optimize operational efficiency, improve financial management, and
effectively engage consumers in managing their health and care.

These changes in business models and processes follow on the heels of
the extraordinary increase in electronic health record adoption spurred by
the Meaningful Use program of the US federal government.

On top of a foundation of electronic health records, the industry will add
population health management applications, systems that support extensive
patient engagement, broader interoperability, and more significant use of
analytics. Providers involved in patient care will need immediate access to
electronic decision-support tools, the latest relevant research findings on a
given topic, and patient-specific reminders and alerts. Health care executives
will need to be able to devise strategic initiatives that take advantage of access
to real-time, relevant administrative and clinical information.

In parallel with the changes in health care, information technology (IT)
innovation continues at a remarkable pace. The Internet of Things is creating
a reality of intelligent homes, cars, and equipment, such as environmental
sensors and devices attached to patients. Social media use continues to grow

xv

xvi · P R E F A C E

and become more sophisticated and capable. Mobile personal devices have
become the device of choice for personal and professional activities. Big data
has exceptional potential to help identify new diagnostic and therapeutic
algorithms, conduct most market surveillance, and assess the comparative
effectiveness of treatments.

For providers to prosper in this new era they must be very effective in
developing IT strategies, implementing the technology, and leveraging the
technology to improve organizational performance. They must understand
the nature of health care data and the challenges of privacy and security.
Clinicians and managers must appreciate the breadth of health care IT and
emerging health care IT trends.

The transformation of the health care industry means that IT is no longer
a necessary back-office evil—it is an essential foundation if an organization is
to survive. That has not been true in the past; provider organizations could
do quite well in a fee-for-service world without computerized physician order
entry and other advanced IT applications.

Having ready access to timely, complete, accurate, legible, and rele­
vant information is critical to health care organizations, providers, and
the patients they serve. Whether it is a nurse administering medication to
a comatose patient, a physician advising a patient on the latest research
findings for a specific cancer treatment, a billing clerk filing an electronic
claim, a chief executive officer justifying to the board the need for build­
ing a new emergency department, or a health policy analyst reporting on
the cost-effectiveness of a new prevention program to the state’s Medicaid
program, each individual needs access to high-quality information with
which to effectively perform his or her job.

The need for quality information in health care, already strong, has
never been greater, particularly as this sector of our society strives to provide
quality care, contain costs, and ensure adequate access.

PURPOSE OF THIS BOOK

The purpose of this book is to prepare future health care executives with
the knowledge and skills they need to manage information and information
systems technology effectively in this new environment. We wrote this book
with the graduate student (or upper-level undergraduate student) enrolled in
a health care management program in mind.

Our definition of health care management is fairly broad and includes
a range of academic programs from health administration, health infor­
mation management, and public health programs to master of business

P R E F A C E · xvii

administration (MBA) programs with an emphasis in health to nursing
administration and physician executive educational programs. This book
may also serve as an introductory text in health informatics programs.

The first (2005), second (2009), and third (2013) editions have been
widely used by a variety of health care management and health information
systems programs throughout the United States and abroad. Although we
have maintained the majority of the chapters from the third edition, this
edition has gone through significant changes in composition and structure
reflecting feedback from educators and students and the need to discuss
topics such as population health and recent changes in payment reform ini­
tiatives. We have removed the section on the international perspective on
health care information technology and updated the case studies of organi­
zations experiencing management-related information system challenges. We
also added a new chapter on the role of information systems in managing
population health.

ORGANIZATION OF THIS BOOK

The chapters in this book are organized into four major parts:

• Part One: “Major Environmental Forces That Shape the National
Health Information System Landscape” (Chapters One through Four)

• Part Two: “Selection, Implementation, Evaluation, and Management of
Health Care Information Systems” (Chapters Five through Eight)

• Part Three: “Laws, Regulations, and Standards That Affect Health
Care Information Systems” (Chapters Nine through Eleven)

• Part Four: “Senior-Level Management Issues Related to Health
Care Information Systems Management” (Chapters Twelve through
Fourteen)

In addition Appendix A provides an overview of the health care IT indus­
try. Appendix B provides a compendium of a sample project charter, sample
job descriptions, and a sample user satisfaction survey.

The purpose of Part One (“Major Environmental Forces That Shape
the National Health Information System Landscape”) is to provide the
reader with the foundation needed for the rest of the book. This foun­
dation includes an overview of the major environmental forces that are
shaping the national health IT landscape, such as Medicare’s alternative
payment programs. The reader will gain insight into the different types
of clinical, administrative, and external data used by health care provider

xviii · P R E F A C E

organizations. Additionally, the reader will gain an understanding of the
adoption, use, and functionality of health care information systems with
focus on electronic health records (EHRs), personal health records (PHRs),
and systems need to support population health management (e.g., data
analytics, telehealth).

Specifically Part One has four chapters:

• Chapter One: National Health Information Technology Landscape. This
chapter discusses the various forces and activities that are shaping
health information systems nationally. The chapter reviews the
HITECH Act, the Affordable Care Act, HIPAA, and national efforts to
advance interoperability.

• Chapter Two: Health Care Data. This chapter examines the range
of health care data and issues with data quality and capture. This
examination is conducted from a cross-continuum, health system
perspective.

• Chapter Three: Health Care Information Systems. This chapter provides
an overview of clinical and administrative information systems. The
chapter focuses on the electronic health record and personal health
record and describes in greater detail the major initiatives that have
led to current adoption and use of EHRs by hospitals and physician
practices (e.g., Meaningful Use and health information exchanges).
The chapter also includes discussion on the state of EHRs in settings
across the care continuum (e.g., behavioral health, community care,
long-term care). It concludes with a discussion on important health
care information system issues including interoperability, usability,
and health IT safety.

• Chapter Four: Information Systems to Support Population Health
Management. This is a new chapter. Its purpose is to focus on the key
data and information needs of health systems to effectively manage
population health. Key topics include population health, telehealth,
patient engagement (including social media), data analytics, and
health information exchange (HIE).

The purpose of Part Two (“Selection, Implementation, Evaluation, and
Management of Health Care Information Systems”) is to provide the reader
with an overview of what is needed to effectively select, implement, evaluate,
and manage health care information systems. This section discusses issues
mid- and senior-level managers are likely to encounter related to managing

P R E F A C E · xix

change and managing projects. The reader will also gain insight into the role
and functions of the IT organization or department.

Specifically Part Two has four chapters:

• Chapter Five: System Acquisition. This chapter discusses the processes
that organizations use to select information systems. We have
included a discussion on the importance of system architecture.

• Chapter Six: System Implementation and Support. This chapter reviews
the processes and activities need to implement and support health
care information systems. We have included an examination of change
management and project management.

• Chapter Seven: Assessing and Achieving Value in Health Care
Information Systems. This chapter discusses the nature of the value
that can be obtained from health care information systems and the
approaches to achieving that value.

• Chapter Eight: Organizing Information Technology Services. This
chapter reviews the structure and responsibilities of the IT
organization. This chapter discusses IT senior management roles such
as the chief information offi cer and the chief medical information
offi cer.

The purpose of Part Three (“Laws, Regulations, and Standards That
Affect Health Care Information Systems”) is to provide the reader with an
overview of the laws, regulations, and standards that affect health care infor­
mation systems. Emphasis is given to system security.

Specifically Part Three has three chapters:

• Chapter Nine: Privacy and Security. This chapter examines privacy and
security regulations and practices.

• Chapter Ten: Performance Standards and Measures. This chapter
discusses the wide range of regulations that affect health care
information systems, with an emphasis on new regulations related to
the focus on the continuum of care.

• Chapter Eleven: Health Care Information Systems Standards. This
chapter reviews the new and emerging standards that govern health
care data, transactions, and quality measures.

The purpose of Part Four (“Senior-Level Management Issues Related to
Health Care Information Systems Management”) is to provide the reader with

xx · P R E F A C E

an understanding of senior-level management responsibilities and activities
related to IT management.

Specifically Part Four has three chapters:

• Chapter Twelve: IT Alignment and Strategic Planning. This chapter
discusses the processes used by organizations to develop an IT
strategic plan. The chapter reviews the challenges faced in developing
these plans.

• Chapter Thirteen: IT Governance and Management. This chapter
discusses several topics that must be addressed by senior leadership
if IT is to be leveraged effectively: establishing IT governance,
developing the IT budget, and ensuring that projects are successful.

• Chapter Fourteen: Health IT Leadership Case Studies. This chapter
comprises case studies that provide real-world situations that touch on
the content of this textbook.

Each chapter in the book (except Chapter Fourteen) begins with a set of
chapter learning objectives and an overview and concludes with a summary
of the material presented and a set of learning activities. These activities are
designed to give students an opportunity to explore more fully the concepts intro­
duced in the chapter and to gain hands-on experience by visiting and talking
with IT and management professionals in a variety of health care settings.

Two appendixes offer supplemental information. Appendix A presents an
overview of the health care IT industry: the companies that provide IT hard­
ware, software, and a wide range of services to health care organizations.
Appendix B contains a sample project charter, sample job descriptions, and a
sample user satisfaction survey: documents referenced throughout the book.

Depending on the nature and interests of the students, various chapters
are worth emphasizing. Students and courses that are targeted for current
or aspiring senior executive positions may want to emphasize Chapter One
(National Health Care IT Landscape), Chapter Four (Population Health),
Chapter Seven (IT Value), Chapter Twelve (IT Strategy), and Chapter Thirteen
(IT Governance and Management). For classes focused on mid-level man­
agement, Chapter One (National Health Care IT Landscape), Chapter Five
(System Selection), Chapter Six (System Implementation), and Chapter Seven
(IT Value) will merit attention.

Regardless of role, Chapter Two (Health Care Data), Chapter Three
(Health Care Information Systems), Chapter Eight (IT Organization), and
Part Three (Laws, Regulations, and Standards) provide important founda­
tional knowledge.

P R E F A C E · xxi

One final comment. Two terms, health information technology (HIT) and
health care information systems (HCIS), are frequently used throughout the
text. Although it may seem that these terms are interchangeable, they are, in
fact, related but different. As used in this text, HIT encompasses the technol­
ogies (hardware, software, networks, etc.) used in the management of health
information. HCIS describes a broader concept that not only encompasses HIT
but also the processes and people that the HIT must support. HCIS delivers
value to individual health care organizations, patients, and providers, as well
as across the continuum of care and for entire communities of individuals.
HIT delivers little value on its own. Both HCIS and HIT must be managed,
but the management of HCIS is significantly more difficult and diverse.

Health care and health care information technology are in the early stages
of a profound transformation. We hope you find this textbook helpful as we
prepare our students for the challenges that lie ahead.

Acknowledgments

We wish to extend a special thanks to Juli Wilt for her dedication and assis­
tance in preparing the fi nal manuscript for this book. We also wish to thank
the following MUSC students in the doctoral program in health administra­
tion, who contributed information systems management stories and expe­
riences to us for use as case studies: Penney Burlingame, Barbara Chelton,
Stuart Fine, David Freed, David Gehant, Patricia Givens, Shirley Harkey,
Victoria Harkins, Randall Jones, Michael Moran, Catrin Jones-Nazar, Ronald
Kintz, Lauren Lent, George Mikatarian, Lorie Shoemaker, and Gary Wilde.

To all of our students whom we have learned from over the years, we
thank you.

Finally, we wish to extend a very special thanks to Molly Shane Grasso
for her many contributions to Chapter Four, “Information Systems to Support
Population Health Management.”

xxiii

The Authors

Karen A. Wager is professor and associate dean for student affairs in the
College of Health Professions at the Medical University of South Carolina
(MUSC), where she teaches management and health information systems
courses to graduate students. She has more than thirty years of professional
and academic experience in the health information management profession
and has published numerous articles, case studies, and book chapters. Recog­
nized for her excellence in interprofessional education and in bringing prac­
tical research to the classroom, Wager received the 2016 College Teacher of
the Year award and the 2008 MUSC outstanding teaching award in the educa­
tor-lecturer category and the 2008 Governor’s Distinguished Professor Award.
She currently serves as the chair of the Accreditation Council for the Com­
mission on Accreditation of Healthcare Management Education (CAHME), is
a member of the CAHME board of directors, and is a past fellow of CAHME.
Wager previously served as a member of the HIMSS-AUPHA-CAHME Task
Force responsible for the development of a model curriculum in health
information systems appropriate for educating graduate students in health
administration programs. She is past president of the South Carolina chapter
of the Healthcare Information and Management Systems Society (HIMSS)
and past president of the South Carolina Health Information Management
Association. Wager holds a doctor of business administration (DBA) degree
with an emphasis in information systems from the University of Sarasota.

Frances Wickham Lee is professor and director of instructional operations
for Healthcare Simulation South Carolina at the Medical University of South
Carolina (MUSC). She recently joined the faculty at Walden University to
teach in the Master of Healthcare Administration program. Lee has more
than thirty years of professional and academic experience in the health
information management, including publication of numerous articles and
book chapters related to the field. She is past president of the North Carolina
Health Information Management Association and South Carolina chapter of
the Healthcare Information and Management Systems Society (HIMSS). Since
2007, Lee has broadened her expertise as a health care educator through
her membership in a pioneering team charged with bringing health care

xxv

xxvi · T H E A U T H O R S

simulation to students and practicing professionals across the state of South
Carolina. She holds a DBA degree with an emphasis in information systems
from the University of Sarasota.

John P. Glaser currently serves as the senior vice president of population health
for Cerner. He joined Cerner in 2015 as part of the Siemens Health Services
acquisition, where he was CEO. Prior to Siemens, Glaser was vice president
and CIO at Partners HealthCare. He also previously served as vice president of
information systems at Brigham and Women’s Hospital.

Glaser was the founding chair of the College of Healthcare Informa­
tion Management Executives (CHIME) and the past president of the Health-
care Information and Management Systems Society (HIMSS). He has served
on numerous boards including eHealth Initiative, the American Telemedi­
cine Association (ATA), and the American Medical Informatics Association
(AMIA). He is a fellow of CHIME, HIMSS, and the American College of Health
Informatics. He is a former senior advisor to the Office of the National Coor­
dinator for Health Information Technology (ONC).

Glaser has published more than two hundred articles, three books on the
strategic application of information technology in health care. Glaser holds
a PhD in health care information systems from the University of Minnesota.

Health Care

Information Systems

Major Environmental
Forces That Shape

the National Health
Information System

Landscape

PART ONE

1

CHAPTER 1

The National Health

Information Technology

Landscape

LEARNING OBJECTIVES

• To be able to discuss some of the most signifi cant infl uences
shaping the current and future health information technology
landscapes in the United States.

• To understand the roles national private sector and government
initiatives have played in the advancement of health information
technology in the United States.

• To be able to describe major events since the 1990s that have
infl uenced the adoption of health information technologies and
systems.

3

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Since the early 1990s, the use of health information technology (HIT)
across all aspects of the US health care delivery system has been increasing.
Electronic health records (EHRs), telehealth, social media, mobile applica­
tions, and so on are becoming the norm—even commonplace—today. Today’s
health care providers and organizations across the continuum of care have
come to depend on reliable HIT to aid in managing population health effec­
tively while reducing costs and improving quality patient care. Chapter One
will explore some of the most signifi cant influences shaping the current and
future HIT landscapes in the United States. Certainly, advances in infor­
mation technology affect HIT development, but national private sector and
government initiatives have played key roles in the adoption and application
of the technologies in health care. This chapter will provide a chronologi­
cal overview of the significant government and private sector actions that
have directly or indirectly affected the adoption of HIT since the Institute of
Medicine landmark report, The Computer-Based Patient Record: An Essential
Technology for Health Care, authored by Dick and Steen and published in 1991.
Knowledge of these initiatives and mandates shaping the current HIT national
landscape provides the background for understanding the importance of the
health information systems that are used to promote excellent, cost-effective
patient care.

1990s: THE CALL FOR HIT

Institute of Medicine CPR Report

The Institute of Medicine (IOM) report The Computer-Based Patient Record:
An Essential Technology for Health Care (Dick & Steen, 1991) brought
international attention to the numerous problems inherent in paper-based
medical records and called for the adoption of the computer-based patient
record (CPR) as the standard by the year 2001. The IOM defi ned the
CPR as “an electronic patient record that resides in a system specifi ­
cally designed to support users by providing accessibility to complete and
accurate data, alerts, reminders, clinical decision support systems, links
to medical knowledge, and other aids” (Dick & Steen, 1991, p. 11). This
vision of a patient’s record offered far more than an electronic version of
existing paper records—the IOM report viewed the CPR as a tool to assist
the clinician in caring for the patient by providing him or her with remind­
ers, alerts, clinical decision–support capabilities, and access to the latest
research findings on a particular diagnosis or treatment modality. CPR
systems and related applications, such as EHRs, will be further discussed

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in Chapter Three. At this point, it is important to understand the IOM
report’s impact on the vendor community and health care organizations.
Leading vendors and health care organizations saw this report as an
impetus toward radically changing the ways in which patient information
would be managed and patient care delivered. During the 1990s, a number
of vendors developed CPR systems. However, despite the fact that these
systems were, for the most part, reliable and technically mature by the
end of the decade, only 10 percent of hospitals and less than 15 percent
of physician practices had implemented them (Goldsmith, 2003). Needless
to say, the IOM goal of widespread CPR adoption by 2001 was not met.
The report alone was not enough to entice organizations and individual
providers to commit to the required investment of resources to make the
switch from predominantly paper records.

Health Insurance Portability and Accountability Act (HIPAA)

Five years after the IOM report advocating CPRs was published, President
Clinton signed into law the Health Insurance Portability and Account­
ability Act (HIPAA) of 1996 (which is discussed in detail in Chapter Nine).
HIPAA was designed primarily to make health insurance more affordable
and accessible, but it included important provisions to simplify adminis­
trative processes and to protect the security and confi dentiality of personal
health information. HIPAA was part of a larger health care reform effort and
a federal interest in HIT for purposes beyond reimbursement. HIPAA also
brought national attention to the issues surrounding the use of personal
health information in electronic form. The Internet had revolutionized the
way that consumers, providers, and health care organizations accessed health
information, communicated with each other, and conducted business, creat­
ing new risks to patient privacy and security.

2000–2010: THE ARRIVAL OF HIT

IOM Patient Safety Reports

A second IOM report, To Err Is Human: Building a Safer Health Care System
(Kohn, Corrigan, & Donaldson, 2000), brought national attention to research
estimating that 44,000 to 98,000 patients die each year because of medical
errors. A subsequent related report by the IOM Committee on Data Stan­
dards for Patient Safety, Patient Safety: Achieving a New Standard for Care
(Aspden, 2004), called for health care organizations to adopt information

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technology capable of collecting and sharing essential health information on
patients and their care. This IOM committee examined the status of stan­
dards, including standards for health data interchange, terminologies, and
medical knowledge representation. Here is an example of the committee’s
conclusions:

• As concerns about patient safety have grown, the health care
sector has looked to other industries that have confronted similar
challenges, in particular, the airline industry. This industry learned
long ago that information and clear communications are critical to
the safe navigation of an airplane. To perform their jobs well and
guide their plane safely to its destination, pilots must communicate
with the airport controller concerning their destination and current
circumstances (e.g., mechanical or other problems), their fl ight
plan, and environmental factors (e.g., weather conditions) that
could necessitate a change in course. Information must also pass
seamlessly from one controller to another to ensure a safe and
smooth journey for planes fl ying long distances, provide notifi cation
of airport delays or closures because of weather conditions, and
enable rapid alert and response to extenuating circumstance, such as
a terrorist attack.

• Information is as critical to the provision of safe health care—which
is free of errors of commission and omission—as it is to the safe
operation of aircraft. To develop a treatment plan, a doctor must have
access to complete patient information (e.g., diagnoses, medications,
current test results, and available social supports) and to the most
current science base (Aspden, 2004).

Whereas To Err Is Human focused primarily on errors that occur in hospi­
tals, the 2004 report examined the incidence of serious safety issues in other
settings as well, including ambulatory care facilities and nursing homes. Its
authors point out that earlier research on patient safety focused on errors
of commission, such as prescribing a medication that has a potentially fatal
interaction with another medication the patient is taking, and they argue
that errors of omission are equally important. An example of an error of
omission is failing to prescribe a medication from which the patient would
likely have benefited (Institute of Medicine, Committee on Data Standards
for Patient Safety, 2003). A significant contributing factor to the unacceptably
high rate of medical errors reported in these two reports and many others is
poor information management practices. Illegible prescriptions, unconfi rmed

2 0 0 0 – 2 0 1 0 : T H E A R R I V A L O F H I T · 7

verbal orders, unanswered telephone calls, and lost medical records could all
place patients at risk.

Transparency and Patient Safety

The federal government also responded to quality of care concerns by pro­
moting health care transparency (for example, making quality and price
information available to consumers) and furthering the adoption of HIT. In
2003, the Medicare Modernization Act was passed, which expanded the
program to include prescription drugs and mandated the use of electronic
prescribing (e-prescribing) among health plans providing prescription drug
coverage to Medicare beneficiaries. A year later (2004), President Bush called
for the widespread adoption of EHR systems within the decade to improve
efficiency, reduce medical errors, and improve quality of care. By 2006, he
had issued an executive order directing federal agencies that administer or
sponsor health insurance programs to make information about prices paid
to health care providers for procedures and information on the quality of
services provided by physicians, hospitals, and other health care providers
publicly available. This executive order also encouraged adoption of HIT
standards to facilitate the rapid exchange of health information (The White
House, 2006).

During this period significant changes in reimbursement practices also
materialized in an effort to address patient safety, health care quality, and
cost concerns. Historically, health care providers and organizations had
been paid for services rendered regardless of patient quality or outcome.
Nearing the end of the decade, payment reform became a hot item. For
example, pay for performance (P4P) or value-based purchasing pilot
programs became more widespread. P4P reimburses providers based on
meeting predefined quality measures and thus is intended to promote
and reward quality. The Centers for Medicare and Medicaid Services
(CMS) notified hospitals and physicians that future increases in payment
would be linked to improvements in clinical performance. Medicare also
announced it would no longer pay hospitals for the costs of treating certain
conditions that could reasonably have been prevented—such as bedsores,
injuries caused by falls, and infections resulting from the prolonged use of
catheters in blood vessels or the bladder—or for treating “serious prevent­
able” events—such as leaving a sponge or other object in a patient during
surgery or providing the patient with incompatible blood or blood prod­
ucts. Private health plans also followed Medicare’s lead and began denying
payment for such mishaps. Providers began to recognize the importance

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of adopting improved HIT to collect and transmit the data needed under
these payment reforms.

Office of the National Coordinator for Health

Information Technology

In April 2004, President Bush signed Executive Order No. 13335, 3 C.F.R.,
establishing the Office of the National Coordinator for Health Information
Technology (ONC) and charged the office with providing “leadership for
the development and nationwide implementation of an interoperable health
information technology infrastructure to improve the quality and effi ciency
of health care.” In 2009, the role of the ONC (organizationally located within
the US Department of Health and Human Services) was strengthened when
the Health Information Technology for Economic and Clinical Health
(HITECH) Act legislatively mandated it to provide leadership and oversight
of the national efforts to support the adoption of EHRs and health informa­
tion exchange (HIE) (ONC, 2015).

In spite of the various national initiatives and changes to reimbursement
during the first decade of the twenty-first century, by the end of the decade
only 25 percent of physician practices (Hsiao, Hing, Socey, & Cai, 2011) and
12 percent of hospitals (Jha, 2010) had implemented “basic” EHR systems.
The far majority of solo and small physician practices continued to use paper-
based medical record systems. Studies show that the relatively low adoption
rates among solo and small physician practices were because of the cost of
HIT and the misalignment of incentives (Jha et al., 2009). Patients, payers,
and purchasers had the most to gain from physician use of EHR systems, yet
it was the physician who was expected to bear the total cost. To address this
misalignment of incentives issue, to provide health care organizations and
providers with some funding for the adoption and Meaningful Use of EHRs,
and to promote a national agenda for HIE, the HITECH Act was passed as a
part of the American Recovery and Reinvestment Act in 2009.

2010–PRESENT: HEALTH CARE REFORM AND

THE GROWTH OF HIT

HITECH and Meaningful Use

An important component of HITECH was the establishment of the Medicare
and Medicaid EHR Incentive Programs. Eligible professionals and hospitals
that adopt, implement, or upgrade to a certified EHR received incentive pay­
ments. After the first year of adoption, the providers had to prove successfully

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that they were “demonstrating Meaningful Use” of certified EHRs to receive
additional incentive payments. The criteria, objectives, and measures for
demonstrating Meaningful Use evolved over a five-year period from 2011 to
2016. The first stage of Meaningful Use criteria was implemented in 2011–2012
and focused on data capturing and sharing. Stage 2 (2014) criteria are
intended to advance clinical processes, and Stage 3 (2016) criteria aim to show
improved outcomes. Table 1.1 provides a broad overview of the Meaningful
Use criteria by stage.

Through the Medicare EHR Incentive Program, each eligible professional
who adopted and achieved meaningful EHR use in 2011 or 2012 was able
to earn up to $44,000 over a five-year period. The amount decreased over
the period, creating incentives to providers to start sooner rather than later.

Table 1.1 Stages of Meaningful Use

Stage 1: Stage 2: Stage 3:
Meaningful Use criteria Meaningful Use criteria Meaningful Use criteria
focus focus focus

Electronically capturing
health information in a
standardized format

Using that information
to track key clinical
conditions

Communicating that
information for care
coordination processes

Initiating the reporting
of clinical quality
measures and public
health information

Using information to
engage patients and
their families in their
care

More rigorous HIE

Increased requirements
for e-prescribing and
incorporating lab
results

Electronic transmission
of patient summaries
across multiple settings

More patient-controlled
data

Improving quality, safety,
and effi ciency leading
to improved health
outcomes

Decision support for
national high-priority
conditions

Patient access to self-
management tools

Access to comprehensive
patient data through
patient-centered HIE

Improving population
health

Source: ONC (n.d.a.).

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Eligible hospitals could earn over $2 million through the Medicare EHR
Incentive Program, and the Medicaid program made available up to $63,500
for each eligible professional (through 2021) and over $2 million to each
eligible hospital. As of December 2015, more than 482,000 health care pro­
viders received a total of over $31 billion in payments for participating in the
Medicare and Medicaid EHR Incentive Programs (CMS, n.d.). See Table 1.2
for primary differences between the two incentive programs.

Within the ONC, the Office of Interoperability and Standards oversees
certification programs for HIT. The purpose of certification is to provide
assurance to EHR purchasers and other users that their EHR system has the
technological capability, functionality, and security needed to assist them in
meeting Meaningful Use criteria. Eligible providers who apply for the EHR
Medicare and Medicaid Incentive Programs are required to use certifi ed EHR
technology. The ONC has authorized certain organizations to perform the
actual testing and certification of EHR systems.

Other HITECH Programs

Many small physician practices and rural hospitals do not have the in-house
expertise to select, implement, and support EHR systems that meet certifi ca­
tion standards. To address these needs, HITECH funded sixty-two regional
extension centers (RECs) throughout the nation to support providers in adopt­
ing and becoming meaningful users of EHRs. The RECs are primarily intended
to provide advice and technical assistance to primary care providers, espe­
cially those in small practices, and to small rural hospitals, which often do not
have information technology (IT) expertise. Furthermore, HITECH provided
funding for various workforce training programs to support the education
of HIT professionals. The education-based programs included curriculum
development, community college consortia, competency examination, and
university-based training programs, with the overarching goal of training an
additional forty-five thousand HIT professionals. Funding was also made avail­
able to seventeen Beacon communities and Strategic Health IT Advanced
Research Projects (SHARP) across the nation. The Beacon programs are
leading organizations that are demonstrating how HIT can be used in innova­
tive ways to target specific health problems within communities (HealthIT.gov,
2012). These programs are illustrating HIT’s role in improving individual and
population health outcomes and in overcoming barriers such as coordination
of care, which plagues our nation’s health care system (McKethan et al., 2011).

Achieving Meaningful Use requires that health care providers are able to
share health information electronically with others using a secure network
for HIE. To this end, HITECH provided state grants to help build the HIE

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Table 1.2 Differences between Medicare and Medicaid EHR incentive programs

Medicare EHR Incentive Program Medicaid EHR Incentive Program

Federally implemented and available
nationally

Medicare Advantage professionals have
special eligibility accommodations.

Open to physicians, subsection (d)
hospitals, and critical access hospitals

Same definition of Meaningful Use
applied to all participants nationally

Must demonstrate Meaningful Use in
fi rst year

Maximum incentive for eligible
professionals is $44,000; 10 percent
for HPSA (health professional shortage
area).

2014 is the last year in which a
professional can initiate participation.

Payments over fi ve years

In 2015 fee reductions (penalties) begin
for those who do not demonstrate
Meaningful Use of a certifi ed HER.

2016 is the last incentive payment year.

No Medicare patient population
minimum is required.

Implemented voluntarily by states

Medicaid managed care professionals
must meet regular eligibility
requirements.

Open to fi ve types of professionals and
three types of hospitals

States can adopt a more rigorous
definition of Meaningful Use.

Adopt, implement, or upgrade option in
fi rst year

Maximum incentive for eligible
professionals is $63,750.

2016 is the last year in which a
professional can initiate participation.

Payments over six years

No fee reductions (penalties)

2021 is the last incentive payment year.

Eligible professionals must have a
30 percent Medicaid population
(20 percent for pediatricians) to
participate; this must be demonstrated
annually.

Source: Carson, Garr, Goforth, and Forkner (2010).

infrastructure for exchange of electronic health information among provid­
ers and between providers and consumers. Nearly all states have approved
strategic and operational plans for moving forward with implementation of
their HIE cooperative agreement programs.

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Affordable Care Act

In addition to the increased efforts to promote HIT through legislated pro­
grams, the early 2010s brought dramatic change to the health care sector as
a whole with the passage of significant health care reform legislation. Amer­
icans have grappled for decades with some type of “health care reform” in
an attempt to achieve the simultaneous “triple aims” for the US health care
delivery system:

• Improve the patient experience of care

• Improve the health of populations

• Reduce per capita cost of health care (IHI, n.d.)

Full achievement of these aims has been challenging within a health care
delivery system managed by different stakeholders—payers, providers, and
patients—whose goals are frequently not well aligned. The latest attempt at
reform occurred in 2010, when President Obama signed into law the Patient
Protection and Affordable Care Act (PPACA), now known as the Affordable
Care Act (ACA).

Along with mandating that individuals have health insurance and
expanding Medicaid programs, the ACA created the structure for health
insurance exchanges, including a greater role for states, and imposed
changes to private insurance, such as prohibiting health plans from
placing lifetime limits on the dollar value of coverage and prohibiting
preexisting condition exclusions. Numerous changes were to be made to
the Medicare program, including continued reductions in Medicare pay­
ments to certain hospitals for hospital-acquired conditions and excessive
preventable hospital readmissions. Additionally, the CMS established an
innovation center to test, evaluate, and expand different payment struc­
tures and methodologies to reduce program expenditures while main­
taining or improving quality of care. Through the innovation center and
other means, CMS has been aggressively pursuing implementation of
value-based payment methods and exploring the viability of alternative
models of care and payment.

The final assessment of the success of ACA is still unknown; however,
what is certain is that its various programs will rely heavily on quality HIT
to achieve their goals. A greater emphasis than ever is placed on facilitating
patient engagement in their own care through the use of technology. On the
other end of the spectrum, new models of care and payment include improved
health for populations as an explicit goal, requiring HIT to manage the sheer
volume and complexity of data needed.

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Value-Based Payment Programs

Shortly after the ACA was passed, CMS implemented several value-based
payment programs in an effort to reward health care providers with incentive
payments for the quality of care they provide to Medicare patients. In 2015,
the Medicare Access and CHIP Reauthorization Act (MACRA) was signed
into law. Among other things, MACRA outlines a timetable for the 2019
implementation of a merit-based incentive payment system (MIPS) that will
replace other value-based payment programs, including the EHR Incentive
Programs. MIPS will use a set of performance measures, divided into catego­
ries, to calculate a score (between 0 and 100) for eligible professionals. Each
category of performance will be weighted as shown in Table 1.3.

Health care providers meeting the established threshold score will receive
no adjustment to payment; those scoring below will receive a negative adjust­
ment, and those above, a positive adjustment. Exceptional performers may
receive bonus payments (CMS, n.d.).

Alternate Payment Methods

Providers who meet the criteria to provide an alternate payment method
(APM) will receive bonus payments and will be exempt from the MIPS.
Although there are likely to be other APMs identified over time, three types
are receiving a great deal of attention currently: accountable care organi­
zations (ACOs), bundled payments, and patient-centered medical homes
(PCMHs). ACOs are “networks of . . . health care providers that share respon­
sibility for coordinating care and meeting health care quality and cost metrics
for a defined patient population” (Breakaway Policy Strategies for FasterCures,
2015, p. 2). Bundled payments aim to incentivize providers to improve care
coordination, promote teamwork, and lower costs. Payers will compensate

Table 1.3 MIPS performance categories

Category Weight (%)

Quality 50

Advancing care information 25

Clinical practice improvement activities 15

Resource use 10

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providers with a single payment for an episode of care. PCMHs are APMs
that are rooted in the private sector. In 2007, four physician societies pub­
lished a joint statement of principles emphasizing a personal physician–led
coordination of care. All of the APMs rely heavily on HIT. ACOs and PCMHs,
in particular, require that HIT support the organization and its providers in
the carrying out the following functions:

• Manage and coordinate integrated care.

• Identify, manage, and reduce or contain costs.

• Adhere to evidence-based practice guidelines and standards of care;
ensure quality and safety.

• Manage population health.

• Engage patients and their families and caregivers in their own care.

• Report on quality outcomes.

HIT Interoperability Efforts

Despite efforts dating back to the first reports on the need for adoption of
computerized patient records, complete interoperability among HIT systems,
which is key to supporting an integrated health care delivery system that
provides improved care to individuals and populations while managing costs,
remains elusive. The federal government, along with other provider, vendor,
and professional organizations, however, recognize this need for interopera­
bility. The ONC defines interoperability as “the ability of a system to exchange
electronic health information with and use electronic health information from
other systems without special effort on the part of the user” (ONC, n.d.a).
Interoperability among HIT encompasses far more than just connected EHRs
across systems. Home health monitoring systems are becoming common­
place, telehealth is on the rise, and large public health databases exist at
state and national levels. True interoperability will encompass any electronic
sources with information needed to provide the best possible health care.

Some of the more notable efforts toward HIT interoperability include
the efforts by the government under the direction of the ONC and several
other national public and private organizations. In 2015, the ONC published
“Connecting Health and Care for the Nation: A Shared Nationwide Interop­
erability Roadmap,” a ten-year plan for achieving HIT interoperability in the
United States. Figure 1.1 summarizes the key milestones identified in the ONC
road map. The ultimate goal for 2024 is “a learning health system enabled
by nationwide interoperability.” The goal of the learning health system is to

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Figure 1.1 Milestones for a supportive payment and regulatory environment

Source: ONC (2015).

improve the health of individuals and populations by “generating information
and knowledge from data captured and updated over time . . . and sharing and
disseminating what is learned in timely and actionable forms that directly
enable individuals, clinicians, and public health entities to . . . make informed
decisions” (ONC, 2015, p. 18).

Health Level Seven International (HL7), a not-for-profi t, ANSI (American
National Standards Institute)–accredited, standards-developing organization,
is focused on technical standards for HIE. The HL7 Fast Healthcare Interop­
erability Resources (FHIR) standards were introduced in 2012 and are under
development to improve the exchange of EHR data. About this same time
Healtheway, now the Sequoia Project, was chartered as a nonprofi t organi­
zation to “advance the implementation of secure, interoperable nationwide
health information exchange” (Sequoia Project, n.d.a). The Sequoia Project
supports several initiatives, including the eHealth Exchange, a group of
government and nongovernment organizations devoted to improving patient
care through “interoperable health information exchange” (Sequoia Project,
n.d.a). Unlike HL7, which focuses on technical standards, eHealth Exchange’s
primary focus is on the legal and policy barriers associated with nationwide
interoperability. Another Sequoia initiative, Carequality, strives to connect
private HIE networks. Another private endeavor, Commonwell Health Alli­
ance, is a consortium of HIT vendors and other organizations that are com­
mitted to achieving interoperability. Commonwell began in 2013 with six
EHR vendors. In 2015, their membership represented 70 percent of hospitals.
Provider members of Commonwell register their patients in order to exchange
easily information with other member providers (Jacob, 2015).

Although HIT has become commonplace across the continuum of care,
seamless interoperability among the nation’s HIT systems has not yet been
realized. One author describes the movement toward HIT interoperability in
the United States not as a straight path but rather as a jigsaw puzzle with
multiple public and private organizations “working on different pieces”

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(Jacob, 2015). Interoperability requires not only technical standards but also
a national health information infrastructure, along with an effective gov­
erning system. Concerns about the misalignment of incentives for achiev­
ing interoperability remain. Most experts agree that technology is not the
barrier to interoperability. Governance and alignment of agendas among
disparate organizations are cited as the most daunting barriers. Because of
its potential to affect seriously the progress of interoperability, in 2015, the
ONC reported to Congress on the phenomenon of health information block­
ing, which is defined as occurring “when persons or entities knowingly and
unreasonably interfere with the exchange or use of electronic health infor­
mation” (ONC, 2015). The report charged that current economic incentives
were not supportive of information exchange and that some of the current
market practices actually discouraged sharing health information (DeSalvo
& Daniel, 2015).

SUMMARY

Chapter One provides a brief chronological overview of the some of the most
significant national drivers in the development, growth, and use of HIT in
the United States. Since the 1990s and the publication of The Computer-Based
Patient Record: An Essential Technology for Health Care, the national HIT
landscape has certainly evolved, and it will continue to do so. Challenges
to realizing an integrated national HIT infrastructure are numerous, but the
need for one has never been greater. Recognizing that the technology is not
the major barrier to the national infrastructure, the government, through
legislation, CMS incentive programs, the ONC, and other programs, will
continue to play a significant role in the Meaningful Use of HIT, pushing for
the alignment of incentives within the health care delivery system.

In a 2016 speech, CMS acting chief Andy Slavitt summed up the govern­
ment’s role in achieving its HIT vision with the following statements:

The focus will move away from rewarding providers for the use of tech­
nology and towards the outcome they achieve with their patients.

Second, providers will be able to customize their goals so tech compa­
nies can build around the individual practice needs, not the needs of the
government. Technology must be user-centered and support physicians,
not distract them.

Third, one way to aid this is by leveling the technology playing fi eld for
start-ups and new entrants. We are requiring open APIs . . . that allow
apps, analytic tools, and connected technologies to get data in and out of
an EHR securely.

K E Y T E R M S · 17

We are deadly serious about interoperability. We will begin initiatives . . .
pointing technology to fill critical use cases like closing referral loops and
engaging a patient in their care.

Technology companies that look for ways to practice “data blocking” in oppo­
sition to new regulations will find that it won’t be tolerated. (Nerney, 2016)

Many of the initiatives discussed in Chapter One will be explored more
fully in subsequent chapters of this book. The purpose of Chapter One is
to provide the reader with a snapshot of the national HIT landscape and
enough historical background to set the stage for why health care managers
and leaders must understand and actively engage in the implementation of
effective health information systems to achieve better health for individuals
and populations while managing costs.

KEY TERMS
Accountable Care Organizations (ACOs)

Affordable Care Act (ACA)

Alternate payment methods (APM)

American Recovery and Reinvestment

Act
ANSI (American National Standards

Institute)
Beacon communities
Bundled payments
Centers for Medicare and Medicaid

Services (CMS)
Commonwell Health Alliance
Computer-based patient record (CPR)
Coordination of care
eHealth Exchange
Electronic health records (EHRs)
e-prescribing
Fast Healthcare Interoperability

Resources (FHIR) standards
Health information blocking
Health information exchange (HIE)
Health information technology (HIT)
Health Information Technology for

Economic and Clinical Health

(HITECH) Act

Health Insurance Portability and

Accountability Act (HIPAA)

Health Level Seven International

(HL7)

HIT interoperability
Meaningful Use of EHR
Medicare Access and CHIP

Reauthorization Act (MACRA)
Medicare Modernization Act
Merit-based incentive payment system

(MIPS)
Nationwide Interoperability

Roadmap
Office of the National Coordinator

for Health Information Technology
(ONC)

Patient-centered medical homes
(PCMHs)

Patient safety
Pay for performance (P4P)
Regional extension centers (RECs)
Strategic Health IT Advanced

Research Projects (SHARP)
The Sequoia Project
Value-based payment

18 · C H A P T E R 1 : T H E N A T I O N A L H E A L T H I N F O R M A T I O N T E C H N O L O G Y L A N D S C A P E

LEARNING ACTIVITIES

1. Investigate the latest Meaningful Use criteria for eligible professionals
or eligible hospitals. Visit either a physician practice or hospital in
your community. Have they participated in the Medicare or Medicaid
EHR Incentive Program? Why or why not? If the organization or
provider has participated in the program, what has the experience
been like? What lessons have they learned? Find out the degree to
which the facility uses EHRs and what issues or challenges they have
had in achieving Meaningful Use.

2. Evaluate different models of care within your local community or
state. Did you find any examples of accountable care organizations
or patient-centered medical homes? Explain. Working as a team, visit
or interview a leader from a site that uses an innovative model of
care. Describe the model, its use, challenges, and degree of patient
coordination and integration. How is HIT used to support the delivery
of care and reporting of outcomes?

3. Investigate one of the Beacon communities to find out how they
are using HIT to improve quality of care and access to care within
their region. Be prepared to share with the class a summary of your
findings. Do you think the work that this Beacon community has
done could be replicated in your community? Why or why not?

4. Explore the extent to which health information exchange is occurring
within your community, region, or state. Who are the key players?
What types of models of health information exchange exist? To
what extent is information being exchanged across organizations for
patient care purposes?

5. Investigate the CMS website to determine their current and proposed
value-based or pay-for-performance programs. Compare one or more
of the programs to the traditional fee-for-service payment method.
What are the advantages and disadvantages of each to a physician
provider in a small practice?

REFERENCES

Aspden, P. (2004). Patient safety: Achieving a new standard for care. Washington,
DC: National Academies Press.

Breakaway Policy Strategies for FasterCures. (2015). A closer look at alternative
payment models. FasterCures value and coverage issue brief. Retrieved August 4,
2016, from http://www.fastercures.org/assets/Uploads/PDF/VC-Brief-Alternative
PaymentModels.pdf

R E F E R E N C E S · 19

Carson, D. D., Garr, D. R., Goforth, G. A., & Forkner, E. (2010). The time to hesitate
has passed: The age of electronic health records is here (pp. 2–11). Columbia,
SC: South Carolina Medical Association.

Centers for Medicare & Medicaid Services (CMS). (n.d.). The merit-based incen­
tive payment system: MIPS scoring methodology overview. Retrieved August
4, 2016, from https://www.cms.gov/Medicare/Quality-Initiatives-Patient-
Assessment-Instruments/Value-Based-Programs/MACRA-MIPS-and-APMs/
MIPS-Scoring-Methodology-slide-deck.pdf

DeSalvo, K., & Daniel, J. (2015, April 10). Blocking of health information undermines
health system interoperability and delivery reform. HealthIT Buzz. Retrieved
August 4, 2016, from https://www.healthit.gov/buzz-blog/from-the-onc-desk/
health-information-blocking-undermines-interoperability-delivery-reform/

Dick, R. S., & Steen, E. B. (1991). The computer-based patient record: An essential
technology for health care. Washington, DC: National Academy Press.

Goldsmith, J. C. (2003). Digital medicine: Implications for healthcare leaders.
Chicago, IL: Health Administration Press.

HealthIT.gov. (2012). The Beacon community program improving health through
health information technology [Brochure]. Retrieved August 3, 2016, from
https://www.healthit.gov/sites/default/fi les/beacon-communities- lessons­
learned.pdf

Hsiao, C., Hing, E., Socey, T., & Cai, B. (2011, Nov.). Electronic medical record/
electronic health record systems of office-based physicians: United States, 2009
and preliminary 2010 state estimates. NCHS Data Brief (79). Washington, DC:
US Department of Health and Human Services, National Center for Health
Statistics, Division of Health Care Statistics.

Institute for Healthcare Improvement (IHI). (n.d.). The IHI triple aim. Retrieved
September 22, 2016, from http://www.ihi.org/Engage/Initiatives/TripleAim/
Pages/default.aspx

Institute of Medicine, Committee on Data Standards for Patient Safety. (2003).
Reducing medical errors requires national computerized information systems:
Data standards are crucial to improving patient safety. Retrieved from http://
www8.nationalacademies.org/onpinews/newsitem.aspx?RecordID=10863

Jacob, J. A. (2015). On the road to interoperability, public and private organizations
work to connect health care data. JAMA, 314(12), 1213.

Jha, A. K. (2010). Meaningful use of electronic health records. JAMA, 304(15),
1709. doi:10.1001/jama.2010.1497

Jha, A. K., Desroches, C. M., Campbell, E. G., Donelan, K., Rao, S. R., Ferris,
T. G. . . . Blumenthal, D. (2009). Use of electronic health records in US hos­
pitals. New England Journal of Medicine, 360(16), 1628–1638. doi:10.1056/
nejmsa0900592

Kohn, L. T., Corrigan, J., & Donaldson, M. S. (2000). To err is human: Building a
safer health system. Washington, DC: National Academy Press.

20 · C H A P T E R 1 : T H E N A T I O N A L H E A L T H I N F O R M A T I O N T E C H N O L O G Y L A N D S C A P E

McKethan, A., Brammer, C., Fatemi, P., Kim, M., Kirtane, J., Kunzman, J. . . .
Jain, S. H. (2011). An early status report on the Beacon Communities’ plans
for transformation via health information technology. Health Affairs, 30(4),
782–788. doi:10.1377/hlthaff.2011.0166

Nerney, C. (2016, January). CMS acting chief Slavitt on interoperabil­
ity. Retrieved August 3, 2016, from http://www.hiewatch.com/news/
cms-acting-chief-slavitt-interoperability

Office of the National Coordinator for Health Information Technology (ONC).
(2015). Connecting health and care for the nation: A shared nationwide interop­
erability roadmap. Retrieved August 3, 2016, from https://www.healthit.gov/
sites/default/fi les/nationwide-interoperability-roadmap-draft-version-1.0.pdf

Office of the National Coordinator for Health Information Technology (ONC).
(n.d.a). EHR incentives & certifi cation. Retrieved September 21, 2016, from
https://www.healthit.gov/providers-professionals/how-attain-meaningful-use

Office of the National Coordinator for Health Information Technology (ONC).
(n.d.b). Interoperability. Retrieved September 21, 2016, from https://www
.healthit.gov/policy-researchers-implementers/interoperability

The Sequoia Project. (n.d.a). About the Sequoia Project. Retrieved August 4, 2016,
from http://sequoiaproject.org/about-us/

The Sequoia Project. (n.d.b). What is eHealth exchange. Retrieved from http://
sequoiaproject.org/ehealth-exchange/

The White House. (2006, August). Fact sheet: Health care transparency: Empowering
consumers to save on quality care. Retrieved September 22, 2016, from https://
georgewbush-whitehouse.archives.gov/news/releases/2006/08/20060822.html

CHAPTER 2

Health Care Data

LEARNING OBJECTIVES

• To be able to define health care data and information.

• To be able to understand the major purposes for maintaining
patient records.

• To be able to discuss basic patient health record and claims
content.

• To be able to discuss basic uses of health care data, including big
and small data and analytics.

• To be able to identify common issues related to health care data
quality.

21

22 · C H A P T E R 2 : H E A L T H C A R E D A T A

Central to health care information systems is the actual health care data
that is collected and subsequently transformed into useful health care infor­
mation. In this chapter we will examine key aspects of health care data. In
particular, this chapter is divided into four main sections:

• Health care data and information defined (What are health data and
health information?)

• Health care data and information sources (Where does health data
originate and why? When does health care data become health care
information?)

• Health care data uses (How do health care organizations use data?
What is the impact of the trend toward analytics and big data on
health care data?)

• Health care data quality (How does the quality of health data affect its
use?)

HEALTH CARE DATA AND INFORMATION DEFINED

Often the terms health care data and health care information are used inter­
changeably. However, there is a distinction, if somewhat blurred in current
use. What, then, is the difference between health data and health informa­
tion? The simple answer is that health information is processed health data.
(We interpret processing broadly to cover everything from formal analysis to
explanations supplied by the individual decision maker’s brain.) Health care
data are raw health care facts, generally stored as characters, words, symbols,
measurements, or statistics. One thing apparent about health care data is that
they are generally not very useful for decision making. Health care data may
describe a particular event, but alone and unprocessed they are not particu­
larly helpful. Take, for example, this figure: 79 percent. By itself, what does
it mean? If we process this datum further by indicating that it represents the
average bed occupancy for a hospital for the month of January, it takes on
more meaning. With the additional facts attached, is this figure now infor­
mation? That depends. If all a health care executive wants or needs to know
is the bed occupancy rate for January, this could be considered information.
However, for the hospital executive who is interested in knowing the trend
of the bed occupancy rate over time or how the facility’s bed occupancy rate
compares to that of other, similar facilities, this is not yet the information
he needs. A clinical example of raw data would be the lab value, hematocrit
(HCT) = 32 or a diagnosis, such as diabetes. These are single facts, data at
the most granular level. They take on meaning when assigned to particular

H E A L T H C A R E D A T A A N D I N F O R M A T I O N D E F I N E D · 23

patients in the context of their health Figure 2.1 Health care data to
care status or analyzed as components health care knowledge
of population studies.

Knowledge is seen by some as
the highest level in a hierarchy with
data at the bottom and information in
the middle (Figure 2.1). Knowledge is
defined by Johns (1997, p. 53) as “a
combination of rules, relationships,
ideas, and experience.” Another way
of thinking about knowledge is that it
is information applied to rules, expe­
riences, and relationships with the
result that it can be used for decision
making. Data analytics applied to
health care information and research
studies based on health care information are examples of transforming health
care information into new knowledge. To carry out our example from previ­
ous paragraphs, the 79 percent occupancy rate could be related to additional
information to lead to knowledge that the health care facility’s referral strat­
egy is working.

Where do health care data end and where does health care information
begin? Information is an extremely valuable asset at all levels of the health care
community. Health care executives, clinical staff members, and others rely on
information to get their jobs accomplished. The goal of this discussion is not
to pinpoint where data end and information begins but rather to further an
understanding of the relationship between health care data and information—
health care data are the beginnings of health care information. You cannot
create information without data. Through the rest of this chapter the terms
health care data and health care information will be used to describe either the
most granular components of health care information or data that have been
processed, respectively (Lee, 2002).

The first several sections of this chapter focus primarily on the health
care data and information levels, but the content of the section on health care
data quality takes on new importance when applied to processes for seeking
knowledge from health care data. We will begin the chapter exploring where
some of the most common health care data originate and describe some of the
most common organizational and provider uses of health care information,
including patient care, billing and reimbursement, and basic health care
statistics. Please note there are many other uses for health information that
go beyond these basics that will be explored throughout this text.

24 · C H A P T E R 2 : H E A L T H C A R E D A T A

HEALTH CARE DATA AND INFORMATION SOURCES

The majority of health care information created and used in health care
information systems within and across organizations can be found as an
entry in a patient’s health record or claim, and this information is readily
matched to a specifi c, identifi able patient.

The Health Insurance Portability and Accountability Act (HIPAA), the
federal legislation that includes provisions to protect patients’ health informa­
tion from unauthorized disclosure, defi nes health information as any information,
whether oral or recorded in any form or medium, that does the following:

• Is created or received by a health care provider, health plan, public
health authority, employer, life insurer, school or university, or health
care clearinghouse

• Relates to the past, present, or future physical or mental health
or condition of an individual, the provision of health care to an
individual, or the past, present, or future payment for the provision of
health care to an individual

HIPAA refers to this type of identifiable information as protected health
information (PHI).

The Joint Commission, the major accrediting agency for many types of
health care organizations in the United States, has adopted the HIPAA defi ni­
tion of protected health information as the definition of “health information”
listed in their accreditation manuals’ glossary of terms (The Joint Commis­
sion, 2016). Creating, maintaining, and managing quality health information
is a significant factor in health care organizations, such as hospitals, nursing
homes, rehabilitation centers, and others, who want to achieve Joint Commis­
sion accreditation. The accreditation manuals for each type of facility contain
dozens of standards that are devoted to the creation and management of
health information. For example, the hospital accreditation manual contains
two specific chapters, Record of Care, Treatment, and Services (RC) and Infor­
mation Management (IM). The RC chapter outlines specifi c standards govern­
ing the components of a complete medical record, and the IM chapter outlines
standards for managing information as an important organizational resource.

Medical Record versus Health Record

The terms medical record and health record are often used interchangeably
to describe a patient’s clinical record. However, with the advent and subse­
quent evolution of electronic versions of patient records these terms actually
describe different entities. The Office of the National Coordinator for Health

H E A L T H C A R E D A T A A N D I N F O R M A T I O N S O U R C E S · 25

Information Technology (ONC) distinguishes the electronic medical record
and the electronic health record as follows.

Electronic medical records (EMRs) are a digital version of the paper
charts.  An EMR contains the medical and treatment history of the patients
in one practice (or organization). EMRs have advantages over paper records.
For example, EMRs enable clinicians (and others) to do the following:

• Track data over time

• Easily identify which patients are due for preventive screenings or

checkups

• Check how their patients are doing on certain parameters—such as

blood pressure readings or vaccinations

• Monitor and improve overall quality of care within the practice

But the information in EMRs doesn’t travel easily out of the practice (or
organization). In fact, the patient’s record might even have to be printed out
and delivered by mail to specialists and other members of the care team. In
that regard, EMRs are not much better than a paper record.

Electronic health records (EHRs) do all those things—and more. EHRs
focus on the total health of the patient—going beyond standard clinical
data collected in the provider’s office (or during episodes of care)—and is
inclusive of a broader view on a patient’s care. EHRs are designed to reach
out beyond the health organization that originally collects and compiles
the information. They are built to share information with other health care
providers (and organizations), such as laboratories and specialists, so they
contain information from all the clinicians involved in the patient’s care
(Garrett & Seidman, 2011). Another distinguishing feature of the EHR (dis­
cussed in more detail in Chapter Three) is the inclusion of decision-support
capabilities beyond those of the EMR.

Patient Record Purposes

Health care organizations maintain patient clinical records for several key
purposes. As we move into the discussion on clinical information systems in
subsequent chapters, it will be important to remember these purposes, which
remain constant regardless of the format or infrastructure supporting the
records. In considering the purposes listed, the scope of care is also important.
Records support not only managing a single episode of care but also a patient’s
continuum of care and population health. Episode of care generally refers to
the services provided to a patient with a specific condition for a specifi c period

26 · C H A P T E R 2 : H E A L T H C A R E D A T A

of time. Continuum of care, as defined by HIMSS (2014), is a concept involving
a system that guides and tracks patients over time through a comprehensive
array of health services spanning all levels and intensity of care. Population
health is a relatively new term and definitions vary. However, the concept
behind managing population health is to improve health outcomes within
defined communities (Stoto, 2013). The following list comprises the most
commonly recognized purposes for creating and maintaining patient records.

1. Patient care. Patient records provide the documented basis for
planning patient care and treatment, for a single episode of care and
across the care continuum. This purpose is considered the number-
one reason for maintaining patient records. As our health care
delivery system moves toward true population health management
and patient-focused care, the patient record becomes a critical tool for
documenting each provider’s contribution to that care.

2. Communication. Patient records are an important means by which
physicians, nurses, and others, whether within a single organization
or across organizations, can communicate with one another about
patient needs. The members of the health care team generally
interact with patients at different times during the day, week, or
even month or year. Information from the patient’s record plays an
important role in facilitating communication among providers across
the continuum of care. The patient record may be the only means
of communication among various providers. It is important to note
that patients also have a right to access their records, and their
engagement in their own care is often reflected in today’s records.

3. Legal documentation. Patient records, because they describe and
document care and treatment, are also legal records. In the event
of a lawsuit or other legal action involving patient care, the record
becomes the primary evidence for what actually took place during the
care. An old but absolutely true adage about the legal importance of
patient records says, “If it was not documented, it was not done.”

4. Billing and reimbursement. Patient records provide the
documentation patients and payers use to verify billed services.
Insurance companies and other third-party payers insist on clear
documentation to support any claims submitted. The federal
programs Medicare and Medicaid have oversight and review
processes in place that use patient records to confirm the accuracy
of claims filed. Filing a claim for a service that is not clearly
documented in the patient record may be construed as fraud.

H E A L T H C A R E D A T A A N D I N F O R M A T I O N S O U R C E S · 27

5. Research and quality management. Patient records are used in many
facilities for research purposes and for monitoring the quality of
care provided. Patient records can serve as source documents from
which information about certain diseases or procedures can be taken,
for example. Although research is most prevalent in large academic
medical centers, studies are conducted in other types of health care
organizations as well.

6. Population health. Information from patient records is used to
monitor population health, assess health status, measure utilization of
services, track quality outcomes, and evaluate adherence to evidence-
based practice guidelines. Health care payers and consumers are
increasingly demanding to know the cost-effectiveness and effi cacy of
different treatment options and modalities. Population health focuses
on prevention as a means of achieving cost-effective care.

7. Public health. Federal and state public health agencies use
information from patient records to inform policies and procedures to
ensure that they protect citizens from unhealthy conditions.

Patient Records as Legal Documents

The importance of maintaining complete and accurate patient records cannot
be underestimated. They serve not only as a basis for planning patient care
but also as the legal record documenting the care that was provided to
patients. The data captured in a patient record become a permanent record
of that patient’s diagnoses, treatments, response to treatments, and case
management. Patient records provide much of the source data for health
care information that is created, maintained, and managed within and across
health care organizations.

When the patient record was a file folder full of paper housed in the health
information management department of the hospital, identifying the legal
health record (LHR) was fairly straightforward. Records kept in the usual
course of business (in this case, providing care to patients) represent an
exception to the hearsay rule, are generally admissible in a court, and there­
fore can be subpoenaed—they are legal documentation of the care provided
to the patients. With the implementation of comprehensive EHR systems
the definition of an LHR remains the same, but the identification of the
boundaries for it may be harder to determine. In 2013, the ONC’s National
Learning Consortium published the Legal Health Record Policy Template to
guide health care organizations and providers in defi ning which records and
record sets constitute their legal health record for administrative, business, or

28 · C H A P T E R 2 : H E A L T H C A R E D A T A

evidentiary purposes. The media on which the records are maintained does
not determine the legal status; rather, it is the purpose for which the record
was created and is maintained. The complete template can be found at www
.healthit.gov/sites/default/fi les/legal_health_policy_template.docx.

Because of the legal nature of patient records, the majority of states
have specific retention requirements for information contained within them.
These state requirements should be the basis for the health care organiza­
tion’s formal retention policy. (The Joint Commission and other accrediting
agencies also address retention but generally refer organizations back to their
own state regulations for specifics.) When no specific retention requirement
is made by the state, all patient information that is a part of the LHR should
be maintained for at least as long as the state’s statute of limitations or other
regulation requires. In the case of minor children the LHR should be retained
until the child reaches the age of majority as defined by state law, usually
eighteen or twenty-one. Health care executives should be aware that stat­
utes of limitations may allow a patient to bring a case as long as ten years
after the patient learns that his or her care caused an injury (Lee, 2002).
Although some specific retention requirements and general guidelines exist,
it is becoming increasingly popular for health care organizations to keep all
LHR information indefinitely, particularly if the information is stored in an
electronic format. If an organization does decide to destroy LHR information,
this destruction must be carried out in accordance with all applicable laws
and regulations.

Another important aspect related to the legal nature of patient records is
the need for them to be authenticated. State and federal laws and accredita­
tion standards require that medical record entries be authenticated to ensure
that the legal document shows the person or persons responsible for the care
provided. Generally, authentication of an LHR entry is accomplished when
the physician or other health care professional signs it, either with a hand­
written signature or an electronic signature.

Personal Health Records

An increasingly common type of patient record is maintained by the indi­
vidual to track personal health care information: the personal health record
(PHR). According to the American Health Information Management Associ­
ation (AHIMA, 2016), a PHR “is a tool .  .  . to collect, track and share past
and current information about your health or the health of someone in your
care.” A PHR is not the same as a health record managed by a health care
organization or provider, and it does not constitute a legal document of care,
but it should contain all pertinent health care information contained in an

H E A L T H C A R E D A T A A N D I N F O R M A T I O N S O U R C E S · 29

individual’s health records. PHRs are an effective tool enabling patients to be
active members of their own health care teams (AHIMA, 2016).

Patient Record Content

The following components are common to most patient records, regardless of
facility type or record system (AHIMA, 2016). Specific patient record content
is determined to a large extent by external requirements, standards, and
regulations (discussed in Chapter Nine). Keep in mind, a patient record may
contain some or all of the documentation listed. Depending on the patient’s
illness or injury and the type of treatment facility, he or she may need addi­
tional specialized health care services. These services may require specifi c
documentation. For example, long-term care facilities and behavioral health
facilities have special documentation requirements. Our list is intended to
introduce the common components of patient records, not to provide a com­
prehensive list of all possible components. The following provides a general
overview of record content and the person or persons responsible for cap­
turing the content during a single episode of care. It reveals that the patient
record is a repository for a variety of health care data and information that
is captured by many different individuals involved in the care of the patient.

• Identifi cation screen. Information found on the identifi cation screen
of a health or medical record originates at the time of registration or
admission. The identifi cation data generally includes at least the patient
name, address, telephone number, insurance carrier, and policy number,
as well as the patient’s diagnoses and disposition at discharge. These
diagnoses are recorded by the physicians and coded by administrative
personnel. (Diagnosis coding is discussed following in this chapter.)
The identifi cation component of the data is used as a clinical and an
administrative document. It provides a quick view of the diagnoses that
required care during the encounter. The codes and other demographic
information are used for reimbursement and planning purposes.

• Problem list. Patient records frequently contain a comprehensive
problem list, which identifi es signifi cant illnesses and operations the
patient has experienced. This list is generally maintained over time.
It is not specifi c to a single episode of care and may be maintained
by the attending or primary care physician or collectively by all the
health care providers involved in the patient’s care.

• Medication record. Sometimes called a medication administration record
(MAR), this record lists medicines prescribed for and subsequently
administered to the patient. It often also lists any medication allergies

30 · C H A P T E R 2 : H E A L T H C A R E D A T A

the patient may have. Nursing personnel are generally responsible for
documenting and maintaining medication information in acute care
settings, because they are responsible for administering medications
according to physicians’ written or verbal orders.

• History and physical. The history component of the report describes
any major illnesses and surgeries the patient has had, any signifi cant
family history of disease, patient health habits, and current
medications. The information for the history is provided by the patient
(or someone acting on his or her behalf) and is documented by the
attending physician or other care provider at the beginning of or
immediately prior to an encounter or treatment episode. The physical
component of this report states what the physician found when he
or she performed a hands-on examination of the patient. The history
and physical together document the initial assessment of the patient
for the particular care episode and provide the basis for diagnosis
and subsequent treatment. They also provide a framework within
which physicians and other care providers can document signifi cant
findings. Although obtaining the initial history and physical is a one­
time activity during an episode of care, continued reassessment and
documentation of that reassessment during the patient’s course of
treatment is critical. Results of reassessments are generally recorded
in progress notes.

• Progress notes. Progress notes are made by the physicians, nurses,
therapists, social workers, and other staff members caring for the
patient. Each provider is responsible for the content of his or her notes.
Progress notes should refl ect the patient’s response to treatment along
with the provider’s observations and plans for continued treatment.
There are many formats for progress notes. In some organizations all
care providers use the same note format; in others each provider type
uses a customized format. A commonly used format for a progress
note is the SOAP format. Providers are expected to enter notes divided
into four components:

o Subjective fi ndings

o Objective fi ndings

o Assessment

o Plan

• Consultation. A consultation note or report records opinions about
the patient’s condition made by another health care provider at
the request of the attending physician or primary care provider.

H E A L T H C A R E D A T A A N D I N F O R M A T I O N S O U R C E S · 31

Consultation reports may come from physicians and others inside or
outside a particular health care organization, but this information is
maintained as part of the patient record.

• Physician’s orders. Physician’s orders are a physician’s directions,
instructions, or prescriptions given to other members of the health
care team regarding the patient’s medications, tests, diets, treatments,
and so forth. In the current US health care system, procedures and
treatments must be ordered by the appropriate licensed practitioner; in
most cases this will be a physician.

• Imaging and X-ray reports. The radiologist is responsible for
interpreting images produced through X-rays, mammograms,
ultrasounds, scans, and the like and for documenting his or her
interpretations or findings in the patient’s record. These fi ndings
should be documented in a timely manner so they are available to the
appropriate provider to facilitate the appropriate treatment. The actual
digital images are generally maintained in the radiology or imaging
departments in specialized computer systems. These images are
typically not considered part of the legal patient record, per se, but in
modern EHRs they are available through the same interface.

• Laboratory reports. Laboratory reports contain the results of tests
conducted on body fl uids, cells, and tissues. For example, a medical
lab might perform a throat culture, urinalysis, cholesterol level, or
complete blood count. There are hundreds of specifi c lab tests that can
be run by health care organizations or specialized labs. Lab personnel
are responsible for documenting the lab results into the patient record.
Results of the lab work become part of the permanent patient
record. However, lab results must also be available during treatment.
Health care providers rely on accurate lab results in making clinical
decisions, so there is a need for timely reporting of lab results and
a system for ensuring that physicians and other appropriate care
providers receive the results. Physicians or other primary care providers
are responsible for documenting any findings and treatment plans
based on the lab results.

• Consent and authorization forms. Copies of consents to admission,
treatment, surgery, and release of information are an important
component of the patient record related to its use as a legal document.
The practitioner who actually provides the treatment must obtain
informed consent for the treatment. Patients must sign informed
consent documents before treatment takes place. Forms authorizing
release of information must also be signed by patients before any

32 · C H A P T E R 2 : H E A L T H C A R E D A T A

patient-specifi c health care information is released to parties not
directly involved in the care of the patient.

• Operative report. Operative reports describe any surgery performed and
list the names of surgeons and assistants. The surgeon is responsible for
documenting the information found in the operative report.

• Pathology report. Pathology reports describe tissue removed during
any surgical procedure and the diagnosis based on examination
of that tissue. The pathologist is responsible for documenting the
information contained within the pathology report.

• Discharge summary. Each acute care patient record contains a
discharge summary. The discharge summary summarizes the
hospital stay, including the reason for admission, signifi cant fi ndings
from tests, procedures performed, therapies provided, responses to
treatments, condition at discharge, and instructions for medications,
activity, diet, and follow-up care. The attending physician is
responsible for documenting the discharge summary at the conclusion
of the patient’s stay in the hospital.

With the passage of the Accountable Care Act (ACA) and other health
care payment reform measures, organizations and communities have begun
to shift focus from episodic care to population health. By defi nition, pop­
ulation health focuses on maintaining health and managing health care
utilization for a defined population of patients or community with the goal
of decreasing costs. Along with other key components, successful popula­
tion health will require extensive care coordination across care providers
and community organizations. Care managers are needed to interact with
patients on a regular basis during and in between clinical encounters (Insti­
tute for Health Technology Transformation, 2012). Needless to say, this will
have a significant impact on the form and structure of the future EHRs.
These care managers will document all plan findings, clinical and social,
within the patient’s record and rely on other providers’ notes and fi ndings
to effectively coordinate care. Baker, Cronin, Conway, DeSalvo, Rajkumar,
and Press (2016), for example, describes a new tool to support “person-cen­
tered care by a multidisciplinary team,” the comprehensive shared care
plan (CSCP), which will rely on HIT to enable collaboration across settings.
A stakeholder group organized by the US Department of Health and Human
Services developed key goals for the CSCP as they envision it:

• It should enable a clinician to electronically view information that is
directly relevant to his or her role in the care of the person, to easily

H E A L T H C A R E D A T A A N D I N F O R M A T I O N S O U R C E S · 33

identify which clinician is doing what, and to update other members
of an interdisciplinary team on new developments.

• It should put the person’s goals (captured in his or her own words) at
the center of decision making and give that individual direct access to
his or her information in the CSCP.

• It should be holistic and describe clinical and nonclinical (including
home- and community-based) needs and services.

• It should follow the person through high-need episodes (e.g., acute
illness) as well as periods of health improvement and maintenance
(Baker et al., 2016).

Figures 2.2 through 2.5 display screens from one organization’s EHR.

Claims Content

As we have seen in the previous section, health care information is captured
and stored as a part of the patient record. However, there is more to the
story: health care organizations and providers must be paid for the care
they provide. Generally, the health care organization’s accounting or billing

Figure 2.2 Sample EHR information screen

Source: Medical University of South Carolina; Epic.

34 · C H A P T E R 2 : H E A L T H C A R E D A T A

Figure 2.3 Sample EHR problem list

Source: Medical University of South Carolina; Epic.

Figure 2.4 Sample EHR progress notes

Source: Medical University of South Carolina; Epic.

H E A L T H C A R E D A T A A N D I N F O R M A T I O N S O U R C E S · 35

Figure 2.5 Sample EHR lab report

Source: Medical University of South Carolina; Epic.

department is responsible for processing claims, an activity that includes
verifying insurance coverage; billing third-party payers (private insurance
companies, Medicare, or Medicaid); and processing the payments as they
are received. Centers for Medicare and Medicaid Services (CMS) currently
requires health care providers to submit claims electronically using a set of
standard elements. As early as the 1970s the health care community strived to
develop standard insurance claim forms to facilitate payment collection. With
the nearly universal adoption of electronic billing and government-mandated
transaction standards, standard claims content has become essential.

Depending on the type of service provided to the patient, one of two
standard data sets will be submitted to the third-party payer. The UB-04, or
CMS-1450, is submitted for inpatient, hospital-based outpatient, home health
care, and long-term care services. The CMS-1500 is submitted for health care
provider services, such as those provided by a physician’s office. It is also
used for billing by some Medicaid state agencies. The standard requirements
for the parallel electronic counterparts to the CMS-1450 and CMS-1500 are
defined by ANSI ASC X12N 837I (Institutional) and ANSI ASC X12N 837P
(Professional), respectively. Therefore, the claims standards are frequently
referred to as 837I and 837P.

UB-04/CMS-1450/837I

In 1975, the American Hospital Association (AHA) formed the National
Uniform Billing Committee (NUBC), bringing the major national provider and

36 · C H A P T E R 2 : H E A L T H C A R E D A T A

payer organizations together for the purpose of developing a single billing form
and standard data set that could be used for processing health care claims
by institutions nationwide. The first uniform bill was the UB-82. It has since
been modified and improved on, resulting, first, in the UB-92 data set and now
in the currently used UB-04, also known as CMS-1450. UB-04 is the de facto
institutional provider claim standard. Its content is required by CMS and has
been widely adopted by other government and private insurers. In addition to
hospitals, UB-04 or 837I is used by skilled nursing facilities, end stage renal
disease providers, home health agencies, hospices, rehabilitation clinics and
facilities, community mental health centers, critical access hospitals, federally
qualified health centers, and others to bill their third-party payers. The NUBC
is responsible for maintaining and updating the specifications for the data
elements and codes that are used for the UB-04/CMS-1450 and 837I. A full
description of the elements required and the specifications manual can be
found on the NUBC website, www.nubc.org (CMS 2016a; NUBC, 2016).

CMS-1500/837P

The National Uniform Claim Committee (NUCC) was created by the Amer­
ican Medical Association (AMA) to develop a standardized data set for the
noninstitutional or “professional” health care community to use in the sub­
mission of claims (much as the NUBC has done for institutional providers).
Members of this committee represent key provider and payer organizations,
with the AMA appointing the committee chair. The standardized claim form
developed and overseen by NUCC is the CMS-1500 and its electronic coun­
terpart is the 837P. This standard has been adopted by CMS to bill Medicare
fee-for-service, and similar to UB-04 and 837I for institutional care, it has
become the de facto standard for all types of noninstitutional provider claims,
such as those for private physician services. NUCC maintains a crosswalk
between the 837P and CMS-1500 explaining the specific data elements, which
can be found on their website at www.nucc.org (CMS, 2013; NUCC, 2016).

It is important to recognize that the UB-04 and the CMS-1500 and their
electronic counterparts incorporate standardized data sets. Regardless of a
health care organization’s location or a patient’s insurance coverage, the same
data elements are collected. In many states UB-04 data and CMS-1500 data
must be reported to a central state agency responsible for aggregating and
analyzing the state’s health data. At the federal level the CMS aggregates the
data from these claims forms for analyzing national health care reimburse­
ment and clinical and population trends. Having uniform data sets means
that data can be compared not only within organizations but also within
states and across the country.

H E A L T H C A R E D A T A A N D I N F O R M A T I O N S O U R C E S · 37

Diagnostic and Procedural Codes

Diagnostic and procedural codes are captured during the patient encounter,
not only to track clinical progress but also for billing, reimbursement, and
other administrative purposes. This diagnostic and procedural information
is initially captured in narrative form through physicians’ and other health
care providers’ documentation in the patient record. This documentation is
subsequently translated into numerical codes. Coding facilitates the classi­
fication of diagnoses and procedures for reimbursement purposes, clinical
research, and comparative studies.

Two major coding systems are employed by health care providers today:

• ICD-10 (International Classification of Diseases)

• CPT (Current Procedural Terminology), published by the American

Medical Association

Use of these systems is required by the federal government for reimburse­
ment, and they are recognized by health care agencies nationally and inter­
nationally. The UB-04 and CMS-1500 have very specific coding requirements
for claim submission, which include use of these coding sets.

ICD-10-CM

The ICD-10 classification system used to code diseases and other health
statuses in the United States is derived from the International Classifi ca­
tion of Diseases, Tenth Revision, which was developed by the World Health
Organization (WHO) (CDC, 2016) to capture disease data. The precursors to
the current ICD system were developed to enable comparison of morbidity
(illness) and mortality (death) statistics across nations. Over the years this
basic purpose has evolved and today ICD-10-CM (Clinical Modifi cation)
coding plays major role in reimbursement to hospitals and other health
care institutions. ICD-10-CM codes used for determining the diagnosis
related group (DRG) into which a patient is assigned. DRGs are in turn the
basis for determining appropriate inpatient reimbursements for Medicare,
Medicaid, and many other health care insurance benefi ciaries. Accurate
ICD coding has, as a consequence, become vital to accurate institutional
reimbursement.

The National Center of Health Statistics (NVHS) is the federal agency
responsible for publishing ICD-10-CM (Clinical Modification) in the United
States. Procedure information is similarly coded using the ICD-10-PCS (Pro­
cedural Coding System). ICD-10-PCS was developed by CMS for US inpatient

– –

38 · C H A P T E R 2 : H E A L T H C A R E D A T A

Exhibit 2.1 Excerpt from ICD 10 CM 2016

Malignant neoplasms (C00-C96)

Malignant neoplasms, stated or presumed to be primary (of specifi ed

sites), and certain specified histologies, except neuroendocrine, and of

lymphoid, hematopoietic, and related tissue (C00-C75)

Malignant neoplasms of lip, oral cavity, and pharynx (C00-C14)

C00 Malignant neoplasm of lip

Use additional code to identify:

alcohol abuse and dependence (F10.-)

history of tobacco use (Z87.891)

tobacco dependence (F17.-)

tobacco use (Z72.0)

Excludes 1: malignant melanoma of lip (C43.0)

Merkel cell carcinoma of lip (C4A.0)

other and unspecifi ed malignant neoplasm of skin of lip (C44.0-)

C00.0 Malignant neoplasm of external upper lip

Malignant neoplasm of lipstick area of upper lip

Malignant neoplasm of upper lip NOS

Malignant neoplasm of vermilion border of upper lip

C00.1 Malignant neoplasm of external lower lip

Malignant neoplasm of lower lip NOS

Malignant neoplasm of lipstick area of lower lip

Malignant neoplasm of vermilion border of lower lip

hospital settings only. The ICD-10-CM and ICD-10-PCS publications are
considered federal government documents whose contents may be used
freely by others. However, multiple companies republish this government
document in easier-to-use, annotated, formally copyrighted versions. In
general, the ICD-10-CM and ICD-10-PCS are updated on an annual basis
(CMS, 2015, 2016b).

Exhibits 2.1 and 2.2 are excerpts from the ICD-10-CM and ICD-10-PCS
classification systems. They show the system in its text form, but large health
care organizations generally use encoders, computer applications that facil­
itate accurate coding. Whether a book or text file or encoder is used, the
classification system follows the same structure.

H E A L T H C A R E D A T A A N D I N F O R M A T I O N S O U R C E S · 39

C00.2 Malignant neoplasm of external lip, unspecifi ed

Malignant neoplasm of vermilion border of lip NOS

C00.3 Malignant neoplasm of upper lip, inner aspect

Malignant neoplasm of buccal aspect of upper lip

Malignant neoplasm of frenulum of upper lip

Malignant neoplasm of mucosa of upper lip

Malignant neoplasm of oral aspect of upper lip

C00.4 Malignant neoplasm of lower lip, inner aspect

Malignant neoplasm of buccal aspect of lower lip

Malignant neoplasm of frenulum of lower lip

Malignant neoplasm of mucosa of lower lip

Malignant neoplasm of oral aspect of lower lip

C00.5 Malignant neoplasm of lip, unspecifi ed, inner aspect

Malignant neoplasm of buccal aspect of lip, unspecifi ed

Malignant neoplasm of frenulum of lip, unspecifi ed

Malignant neoplasm of mucosa of lip, unspecifi ed

Malignant neoplasm of oral aspect of lip, unspecifi ed

C00.6 Malignant neoplasm of commissure of lip, unspecifi ed

C00.7 Malignant neoplasm of overlapping sites of lip

C00.8 Malignant neoplasm of lip, unspecifi ed

Source: CMS (2016b).

CPT and HCPCS

The American Medical Association (AMA) publishes an updated CPT each
year. Unlike ICD-9-CM, CPT is copyrighted, with all rights to publication and
distribution held by the AMA. CPT was first developed and published in 1966.
The stated purpose for developing CPT was to provide a uniform language for
describing medical and surgical services. In 1983, however, the government
adopted CPT, in its entirety, as the major component (known as Level 1) of
the Healthcare Common Procedure Coding System (HCPCS). Since then CPT
has become the standard for physician’s office, outpatient, and ambulatory
care coding for reimbursement purposes. Exhibit 2.3 is a simplifi ed example
of a patient encounter form with HCPCS/CPT codes.

–

40 · C H A P T E R 2 : H E A L T H C A R E D A T A

Exhibit 2.2 Excerpt from ICD 10 PCS 2017 OCW

Section 0 Medical and Surgical

Body System C Mouth and Throat

Operation W Revision: Correcting, to the extent possible, a portion of a
malfunctioning device or the position of a displaced device

Body Part Approach Device Qualifi er

A Salivary Gland 0 Open

3 Percutaneous

X External

0 Drainage Device

C Extraluminal
Device

Z No Qualifi er

S Larynx 0 Open

3 Percutaneous

7 Via Natural or
Artifi cial Opening

8 Via Natural or
Artifi cial Opening
Endoscopic

X External

0 Drainage Device

7 Autologous

Z No Qualifi er

Tissue Substitute

D Intraluminal
Device

J Synthetic
Substitute

K Nonautologous
Tissue Substitute

Y Mouth and 0 Open 0 Drainage Device Z No Qualifi er
Throat

3 Percutaneous 1 Radioactive

7 Via Natural or
Artifi cial Opening 7 Autologous

8 Via Natural or
Artifi cial Opening D Intraluminal
Endoscopic

X External

Device

Element

Tissue Substitute

J Synthetic
Substitute

K Nonautologous
Tissue Substitute

Source: CMS (2016c).

H E A L T H C A R E D A T A A N D I N F O R M A T I O N S O U R C E S · 41

Exhibit 2.3 Patient encounter form coding standards

Pediatric Associates P.A. 123 Children’ s Avenue, Anytown, USA

Offi ce Visits
99211 Estab Pt—minimal Preventive Medicine—New
99212 Estab Pt—focused 99381 Prev Med 0–1 years
99213 Estab Pt—expanded 99382 Prev Med 1–4 years
99214 Estab Pt—detailed 99383 Prev Med 5–11 years
99215 Estab Pt—high complexity 99384 Prev Med 12–17 years

99385 Prev Med 18–39 years
99201 New Pt—problem focused
99202 New Pt—expanded Preventive Medicine—Established
99203 New Pt—detailed 99391 Prev Med 0–1 years
99204 New Pt—moderate complexity 99392 Prev Med 1–4 years

99205 New Pt—high complexity 99393 Prev Med 5–11 years
99394 Prev Med 12–17 years

99050 After Hours 99395 Prev Med 18–39 years
99052 After Hours—after 10 pm
99054 After Hours—Sundays and Holidays 99070 10 Arm Sling

99070 11 Sterile Dressing
Outpatient Consult 99070 45 Cervical Cap
99241 99242 99243 99244 99245

Immunizations, Injections, and Office Laboratory Services
90471 Adm of Vaccine 1 81000 Urinalysis w/ micro
90472 Adm of Vaccine > 1 81002 Urinalysis w/o micro
90648 HIB 82270 Hemoccult Stool
90658 Infl uenza 82948 Dextrostix
90669 Prevnar 83655 Lead Level
90701 DTP 84030 PKU
90702 DT 85018 Hemoglobin
90707 MMR 87086 Urine Culture
90713 Polio Injection 87081 Throat Culture
90720 DTP/HIB 87205 Gram Stain
90700 DTaP 87208 Ova Smear (pin worm)
90730 Hepatitis A 87210 Wet Prep
90733 Meningococcal 87880 Rapid Strep
90744 Hepatitis B 0–11
90746 Hepatitis B 18+ years

Diagnosis
Patient Name
No.
Date
Time
Address
DOB
Name of Insured ID
Insurance Company
Return Appointment ___________________________________________________

42 · C H A P T E R 2 : H E A L T H C A R E D A T A

As coding has become intimately linked to reimbursement, directly deter­
mining the amount of money a health care organization can receive for a
claim from insurers, the government has increased its scrutiny of coding
practices. There are official guidelines for accurate coding, and health care
facilities that do not adhere to these guidelines are liable to charges of fraud­
ulent coding practices. In addition, the Office of Inspector General of the
Department of Health and Human Services (HHS OIG) publishes compliance
guidelines to facilitate health care organizations’ adherence to ethical and
legal coding practices. The OIG is responsible for (among other duties) investi­
gating fraud involving government health insurance programs. More specifi c
information about compliance guidelines can be found on the OIG website
(www.oig.hhs.gov) and will be more thoroughly discussed in Chapter Nine.

HEALTH CARE DATA USES

The previous sections of this chapter examine how health care data is cap­
tured in patient records and billing claims. Even with this brief overview you
can begin to see what a rich source of health care data these records could be.
However, before health care data can be used, it must be stored and retrieved.
How do we retrieve that data so that the information can be aggregated,
manipulated, or analyzed for health care organizations to improve patient
care and business operations? How do we combine this patient care data
created and stored internally with other pertinent data from external sources?

As we discussed previously in the chapter, data need to be processed
to become information. We also noted that data and information may be
considered along a continuum, one person’s data may be another person’s
information depending on the level of processing required. In this section of
the chapter we will focus on the use of data analysis to transform data into
information. There is a lot of discussion about the current and future impact
of so-called big data on the health care community. We will start the dis­
cussion of data analysis by looking at the basic elements required to perform
effective health care data analysis, followed by a comparison of “small” data
analysis examples to the emerging big data.

Regardless of the scope of the data or the tools used, health care data
analysis requires basic elements. First, there must be a source of data, for
example, the EHR, claims data, laboratory data, and so on. Second, these
data must be stored in a retrievable manner, for example, in a database
or data warehouse. Next, an analytical tool, such as mathematical statistics,
probability models, predictive models, and so on, must be applied to the
stored data. Finally, to be meaningful, the analyzed data must be reported
in a usable manner.

H E A L T H C A R E D A T A U S E S · 43

Databases and Data Warehouses

A database generally refers to any structured, accessible set of data stored elec­
tronically; it can be large or small. The back end of EHR and claims systems
are examples of large databases. A data warehouse differs from a database
in its structure and function. In health care, data warehouses that are derived
from health care information systems may be referred to as clinical data repos­
itories. The data in a data warehouse come from a variety of sources, such
as the EHR, claims data, and ancillary health care information systems (lab­
oratory, radiology, etc.). The data from the sources are extracted, “cleaned,”
and stored in a structure that enables the data to be accessed along multiple
dimensions, such as time (e.g., day, month, year); location; or diagnosis. Data
warehouses help organizations transform large quantities of data from sep­
arate transactional files or other applications into a single decision-support
database. The important concept to understand is that the database or data
warehouse provides organized storage for data so that they can be retrieved
and analyzed. Before useful information can be obtained, the data must be
analyzed. In the most straightforward uses, the data from the data stores are
aggregated and reported using simple reporting or statistical methods.

Small versus Big Data

Data stores and data analytics are not new to health care. However, the scope
and speed with which we are now capable of analyzing data and discovering
new information has increased tremendously. Big data is not a data store
(warehouse or database), nor is it a specific analytical tool, but rather it refers
to a combination of the two. Experts describe big data as characterized by
three Vs (the fourth V—veracity, or accuracy—is sometimes added). These
characteristics are present in big but not small data:

• Very large volume of data

• A variety (e.g., images, text, discrete) of types and sources (EHR,

wearable fi tness technology, social media, etc.) of data

• The velocity at which the data is accumulated and processed (Glaser,
2014; Macadamian, n.d.)

Harris and Schneider (2015) describe a useful metaphor for explaining the
difference between big data and traditional data storage and analysis systems.
They tell us to consider “even enormous databases, such as the Medicare
claims database as ‘filing cabinets,’ while big data is more like a ‘conveyor

44 · C H A P T E R 2 : H E A L T H C A R E D A T A

belt.’ The filing cabinet no matter how large, is static, while the conveyor belt
is constantly moving and presenting new data points and even data sources”
(p. 53). They further provide the following examples of questions answered
by big versus small data in health care:

o What are the effects of our immunization programs? versus Is my
child growing as expected?

o What are some the healthiest regions? versus Is this medication
improving my (or my patients’) blood pressure?

Small Data Examples

Disease and Procedure Indexes

Health care management often wants to know summary information about a
particular disease or treatment. Examples of questions that might be asked
are What is the most common diagnosis among patients treated in the facil­
ity? What percentage of patients with diabetes is African American? What is
the most common procedure performed on patients admitted with gastritis
(or heart attack or any other diagnosis)? Traditionally, such questions have
been answered by looking in disease and procedure indexes. Prior to EHRs
and their resulting databases, disease and procedure indexes were large
card catalogues or books that kept track of the numbers of diseases treated
and procedures occurring in a facility by disease and procedure codes. Now
that repositories of health care data are common, the disease and procedure
index function is generally handled as a component of the EHR. The retrieval
of information related to diseases and procedures is still based on ICD and
CPT codes, but the queries are limitless. Users can search the disease and
procedure database for general frequency statistics for any number of combi­
nations of data. Figure 2.6 is an example of a screen resulting from a query
for a specific patient, Iris Hale, who has been identified as a member of both
the Heart Failure and Hypertension registries.

Many other types of aggregate clinical reports are used by health
care providers and executives. Ad hoc reporting capability applied to
clinical databases gives providers and executives access to any number
of summary reports based on the data elements from patient health and
claims records.

Health Care Statistics

Utilization and performance statistics are routinely gathered for health care
executives. This information is needed for facility and health care provision

H E A L T H C A R E D A T A U S E S · 45

planning and improvement. Statistical reports can provide managers and
executives a snapshot of their organization’s performance.

Two categories of statistics directly related to inpatient stays are routinely
captured and reported. Many variations of these reports and others that drill
down to more granular level of data also exist.

• Census statistics. These data reveal the number of patients present
at any one time in a facility. Several commonly computed rates are
based on these census data, including the average daily census and bed
occupancy rates.

• Discharge statistics. This group of statistics is calculated from
data accumulated when patients are discharged. Some commonly
computed rates based on discharge statistics are average length of stay,
death rates, autopsy rates, infection rates, and consultation rates.

Outpatient facilities and group practices, specialty providers, and so on
also routinely collect utilization statistics. Some of the more common statis­
tics are average patient visits per month (or year) and percentage of patients
achieving a health status goal, such as immunizations or smoking cessa­
tion. The number of descriptive health care statistics that can be produced
is limitless. Health care organizations also track a wide variety of fi nancial

Figure 2.6 Sample heart failure and hypertension query screen

Source: Cerner Corporation (2016). Used with permission.

46 · C H A P T E R 2 : H E A L T H C A R E D A T A

performance, patient satisfaction, and employee satisfaction data. Patient
and employee data generally come from surveys that are routinely adminis­
tered. The body of data collected and analyzed is driven by the mission of
the organization, along with reporting requirements from state, federal, and
accrediting organizations.

Health care organizations also look to data to guide improved perfor­
mance and patient satisfaction. Performance data are essential to health
care leaders; however, because they are generally managed within a quality
or performance improvement department and are not derived from health
care data, per se, they will not be discussed in depth in this chapter. A few
significant external agencies that report performance data, however, will be
discussed in Chapter Nine.

Although each organization will determine which daily, monthly, and
yearly statistics they need to track based on their individual service missions,
Rachel Fields (2010) in an article published by Becker’s Hospital Review pro­
vides a list of ten common measures identified by a panel of fi ve hospital
leaders, as shown in Table 2.1.

Big Data Examples

Health care organizations today contend with data from EHRs, internal
databases, data warehouses, as well as the availability of data from the
growing volume of other health-related sources, such as diagnostic imaging
equipment, aggregated pharmaceutical research, social media, and personal
devices such as Fitbits and other wearable technologies. No longer is the data
needed to support health care decisions located within the organization or
any single data source. As we begin to manage populations and care con­
tinuums we have to bring together data from hospitals, physician practices,
long-term care facilities, the patient, and so on. These data needs are bigger
than the data needs we had (and still have) when we focused primarily on
inpatient care.

Big data is a practice that is applied to a wide range of uses across a wide
range of industries and efforts, including health care. There is no single big data
product, application, or technology, but big data is broadening the range of data
that may be important in caring for patients. For instance, in the case of Alz­
heimer’s and other chronic diseases such as diabetes and cancer, online social
sites not only provide a support community for like-minded patients but also
contain knowledge that can be mined for public health research, medication
use monitoring, and other health-related activities. Moreover, popular social
networks can be used to engage the public and monitor public perception and
response during flu epidemics and other public health threats (Glaser, 2014).

H E A L T H C A R E D A T A U S E S · 47

Table 2.1 Ten common hospital statistical measures

Daily Monthly Yearly

1. Quality measures, 4. Point-of-service cash
such as collections

Infection rates 5. Percentage of charity care

Patient falls 6. Percentage of budget spent

Overall mortality
for each department

2. Patient census
7. Door-to-discharge time

statistics 8. Patient satisfaction scores

By physician

By service line

3. Discharged but not
fi nal billed

9. Colleague satisfaction
scores

10. Market share
and service line
development

Source: Fields (2010).

As important and perhaps more important than the data themselves are
the novel analytics that are being developed to analyze these data. In health
care we see an impressive range of analytics:

• Post-market surveillance of medication and device safety

• Comparative effectiveness research (CER)

• Assignment of risk, for example, readmissions

• Novel diagnostic and therapeutic algorithms in areas such as oncology

• Real-time status and process surveillance to determine, for example,
abnormal test follow-up performance and patient compliance with
treatment regimes

• Determination of structure including intent, for example, identifying
treatment patterns using a range of structured and unstructured and
EHR and non-EHR data

• Machine correction of data-quality problems

The potential impact of applying data analytics to big data is huge.
McKinsey & Company (Kayyil, Knott, & Van Kuiken, 2013) estimates that
big data initiatives could account for $300 to $450 billion in reduced
health care spending, or 12 to 17 percent of the $2.6 trillion baseline in
US health care costs. There are several early examples of possibly profound

48 · C H A P T E R 2 : H E A L T H C A R E D A T A

impact. For example, an analysis of the cumulative sum of monthly
hospitalizations because of myocardial infarction, among other clinical and
cost data, led to the discovery of arthritis drug Vioxx’s adverse effects
and its subsequent withdrawal from the market in 2004.

A Deloitte (2011) analysis identifi ed five areas of analysis that will be
crucial in the emerging era of providers being held more accountable for the
care delivered to a patient and a population:

• Population management analytics. Producing a variety of clinical
indicator and quality measure dashboards and reports to help improve
the health of a whole community, as well as help identify and manage
at-risk populations

• Provider profiling/physician performance analytics. Normalizing
(severity and case mix–adjusted profiling), evaluating, and reporting
the performance of individual providers (PCPs and specialists)
compared to established measures and goals

• Point of care (POC) health gap analytics. Identifying patient­
specifi c health care gaps and issuing a specifi c set of actionable
recommendations and notifi cations either to physicians at the point of
care or to patients via a patient portal or PHR

• Disease management. Defining best practice care protocols over
multiple care settings, enhancing the coordination of care, and
monitoring and improving adherence to best practice care protocols

• Cost modeling/performance risk management/comparative
effectiveness. Managing aggregated costs and performance risk and
integrating clinical information and clinical quality measures

HEALTH CARE DATA QUALITY

Up to this point, this chapter has examined health care data and information
with a focus on the origins and uses of such. Changes to the health care
delivery system and payment reform are amending the ways in which we
use health care information. Traditionally, patient clinical and claims records
were used primarily to document episodic care or, at best, the care received
by an individual across the continuum, as long as that care was provided
through a single organization. In today’s environment, care providers, care
coordinators, analysts, and researchers are all looking to EHRs and electronic
claims records as a source of data beyond the episodic scope. Any discussion
of health care data analytics and big data include the EHR as a key data

H E A L T H C A R E D A T A Q U A L I T Y · 49

source. This expanded use of electronic records and the push for bigger and
better data analytics has raised the bar for ensuring the quality of the health
care data. Quality health care data has always been important, but the criteria
for what constitute high-quality data have shifted.

There are many operational definitions for quality. Two of the best known
were developed by the well-known quality “gurus,” Philip B. Crosby and
Joseph M. Juran. Crosby (1979) defines quality as “conformance to require­
ments” or conformance to standards. Juran (Juran & Gryna, 1988) defi nes
quality as “fitness for use,” products or services must be free of defi ciencies.
What these definitions have in common is that the criteria against which
quality is measured will change depending on the product, service, or use.
Herein lies the problem with adopting a single standard for health care data
quality—it depends on the use of the data.

EHRs evolved from patient medical records, whose central purpose was to
document and communicate episodes of patient care. Today EHRs are being
evaluated as source data for complex data analytics and clinical research.
Before an organization can measure the quality of the information it produces
and uses, it must establish data standards. And before it can establish data
standards it must identify all endorsed uses of the EHR.

Consider this scenario. EHRs contain two basic types of data: struc­
tured data that is quantifiable or predefined and unstructured data that is
narrative. Within a health care organization, the clinicians using the EHR
for patient care prefer unstructured data, because it is easier to dictate a
note than to follow a lengthy point and click pathway to create a struc­
tured note. The clinicians feel that the validation screens cost time that is
too valuable for them to waste. The researchers within the organization,
however, want as much of the data in the record as possible to be structured
to avoid missing data and data entry errors. What should the organization
adopt as its standard? Structured or unstructured data? Who will decide
and based on what criteria? This discussion between the primary use of
EHR data and secondary, or reuse, of data is likely to continue. However,
to effectively use EHR data to create new knowledge, either through ana­
lytics or research, will require HIT leaders to adopt the more stringent data
quality criteria posed by these uses. Wells, Nowacki, Chagin, and Kattan
(2013) identify missing data as particularly problematic when using the
EHR for research purposes. They further identify two main sources of
missing EHR data:

1. Data were not collected. A patient was never asked about a condition.
This is most likely directly related to the clinician’s lack of interest
in what would be considered irrelevant to the current episode of

50 · C H A P T E R 2 : H E A L T H C A R E D A T A

care. Few clinicians will take a full history, for example, at every
encounter.

2. Documentation was not complete. The patient was asked, but it was
not noted in the record. This is common in the EHR when clinicians
only note positive values and leave negative values blank. For
example, if a patient states that he or she does not have a history
of cancer, no note will be made, either positive or negative. For a
researcher this creates issues. Is this missing data or a negative
value?

Although there is no single common standard against which health care
data quality can be measured, there are useful frameworks for organizations
to use to evaluate health care quality (once the purpose for the data is clearly
determined).

The following section will examine two different frameworks for eval­
uating health care data quality. The first was developed by the American
Health Information Management Association (AHIMA) (Davoudi et al.,
2015), the second by Weiskopf and Weng (2013). The AHIMA framework
is set in the context of managing health care data quality across the enter­
prise. The Weiskopf and Weng framework was delineated after in-depth
research into the quality of data specifically found within an EHR, as cur­
rently used. Common health data quality issues will be examined using
each framework.

AHIMA Data Quality Characteristics

AHIMA developed and published a set of health care data quality character­
istics as a component of a comprehensive data quality management model.
They define data quality management as “the business processes that ensure
the integrity of an organization’s data during collection, application (includ­
ing aggregation), warehousing, and analysis” (Davoudi et al., 2015). These
characteristics are to be measured for conformance during the entire data
management process.

• Data accuracy. Data that refl ect correct, valid values are accurate.
Typographical errors in discharge summaries and misspelled names
are examples of inaccurate data.

• Data accessibility. Data that are not available to the decision makers
needing them are of no value to those decision makers.

H E A L T H C A R E D A T A Q U A L I T Y · 51

• Data comprehensiveness. All of the data required for a particular use
must be present and available to the user. Even relevant data may not
be useful when they are incomplete.

• Data consistency. Quality data are consistent. Use of an abbreviation
that has two different meanings is a good example of how lack of
consistency can lead to problems. For example, a nurse may use
the abbreviation CPR to mean cardiopulmonary resuscitation at one
time and computer-based patient record at another time, leading to
confusion.

• Data currency. Many types of health care data become obsolete after a
period of time. A patient’s admitting diagnosis is often not the same as
the diagnosis recorded on discharge. If a health care executive needs a
report on the diagnoses treated during a particular time frame, which
of these two diagnoses should be included?

• Data defi nition. Clear definitions of data elements must be provided
so that current and future data users will understand what the data
mean. This issue is exacerbated in today’s health care environment of
collaboration across organizations.

• Data granularity. Data granularity is sometimes referred to as data
atomicity. That is, individual data elements are “atomic” in the sense
that they cannot be further subdivided. For example, a typical patient’s
name should generally be stored as three data elements (last name,
first name, middle name—”Smith” and “John” and “Allen”), not as a
single data element (“John Allen Smith”). Again, granularity is related
to the purpose for which the data are collected. Although it is possible
to subdivide a person’s birth date into separate fi elds for the month, the
date, and the year, this is usually not desirable. The birth date is at its
lowest practical level of granularity when used as a patient identifi er.
Values for data should be defined at the correct level for their use.

• Data precision. Precision often relates to numerical data. Precision
denotes how close to an actual size, weight, or other standard a
particular measurement is. Some health care data must be very
precise. For example, in figuring a drug dosage it is not all right
to round up to the nearest gram when the drug is to be dosed in
milligrams.

• Data relevancy. Data must be relevant to the purpose for which they
are collected. We could collect very accurate, timely data about a
patient’s color preferences or choice of hairdresser, but are these
matters relevant to the care of the patient?

52 · C H A P T E R 2 : H E A L T H C A R E D A T A

Table 2.2 Terms used in the literature to describe the fi ve common dimensions of
data quality

Completeness Correctness Concordance Plausibility Currency

Accessibility Accuracy Agreement Accuracy Recency
Accuracy Corrections made Consistency Believability Timeliness
Availability Errors Reliability Trustworthiness

Missingness Misleading Variation Validity
Omission Positive predictive

value
Presence Quality
Quality Validity
Rate of recording
Sensitivity
Validity

Source: Weiskopf and Weng (2013). Reproduced with permission of Oxford University
Press.

• Data timeliness. Timeliness is a critical dimension in the quality of
many types of health care data. For example, critical lab values must
be available to the health care provider in a timely manner. Producing
accurate results after the patient has been discharged may be of little
or no value to the patient’s care.

Weiskopf and Weng Data Quality Dimensions

Weiskopf and Weng (2013) published a review article in the Journal of the
American Medical Informatics Association that identifi ed five dimensions of
EHR data quality. They based their findings on a pool of ninety-fi ve arti­
cles that examined EHR data quality. Their context was using the EHR for
research, that is, “reusing” the EHR data. Although different terms were
used in the articles, the authors were able to map the terms to one of the
five dimensions (see Table 2.2):

• Completeness: Is the truth about a patient present?

• Correctness: Is an element that is in the EHR true?

• Concordance: Is there agreement between elements in the EHR or
between the EHR and another data source?

• Plausibility: Does an element in the EHR make sense in light of other
knowledge about what that element is measuring?

H E A L T H C A R E D A T A Q U A L I T Y · 53

PERSPECTIVE
Problems with Reusing EHR Data:

Examples from the Literature

Botsis, T., Hartvigsen, G., Chen, F., & Weng, C. (2010). Secondary use
of EHR: Data quality issues and informatics opportunities. Summit on
Translational Bioinformatics, 2010, 1–5.

The authors report on data quality issues they encountered when
attempting to use data that originated in an EHR to conduct survival
analysis of pancreatic cancer patients treated at a large medical center
in New York City. They found that of 3,068 patients within the clini­
cal data warehouse, only 1,589 had appropriate disease documentation
within a pathology report. The sample size was further reduced to 522
when the researchers discovered incompleteness of key study variables.
Other instances of incompleteness and inaccuracies were found within
the remaining 522 subjects’ documentation, causing the researchers to
make inferences regarding some of the non-key study variables.

Bayley, K. B., Belnap, T., Savitz, L., Masica, A. L., Shah, N., & Fleming,
N. S. (2013). Challenges in using electronic health record data for CER.
Medical Care, 51(8 Suppl 3), S80–S86. doi:10.1097/mlr.0b013e31829b1d48

The authors conducted research to determine the “strengths and
challenges” of using EHRs for CER across four major health care systems
with mature EHR systems. They looked at comparing the effectiveness of
antihypertensive medications on blood pressure control for a population
of patients with hypertension who were being followed by primary care
providers within the health systems. Data quality problems that were
identifi ed included the following:

• Missing data

• Erroneous data

• Uninterpretable data

• Inconsistent data

• Text notes and noncoded data

The authors concluded that the potential for EHRs as a source of longi­
tudinal data for comparative effectiveness studies in populations is high,
but they note that “improving data quality within the EHR in order to
facilitate research will remain a challenge as long as research is seen as
a separate activity from clinical care.”

54 · C H A P T E R 2 : H E A L T H C A R E D A T A

• Currency: Is an element in the EHR a relevant representation of the
patient state at a given point in time?

The authors further identify completeness, correctness, and currency as
“fundamental,” stating that concordance and plausibility “appear to be proxies
for the fundamental dimensions when it is not possible to assess them directly.”

Strategies for Minimizing Data Quality Issues

As a beginning point, health care data standardization requires clear, con­
sistent definitions. One essential tool for identifying and ensuring the use of
standard data definitions is to use a data dictionary. AHIMA defines a data
dictionary as “a descriptive list of names (also called ‘representations’ or
‘displays’), definitions, and attributes of data elements to be collected in an
information system or database” (Dooling, Goyal, Hyde, Kadles, & White, 2014,
p. 7) (see Table 2.3).

Regardless of how well data are defined, however, errors in entry will
occur. These errors can be discussed in terms of two types of underlying
cause—systematic errors and random errors. Systematic errors are errors that
can be attributed to a flaw or discrepancy in the system or in adherence
to standard operating procedures or systems. Random errors, however, are
caused by carelessness, human error, or simply making a mistake.

Consider these scenarios:

• A nurse is required to document vital signs into each patient’s EHR
at the beginning of each visit. However, the data entry screen is
cumbersome and often the nurse must wait until the end of day and
go back to update the vital signs. On occasion the EHR locks up
and does not allow the nurse to update the information. This is an
example of a systematic error.

• A physician uses the structured history and physical module of the
EHR within her practice. However, to save time she cuts and pastes
information from one visit to another. During cutting and pasting,
she fails to reread her note and leaves in the wrong encounter date.
Although there are some elements of systematic error in this situation
(not following protocol), the error is primarily a random error.

Effective systems are needed to ensure preventable errors are minimized
and errors that are not preventable are easily detected and corrected. Clearly,
there are multiple points during data collection and processing when the
system design can reduce data errors.

H E A L T H C A R E D A T A Q U A L I T Y · 55

The Markle Foundation (2006, p. 4) argues that comprehensive data
quality programs are needed by health care organizations to prevent “dirty
data” and subsequently improve the quality of patient care. They propose that
a data quality program include “automated and human strategies”:

• Standardizing data entry fi elds and processes for entering data

• Instituting real-time quality checking, including the use of validation
and feedback loops

• Designing data elements to avoid errors (e.g., using check digits,

algorithms, and well-designed user interfaces)

• Developing and adhering to guidelines for documenting the care that
was provided

• Building human capacity, including training, awareness-building, and
organizational change

Health care data quality problems are exacerbated by inter-facility collab­
orations and health information exchange. Imagine standardizing processes
and definitions across multiple organizations.

Certainly, information technology has tremendous potential as a tool
for improving health care data quality. Through the use of electronic data
entry, users can be required to complete certain fields, prompted to add
information, or warned when a value is out of prescribed range. When
health care providers respond to a series of prompts, rather than dictating
a free-form narrative, they are reminded to include all necessary elements
of a health record entry. Data quality is improved when these systems also
incorporate error checking. Structured data entry, drop-down lists, and
templates can be incorporated to promote accuracy, consistency, and com­
pleteness (Wells et al., 2013). To date some of this potential for technology-
enhanced improvements has been realized, but many opportunities remain.
As noted in the Perspective many of the data in existing EHR systems are
recorded in an unstructured format, rather than in data fields designated to
contain specific pieces of information, which can lead to poor health care
data quality. Natural language processing (NLP) is a promising, evolving
technology that will enable efficient data extraction from the unstructured
components of the EHR, but it is not yet commonplace with health care
systems.

A clear example of data quality improvement achieved through informa­
tion technology is the result seen from incorporating medication adminis­
tration systems designed to prevent medication error. With structured data
input and sophisticated error prevention, these systems can signifi cantly

Table 2.3 Excerpt from data dictionary used by AHRQ surgical site infection risk stratifi cation/outcome detection

Table Field Datatype Description

PATIENT Include patients who had surgery that meet inclusion CPT, SNOMED,
or ICD-9 criteria between 1/1/2007 and 1/30/2009.

PATIENT DOB Date The birthdate for the patient

PATIENT PATIENT_ID Integer A unique ID for the patient

PATIENT DATA_SOURCE_ID Varchar(10) An identifi er for the source of the patient record data (UU, IHC, DH
for example)

DIAGNOSIS Include ICD-9 CM discharge codes within one month of surgery. A
list of included codes is in table 2 of Stevenson et al. AJIC vol 36 (3)
155–164.

DIAGNOSIS DIAGNOSIS_ID Integer A unique ID for the diagnosis

DIAGNOSIS DIAGNOSIS_CODE Varchar(64) The code for the patient’s diagnosis

DIAGNOSIS DIAGNOSIS_CODE_ Varchar(64) The nomenclature that the diagnosis code is taken from
SOURCE (ICD9, etc.)

DIAGNOSIS CLINICAL_DTM Date The date and time of the diagnosis’s onset or exacerbation

MICROBIOLOGY Include all Microbiology specimens taken within one month before
or after a surgery. (For risk, this might be expanded to one year or
more.)

MICROBIOLOGY MICRO_ID Integer A unique ID for the procedure

MICROBIOLOGY SPECIMEN_CODE Varchar(64) The site that the specimen was collected from

MICROBIOLOGY SPECIMEN_CODE_ Varchar(64) The nomenclature that the specimen code is taken from (SNOMED,
SOURCE LOINC, etc.)

MICROBIOLOGY PATHOGEN_CODE Varchar(64) The code of the pathogen cultured from the collected specimen

MICROBIOLOGY PATHOGEN_CODE_ Varchar(64) The nomenclature that the pathogen code is taken from (SNOMEN,
SOURCE LOINC, etc.)

MICROBIOLOGY COLLECT_DTM Date The date and time the specimen was collected

ENCOUNTER Include all Encounters within one month before or after surgery.

ENCOUNTER ENCOUNTER_ID Integer A unique ID for the visit. This will serve to tie all of the different data
tables together via foreign key relationship.

ENCOUNTER ADMIT_DTM Date The admission date and time for a patient’s visit

ENCOUNTER DISCH_DTM Date The discharge date and time for a patient’s visit

ENCOUNTER ENCOUNTER_TYPE Varchar(64) The type of patient encounter such as inpatient, outpatient, observa­
tion, etc.

Source: Agency for Healthcare Research and Quality (2012).

58 · C H A P T E R 2 : H E A L T H C A R E D A T A

reduce medication errors. The challenge for the foreseeable future is to
balance the need for structured data with the associated costs (time and
money). Further in the future, new challenges will appear as the breadth
of data contained in patient records is likely to increase. Genomic and pro­
teomic data, along with enhanced behavioral and social data, are likely to
be captured (IOM, 2014). These added data will introduce new quality issues
to be resolved.

SUMMARY

Without health care data and information, there would be no need for health
care information systems. Health care data and information are valuable
assets in health care organizations, and they must be managed similar to
other assets. To that end, health care executives need an understanding of
the sources of health care data and information and recognize the importance
of ensuring the quality of health data and information. In this chapter, after
defining health care data and information, we examined patient record and
claims content as sources for health care data. We looked at disease and
procedure indexes and health care statistics as examples of basic uses of
the health care data. The emerging use of data analytics and big data were
introduced and the chapter concluded with a discussion of two frameworks
for examining health care data quality and a discussion of how informa­
tion technology, in general, and the EHR, in particular, can be leveraged to
improve the quality of health care data.

KEY TERMS

Accountable Care Act (ACA)
American Hospital Association (AHA)
Big data
CMS-1500/837P
Completeness
Comprehensive shared care plan

(CSCP)
Concordance
Consent and authorization forms
Consultation
Continuum of care
Correctness

CPT (Current Procedural Terminology)
Currency
Data accessibility
Data accuracy
Data comprehensiveness
Data consistency
Data currency
Data defi nition
Data granularity
Data precision
Data quality characteristics
Data relevancy

L E A R N I N G A C T I V I T I E S · 59

Data timeliness
Databases
Data warehouses
Diagnosis related group (DRG)
Diagnostic and procedural codes
Discharge summary
Disease and procedure indexes
Electronic health records (EHRs)
Electronic medical records

(EMRs)
Episode of care
Healthcare Common Procedure

Coding System (HCPCS)
Health care data
Health care data quality
Health care information
Health care statistics
Health Insurance Portability and

Accountability Act (HIPAA)
Health record
History and physical
ICD-10 (International Classifi cation of

Diseases)
ICD-10-CM (Clinical Modifi cation)
ICD-10-PCS (Procedural Coding

System)

LEARNING ACTIVITIES

Identifi cation screen
Imaging and X-ray reports
Knowledge
Laboratory reports
Legal health record (LHR)
Medical record
Medication record
The National Center of Health

Statistics (NVHS)
National Uniform Billing Committee

(NUBC)
National Uniform Claim Committee

(NUCC)
Office of Inspector General of the

Department of Health and Human
Services (HHS OIG)

Operative report
Pathology report
Physician’s orders
Plausibility
Population health
Problem list
Progress notes
Protected health information (PHI)
Small data
UB-04/CMS-1450/837I

1. Contact a health care facility (hospital, nursing home, physician’s offi ce,
or other organization) to ask permission to view a sample of the health
records they maintain. Answer the following questions for each record:

a. What is the primary reason (or condition) for which the patient
was seen?

b. How long has the patient had this condition?

c. Did the patient have a procedure performed? If so, what

procedure(s) was (were) done?

60 · C H A P T E R 2 : H E A L T H C A R E D A T A

d. Did the patient experience any complications? If so, what were
they?

e. How does the physician’s initial assessment of the patient compare
with the nurse’s initial assessment? Where in the record would you
find this information?

f. To where was the patient discharged?

g. What were the patient’s discharge orders or instructions? Where in
the record should you find this information?

2. Make an appointment to meet with the business manager at a
physician’s offi ce or health care clinic. Discuss the importance of
ICD-10 coding or CPT coding (or both) for that offi ce. Ask to view the
system that the offi ce uses to assign diagnostic and procedure codes.
After the visit, write a brief summary of your findings and impressions.

3. Visit www.oig.hhs.gov. What are the major responsibilities of the
Offi ce of Inspector General as they relate to coded health care data?
What other responsibilities related to health care fraud and abuse
does this offi ce have?

4. Consider a patient (real or imagined) with a chronic health condition.
Identify at least three actual health care providers that this patient
has seen in the past twelve months. Draw a diagram to illustrate the
timeline of the patient’s encounters. Considering these encounters,
how easy is it for each provider to share health care information
regarding this patient with the others? What are the barriers to the
communication and sharing of health care information? How will this
affect the patient’s overall care?

5. Contact a health care facility (hospital, nursing home, physicians’
offi ce, or other facility) to ask permission to view a sample of the
health records it maintains. These records may be in paper or
electronic form. For each record, answer the following questions
about data quality:

a. How would you assess the quality of the data in the patient’s

record?

b. What proportion of the data in the patient’s medical record is
captured electronically? What information is recorded manually?
Do you think the method of capture affects the quality of the
information?

c. How does the data quality compare with what you expected?

R E F E R E N C E S · 61

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hospital-management-administration/10-statistics-your-hospital-should-track.html

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amiajnl-2011-000681

CHAPTER 3

Health Care

Information Systems

LEARNING OBJECTIVES

• To be able to identify the major types of administrative and
clinical information systems used in health care.

• To be able to give a brief explanation of the history and evolution
of health care information systems.

• To be able to discuss the key functions and capabilities of
electronic health record systems and current adoption rates in
hospitals, physician practices, and other settings.

• To be able to describe the use and adoption of personal health
records and patient portals.

• To be able to discuss current issues pertaining to the use of
HCIS systems including interoperability, usability, and health IT
safety.

65

66 · C H A P T E R 3 : H E A L T H C A R E I N F O R M A T I O N   S Y S T E M S

After reading Chapters One and Two, you should have a general understanding
of the national health IT landscape and the types and uses of clinical and
administrative data captured in provider organizations. In this chapter we
build on these fundamental concepts and introduce health care information
systems, a broad category that includes clinical and administrative applica­
tions. We begin by providing a brief history and overview of information
systems used in health care provider organizations. The chapter focuses on the
electronic health record (EHR) and personal health record (PHR), including
patient portals and the major initiatives that have led to the adoption and
use of EHRs in hospitals and physician practices. Included is a discussion
on the state of EHR adoption and use in other health care settings, including
behavioral health, community health, and long-term care. Applications such as
computerized provider order entry and decision support are described in the
context of the EHR. (Note: Other health IT systems and applications needed to
support population health and value-based payment—such as patient engage­
ment tools, telemedicine, and telehealth—are described in Chapter Four.)
Finally, the chapter concludes with a discussion on important key issues in
the use of HCIS including usability, interoperability, and health IT safety.

We begin first with a brief review of key terms.

REVIEW OF KEY TERMS

An information system (IS) is an arrangement of data (information), pro­
cesses, people, and information technology that interact to collect, process,
store, and provide as output the information needed to support the orga­
nization (Whitten & Bentley, 2007). Note that information technology is
a component of every information system. Information technology (IT) is
a contemporary term that describes the combination of computer technol­
ogy (hardware and software) with data and telecommunications technology
(data, image, and voice networks). Often in current management literature
the terms information system (IS) and information technology (IT) are used
interchangeably.

Within the health care sector, health care IS and IT include a broad range
of applications and products and are used by a wide range of constituent groups
such as payers, government, life sciences, and patients, as well as providers and
provider organizations. For our purpose, however, we have chosen to focus on
health care information systems from the provider organization’s perspective.
The provider organization is the hospital, health system, physician practice,
integrated delivery system, nursing home, or rural health clinic. That is, it
is any setting where health-related services are delivered. The organization
(namely, the capacity, decisions about how health IT is applied, and incentives)

M A J O R H E A L T H C A R E I N F O R M A T I O N S Y S T E M S · 67

and the external environment (regulations and public opinion) are important
elements in how systems are used by clinicians and other users (IOM, 2011).
We also examine the use of patient engagement tools such as PHRs and secure
patient portals. Yet our focus is from an organization or provider perspective.

MAJOR HEALTH CARE INFORMATION SYSTEMS

There are two primary categories of health care information systems: admin­
istrative and clinical. A simple way to distinguish them is by purpose and
the type of data they contain. An administrative information system (or
an administrative application) contains primarily administrative or fi nancial
data and is generally used to support the management functions and general
operations of the health care organization. For example, an administrative
information system might contain information used to manage personnel,
finances, materials, supplies, or equipment. It might be a system for human
resource management, materials management, patient accounting or billing,
or staff scheduling. Revenue cycle management is increasingly important to
health care organizations and generally includes the following:

• Charge capture

• Coding and documentation review

• Managed care contracting

• Denial management of claims

• Payment posting

• Accounts receivable follow-up

• Patient collections

• Reporting and benchmarking

By contrast, a clinical information system (or clinical application) contains
clinical or health-related information used by providers in diagnosing and
treating a patient and monitoring that patient’s care. Clinical information
systems may be departmental systems—such as radiology, pharmacy, or
laboratory systems—or clinical decision support, medication administration,
computerized provider order entry, or EHR systems, to name a few. They
may be limited in their scope to a single area of clinical information (for
example, radiology, pharmacy, or laboratory), or they may be comprehensive
and cover virtually all aspects of patient care (as an EHR system does, for
example). Table 3.1 lists common types of clinical and administrative health
care information systems.

68 · C H A P T E R 3 : H E A L T H C A R E I N F O R M A T I O N   S Y S T E M S

Health care organizations, particularly those that have implemented EHR
systems, generally provide patients with access to their information electron­
ically through a patient portal. A patient portal is a secure website through
which patients may communicate with their provider, request refill on pre­
scriptions, schedule appointments, review test results, or pay bills (Emont,
2011). Another term that is frequently used is personal health record (PHR).
Different from an EHR or patient portal, which is managed by the provider
or health care organization, the PHR is managed by the consumer. It may

Table 3.1. Common types of administrative and clinical information systems

Administrative Applications Clinical Applications

Patient administration systems

Admission, discharge, transfer (ADT)
tracks the patient’ s movement of care in an
inpatient setting

Registration may be coupled with ADT
system; includes patient demographic and
insurance information as well as date of
visit(s), provider information

Scheduling aids in the scheduling of
patient visits; includes information on
patients, providers, date and time of visit,
rooms, equipment, other resources

Patient billing or accounts receivable
includes all information needed to submit
claims and monitor submission and
reimbursement status

Utilization management tracks use and
appropriateness of care

Other administrative and fi nancial
systems

Accounts payable monitors money owed to
other organizations for purchased products
and services

General ledger monitors general fi nancial
management and reporting

Ancillary information systems

Laboratory information supports
collection, verifi cation, and reporting of
laboratory tests

Radiology information supports digital
image generation (picture archiving and
communication systems [PACS]), image
analysis, image management

Pharmacy information supports
medication ordering, dispensing, and
inventory control; drug compatibility
checks; allergy screening; medication
administration

Other clinical information systems

Nursing documentation facilitates
nursing documentation from assessment
to evaluation, patient care decision
support (care planning, assessment, fl ow-
sheet charting, patient acuity, patient
education)

Electronic health record (EHR)
facilitates electronic capture and
reporting of patient’ s health history,
problem lists, treatment and outcomes;
allows clinicians to document clinical
findings, progress notes, and other patient
information; provides decision-support
tools and reminders and alerts

H I S T O R Y A N D E V O L U T I O N · 69

Administrative Applications Clinical Applications

Personnel management manages human
resource information for staff, including
salaries, benefi ts, education, and training

Materials management monitors ordering
and inventory of supplies, equipment needs,
and maintenance

Payroll manages information about staff
salaries, payroll deductions, tax withholding,
and pay status

Staff scheduling assists in scheduling and
monitoring staffi ng needs

Staff time and attendance tracks employee
work schedules and attendance

Revenue cycle management monitors the
entire fl ow of revenue generation from charge
capture to patient collection; generally relies
on integration of a host of administrative and
fi nancial applications

Computerized provider order entry
(CPOE) enables clinicians to directly enter
orders electronically and access decision-
support tools and clinical care guidelines
and protocols

Telemedicine and telehealth supports
remote delivery of care; common features
include image capture and transmission,
voice and video conferencing, text
messaging

Rehabilitation service documentation
supports the capturing and reporting of
occupational therapy, physical therapy,
and speech pathology services

Medication administration is typically
used by nurses to document medication
given, dose, and time

include health information and wellness information, such as an individual’s
exercise and diet. The consumer decides who has access to the information
and controls the content of the record. The adoption and use of patient portals
and PHRs are discussed further on in this chapter. For now, we begin with
a brief historical overview of how these various clinical and administrative
systems evolved in health care.

HISTORY AND EVOLUTION

Since the 1960s, the development and use of health care information systems
has changed dramatically with advances in technology and the impact of
environmental influences and payment reform (see Figure 3.1). In the 1960s to
1970s, health care executives invested primarily in administrative and fi nan­
cial information systems that could automate the patient billing process and
facilitate accurate Medicare cost reporting. The administrative applications
that were used were generally found in large hospitals, such as those affi li­
ated with academic medical centers. These larger health care organizations
were often the only ones with the resources and staff available to develop,

70 · C H A P T E R 3 : H E A L T H C A R E I N F O R M A T I O N   S Y S T E M S

Figure 3.1 History and evolution of health care information systems (1960s to today)

implement, and support such systems. It was common for these facilities to
develop their own administrative and financial applications in-house in what
were then known as “data processing” departments. The systems themselves
ran on large mainframe computers, which had to be housed in large, envi­
ronmentally controlled settings. Recognizing that small, community-based
hospitals could not bear the cost of an in-house, mainframe system, leading
vendors began to offer shared systems, so called because they enabled hospi­
tals to share the use of a mainframe with other hospitals. Vendors typically
charged participating hospitals for computer time and storage, for the number
of terminal connects, and for reports.

By the 1970s, departmental systems such as clinical laboratory or
pharmacy systems began to be developed, coinciding with the advent
of minicomputers. Minicomputers were smaller and more powerful than
some of the mainframe computers and available at a cost that could be
justified by revenue-generating departments. Clinical applications includ­
ing departmental systems such as laboratory, pharmacy, and radiology
systems became more commonplace. Most systems were stand-alone and
did not interface well with other clinical and administrative systems in the
organization.

The 1980s brought a significant turning point in the use of health care
information systems primarily because of the development of the micro­
computer, also known as the personal computer (PC). Sweeping changes
in reimbursement practices designed to rein in high costs of health care
also had a significant impact. In 1982, Medicare shifted from a cost-based
reimbursement system to a prospective payment system based on diagnosis
related groups (DRGs). This new payment system had a profound effect on

H I S T O R Y A N D E V O L U T I O N · 71

hospital billing practices. Reimbursement amounts were now dependent on
the accuracy of the patient’s diagnosis and procedures(s) and other informa­
tion contained in the patient’s record. With hospital reimbursement changes
occurring, the advent of the microcomputer could not have been more timely.
The microcomputer was smaller, often as or more powerful, and far more
affordable than a mainframe computer. Additionally, the microcomputer was
not confined to large hospitals. It brought computing capabilities to a host
of smaller organizations including small community hospitals, physician
practices, and other care delivery settings. Sharing information among micro­
computers also became possible with the development of local area networks.
The notion of best of breed systems was also common; individual clinical
departments would select the best application or system for meeting their
unique unit’s needs and attempt to get the “systems to talk to each other”
using interface engines.

Rapid technological advances continued into the 1990s, with the most
profound being the evolution and widespread use of the Internet and elec­
tronic mail (e-mail). The Internet provided health care consumers, patients,
providers, and industries with access to the World Wide Web and new and
innovative opportunities to access care, promote services, and share infor­
mation. Concurrently, the Institute of Medicine (IOM, 1991) published its fi rst
landmark report The Computer-Based Patient Record: An Essential Technology
for Health Care, which called for the widespread adoption of computerized
patient records (CPRs) as the standard by the year 2001. CPRs were the
precursor to what we refer to today as EHR systems. Numerous studies had
revealed the problems with paper-based medical records (Burnum, 1989;
Hershey, McAloon, & Bertram, 1989; IOM, 1991). Records are often illegible,
incomplete, or unavailable when and where they are needed. They lack any
type of active decision-support capability and make data collection and anal­
ysis very cumbersome. This passive role for the medical record was no longer
sufficient. Health care providers needed access to active tools that afforded
them clinical decision-support capabilities and access to the latest relevant
research findings, reminders, alerts, and other knowledge aids. Along with
patients, they needed access to systems that would support the integration
of care across the continuum.

By the start of the new millennium, health care quality and patient safety
emerged as top priorities. In 2000, the IOM published the report To Err Is
Human: Building a Safer Health Care System, which brought national attention
to research estimating that 44,000 to 98,000 patients die each year to medical
errors. Since then, additional reports have indicated that these fi gures are
grossly underestimated and the incidents of medical errors are much higher

72 · C H A P T E R 3 : H E A L T H C A R E I N F O R M A T I O N   S Y S T E M S

(Classen et al., 2011; James, 2013; Makary & Daniel, 2016;). A subsequent
report, Patient Safety: Achieving a New Standard of Care (2004), called for
health care providers to adopt information technology to help prevent and
reduce errors because of illegible prescriptions, drug-to-drug interactions,
and lost medical records, for example.

By 2009, the US government launched an “unprecedented effort to reengi­
neer” the way we capture, store, and use health information (Blumenthal,
2011, p. 2323). This effort was realized in the Health Information Technology
for Economic and Clinical Health (HITECH) Act. Nearly $30 billion was set
aside over a ten-year period to support the adoption and Meaningful Use
of EHRs and other types of health information technology with the goal
of improving health and health care. Rarely, if ever, have we seen public
investments in the advancement of health information technology of this
magnitude (Blumenthal, 2011). Interest also grew in engaging patients more
fully in providing access to their EHR through patient portals or the concept
of a PHR. We have also seen significant advances in telemedicine and tele­
health, cloud computing, and mobile applications that monitor and track a
wide range of health data.

ELECTRONIC HEALTH RECORDS

Features and Functions

Let’s first examine the features and functions of an EHR because it is core
to patient care. An EHR can electronically collect and store patient data,
supply that information to providers on request, permit clinicians to enter
orders directly into a computerized provider order entry (CPOE) system,
and advise health care practitioners by providing decision-support tools such
as reminders, alerts, and access to the latest research findings or appropriate
evidence-based guidelines. CPOE at its most basic level is a computer appli­
cation that accepts provider orders electronically, replacing handwritten or
verbal orders and prescriptions. Most CPOE systems provide physicians and
other providers with decision-support capabilities at the point of ordering.
For example, an order for a laboratory test might trigger an alert to the
provider that the test has already been ordered and the results are pending.
An order for a drug to which the patient is allergic might trigger an alert
warning to the provider of an alternative drug. These decision-support capa­
bilities make the EHR far more robust than a digital version of the paper
medical record.

Figure 3.2 illustrates an EHR alert reminding the clinician that the patient
is allergic to certain medication or that two medications should not be taken

E L E C T R O N I C H E A L T H R E C O R D S · 73

Figure 3.2 Sample drug alert screen

Source: Medical University of South Carolina, Epic. Used with permission.

in combination with each other. Reminders might also show that the patient
is due for a health maintenance test such as a mammography or a cholesterol
test or for an influenza vaccine (Figure 3.2).

Up until the passage of the HITECH Act of 2009, EHR adoption and use
was fairly low. HITECH made available incentive money through the Medi­
care and Medicaid EHR Incentive Programs for eligible professionals and
hospitals to adopt and become “meaningful users” of EHR. As mentioned in
Chapter One, the Meaningful Use criteria were established and rolled out in
three phases. Each phase built on the previous phase in an effort to further
the advancement and use of EHR technology as a strategy to improve the
nation’s health outcome policy priorities:

• Improve health care quality, safety, and effi ciency and reduce health
disparities.

• Engage patients and families in their health care.

• Improve care coordination.

• Improve population and public health.

• Ensure adequate privacy and security of personal health information.

To accomplish these goals and facilitate patient engagement in managing
their health and care, health care organizations provide patients with access
to their records typically through a patient portal. A patient portal is a
secure website through which patients can electronically access their medical
records. Portals often also enable users to complete forms online, schedule
appointments, communicate with providers, request refills on prescriptions,
review test results, or pay bills (Emont, 2011) (see Figure 3.3). Some provid­
ers offer patients the opportunity to schedule e-visits for a limited number
of nonurgent medical conditions such as allergic skin reactions, colds, and
nosebleeds.

74 · C H A P T E R 3 : H E A L T H C A R E I N F O R M A T I O N   S Y S T E M S

Figure 3.3 Sample patient portal

Source: Medical University of South Carolina.

EHR Adoption Rates in US Hospitals

As of 2015, nearly 84 percent of US nonfederal acute care hospitals had
adopted basic EHR systems representing a nine-fold increase from 2008
(Henry, Pylypchuck, Searcy, & Patel, 2016) (see Figure 3.4). Table 3.2 lists
the difference functionality between a basic system and a fully functional
system (DesRoches et al., 2008). A key distinguishing characteristic is fully
functional EHRs provide order entry capabilities (beyond ordering medica­
tions) and decision-support capabilities.

The Veterans Administration (VA) has used an EHR system for years,
enabling any veteran treated at any VA hospital to have electronic access to
his or her EHR. Likewise, the US Department of Defense is under contract
with Cerner to replace its EHR system. EHR adoption among specialty hos­
pitals such as children’s (55 percent) and psychiatric hospitals (15 percent)
is significantly lower than general medicine hospitals because these types
of hospitals were not eligible for HITECH incentive payments. Small, rural,
and critical access hospitals that have historically lagged behind in EHR
adoption are now closing the gap with general acute care hospitals (Henry
et al., 2016).

E L E C T R O N I C H E A L T H R E C O R D S · 75

Figure 3.4 Percent of non-federal acute care hospitals with adoption of at least a
basic EHR with notes system and position of a certifi ed EHR: 2008–2015

Note: Basic EHR adoption requires the EHR system to have a set of EHR functions
defined in Table 3.2. A certified EHR is EHR technology that meets the technological
capability, functionality, and security requirements adopted by the Department of
Health and Human Services. Possession means that the hospital has a legal agreement
with the EHR vendor but is not equivalent to adoption. *Signifi cantly different from
previous year (p<0.05).
Source: ONC (2015a).

EHR Adoption in Office-Based Physician Practices

In addition to EHR use in hospitals, we have also seen signifi cant increases
in the adoption and use of EHR systems among offi ce-based physician prac­
tices. By 2014, 79 percent of primary care physicians had adopted a certifi ed
EHR system and 70 percent of medical and surgical specialties had as well
(Heisey-Grove & Patel, 2015) (see Figure 3.5).

Ninety-eight percent of physicians in community health centers had
adopted an EHR, three-quarters of them using a certified EHR. Not surpris­
ingly, physicians in solo and small group practices were less likely to have
adopted EHR systems (Heisey-Grove & Patel, 2015).

EHR Adoption in Other Settings

Less is known nationally about EHR adoption rates in settings other than
hospitals and physician practices. Among home health and hospice agencies,

76 · C H A P T E R 3 : H E A L T H C A R E I N F O R M A T I O N   S Y S T E M S

Table 3.2 Functions defining the use of EHRs

Fully Functional
Basic System

System

Health Information Data

Patient demographics X
X

Patient problem lists X
X

Electronic lists of medications taken by
X X

patients

Clinical notes X X

Notes including medical history and

X

follow-up

Order Entry Management

Orders for prescriptions X X

Orders for laboratory tests X

Orders for radiology tests X

Prescriptions sent electronically X

Orders sent electronically X

Results Management

Viewing laboratory results X X

Viewing imaging results X X

Electronic images returned X

Clinical Decision Support

Warnings of drug interactions or
X

contraindications provided

Out-of-range test levels highlighted X

Reminders regarding guidelines-based

X

interventions or screening

the latest national estimates based on data from the 2007 National Home

and Hospice Care survey indicate that 44 percent of home health and

hospice agencies have adopted EHR systems (16 percent EHRs only and 28

percent EHRs and mobile technologies such as tablets or hand-held devices

E L E C T R O N I C H E A L T H R E C O R D S · 77

Figure 3.5. Offi ce-based physician practice EHR adoption since 2004

Source: ONC (2015a).

used to gather information at the point of care) (Bercovitz, Park-Lee, &
Jamoom, 2013).

Some states, such as New York, have attempted to assess EHR adoption in
long-term care facilities such as nursing homes. One study found that among
473 nursing homes in New York, 56.3 percent had implemented an EHR
system (Abramson, Edwards, Silver, & Kaushal, 2014). Among the nursing
homes that did not have EHRs, the majority had plans to implement one
within two years. One-fifth had plans to implement one in more than two
years, and 11.7 percent had no EHR implementation plans (Abramson et al.,
2014). The majority of nursing homes indicated the biggest barriers to health
IT investment were the initial cost, a lack of IT staff members, and the lack
of fiscal incentives. National estimates on EHR adoption in long-term care are
nearly nonexistent. Most are qualitative studies examining the experiences
of early adopters (Cherry, Ford, & Peterson, 2011).

Impact of EHR Systems

Numerous studies over the years have demonstrated the value of using EHR
systems and other types of clinical applications within health care organi­
zations. The majority of benefits fall into three broad categories: (1) quality,
outcomes, and safety; (2) efficiency, improved revenues, and cost reduction;
and (3) provider and patient satisfaction. Following is a brief discussion of
these major categories, along with several recent examples and reports illus­
trating the value of EHRs to the health care process. It is important to note,
however, that despite the benefits, some studies have found mixed results or
negative consequences.

78 · C H A P T E R 3 : H E A L T H C A R E I N F O R M A T I O N   S Y S T E M S

• Quality, outcomes, and safety. EHR systems can have a signifi cant
impact on patient quality, outcomes, and safety. Three major effects
on quality are increased adherence to evidence-based care, enhanced
surveillance and monitoring, and decreased medication errors. Banger
and Graber (2015) recently conducted a review of the literature on the
impact of health IT (including EHR systems) on patient quality and
safety and found four major systematic reviews had been conducted
from 2006 through 2014 each using a consistent methodology (Buntin,
Burke, Hoaglin, & Blumenthal, 2011; Chaudhry et al., 2006; Goldzweig,
Towfigh, Maglione, & Shekelle, 2009; Jones, Rudin, Perry, & Shekelle,
2014). Two of the reviews were published before the HITECH Act
and two afterward. Collectively, 59 percent of the studies examined
demonstrated positive effects on quality and safety, 25 percent had
mixed-positive outcomes, 9 percent were neutral, and 8 percent
were negative (Banger & Graber, 2015). Limitations of most of the
earlier studies were based on the fact that they did not include many
commercially available EHR systems. Since then, more than half of EHR
evaluation studies involved commercially available EHR systems (Jones
et al., 2014). Findings from the most recent systematic review conclude
that CPOE effectively decreases medication errors. Hydari, Telang,
and Marella (2014) studied the incidence of adverse patient safety
events reported from 231 Pennsylvania hospitals from 2005 to 2012
in relation to their level of health IT use. After controlling for several
possibly confounding factors, the authors found that hospitals adopting
advanced EHRs (as defined by HIMSS) experienced a 27 percent overall
reduction in reported patient safety events. Using advanced EHRs was
associated with a 30 percent decline in medication errors and a 25
percent decline in procedure-related errors (Hydari et al., 2014).

• Efficiency, improved revenue, and cost reduction. In addition to
improving quality and safety, some studies have shown that the EHR
can improve effi ciency, increase revenues, and lead to cost reductions
(Barlow, Johnson, & Steck, 2004; Grieger, Cohen, & Krusch, 2007). A
fairly recent study by Howley, Chou, Hansen, and Dalrymple (2014)
examined the financial impact of EHRs on ambulatory practices
by tracking the productivity (e.g., the number of patient visits)
and reimbursement of thirty practices for two years after EHR
implementation. They found that practice revenues increased during
EHR implementation despite seeing fewer patients. Another study
looked at seventeen primary care clinics that used EHR systems and
found that the clinics recovered their EHR investments within an
average period of ten months (95 percent CI 6.2–17.4 months), seeing

E L E C T R O N I C H E A L T H R E C O R D S · 79

more patients with an average increase of 27 percent in the active­
patients-to-clinicians full-time equivalent ratio, and an increase in the
clinic net revenue (p<.001) (Jang, Lortie, & Sanche, 2014).

• Provider and patient satisfaction. Provider and patient satisfaction
are common factors to assess when implementing EHR systems.
Results from satisfaction surveys are often mixed. In a 2008 national
survey of physicians, 90 percent of providers using EHRs reported
they were satisfi ed or very satisfi ed with them and a large majority
could point to specifi c quality benefi ts (DesRoches et al., 2008). Those
who had systems in place for two or more years were more likely
to be satisfi ed (Menachemi, Powers, Au, & Brooks, 2010). A study
that examined EHR satisfaction among obstetrics/gynecology (OB/
GYN) physicians found that 63 percent reported being satisfi ed with
their EHR system, and nearly 31 percent were not satisfi ed (Raglan,
Margolis, Paulus, & Schulkin, 2014). Among study participants,
younger OB/GYN physicians were more satisfi ed with their EHR than
older physicians. A study by Rand (in collaboration with the AMA)
found that although many physicians approved of EHRs in concept
(for example, they appreciated the fact that they could remotely access
patient information and provide improved patient care), they expressed
frustrations with usability and work fl ow (Friedberg et al., 2013).
The time-consuming nature of data entry, interference with face-to­
face patient care, ineffi ciency, and the inability to exchange health
information between EHR products led to dissatisfaction. Physicians
across the full range of specialties and practice models also described
other concerns regarding the degradation of clinical documentation.

Among US hospitals, a 2011 national study found that those with EHRs
had significantly higher patient satisfaction scores on items such as “staff
always giving patients information about what to do for the recovery at home,”
“patients rating the hospital as a 9 or 10 overall,” and “patients would defi ­
nitely recommend the hospital to others” than hospitals that did not (Kazley,
Diana, Ford, & Menachemi, 2011, p. 26). Yet the same study found that the
EHR use was not statistically associated with other patient satisfaction mea­
sures (such as having clean rooms) that one would not expect to be affected
by EHR use. A more recent study by Jarvis and colleagues (2013) assessed the
impact of using advanced EHRs (as defined as Stages 6 or 7 on the HIMSS
Analytics EMR Adoption Model [EMRAM] level of health IT adoption) on
hospital quality patient satisfaction using a composite score for measuring
patient experience. (See the following Perspective.) They found that hospitals
with the most advanced EHRs had the greatest gains in improving clinical

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process of care scores, without negatively affecting the patient experience
(Jarvis et al., 2013). Another study found that physicians using EHRS that met
Meaningful Use criteria and had two or more years EHR experience were more
likely to report clinical benefits (King, Patel, Jamoon, & Furukawa, 2014).

Limitations and Need for Further Research

Not all studies have demonstrated positive outcomes from using EHR systems.
For example, the same EHR or clinical information system can be imple­
mented in different organizations and have different results. As example of
variability, two children’s hospitals implemented the same EHR (including
CPOE) in their pediatric intensive care units. One hospital experienced a
significant increase in mortality (Han et al., 2005), and the other did not (Del
Beccaro, Jeffries, Eisenberg, & Harry, 2006). The hospital that experienced an
increase in mortality noted that several implementation factors contributed
to the deterioration in quality; specific order sets for critical care were not
created, changes in workflow were not well executed, and orders for patients
arriving via critical care transportation could not be written before the
patient arrived at the hospital, delaying life-saving treatments. Many factors
can influence the successful use and adoption of EHR systems. These are
discussed more fully in Chapter Six.

PERSONAL HEALTH RECORDS

In addition to EHRs and patient portals, the broader concept of a personal
health record has emerged in recent years. Initially, the PHR was envisioned
as a tool to enable individuals to keep their own health records, and they
could share information electronically with their physicians or other health
care professionals and receive advice, reminders, test results, and alerts
from them. Unlike the EHR and patient portal, which is managed by health
care provider organizations, the PHR is managed by the consumer. It may
include health and wellness information, such as an individual’s exercise
and diet. The consumer decides who has access to the information and con­
trols the content of the record. Personal data the consumer gathers through
use of health apps such as My Fitness Pal or Fitbits may be included.

What is the value of the PHR, and how does it relate to the EHR? Tang
and Lansky (2005) believe the PHR enables individuals to serve as copilots
in their own care. Patients can receive customized content based on their
needs, values, and preferences. PHRs should be lifelong and comprehensive
and should support information exchange and portability. Patients are often
seen by multiple health care providers in different settings and locations over

P E R S O N A L H E A L T H R E C O R D S · 81

PERSPECTIVE
HIMSS Analytics EHR Adoption Levels among US Hospitals

Stage Cumulative Capabilities 2016—Q1

Stage 7 Complete EHR is used; data warehousing and
data analytics is used to improve care; clinical
information can be shared via standardized
electronic transactions across continuum of care.

4.3%

Stage 6 Physician documentation with structured templates
and discrete data is implemented for at least one
inpatient area. Full CCSS. The closed loop medication
administration with bar coding is used. The fi ve rights
of medication administration are verifi ed.

29.1%

Stage 5 A full complement of radiology PACS system provides
medical images to physicians via an intranet.

34.4%

Stage 4 Computerized provider order entry (CPOE) used to
create orders; CDSS is used with clinical protocols.

10.0%

Stage 3 Nursing/clinical documentation has been implemented
including electronic medication administration
record (MAR); clinical decision support (CDS)
capabilities allow for error checking with order
entry. Medical image access from picture archive and
communication systems (PACS) is available within
organization.

15.3%

Stage 2 Major clinical systems feed into clinical data repository
(CDR) that enables viewing of orders and results.
CDR contains a controlled medical vocabulary, and
clinical decision support system (CDSS) capabilities.
Hospital may have health information exchange
(HIE) capabilities and can share CDR information
with patient care stakeholders.

2.5%

Stage 1 All three major ancillary clinical systems (laboratory,
pharmacy, radiology) are installed.

1.8%

Stage 0 All three key ancillary department systems (laboratory,
pharmacy, radiology) are not installed.

2.6%

N=5,456

Source: Adapted from HIMSS Analytics EMR Adoption Model (EMRAM).
© HIMSS Analytics 2016. Retrieved from http://www.himssanalytics.org/pro­
vider-solutions. Used with permission.

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the course of a lifetime. In our fragmented health care system, this means
patients are often left to consolidate information from the various participants
in their care. A PHR that brings together important health information across
an individual’s lifetime and that is safe, secure, portable, and easily acces­
sible can reduce costs by avoiding unnecessary duplicate tests and improv­
ing health care communications. The concept of patient portals and PHRs
are also inherent in the CMS Meaningful Use program. Stage 3 Meaningful
Use recommendations (originally scheduled for implementation in 2017 but
now under policy reconsideration) state that patients should be able to (1)
communicate electronically using secure messaging, (2) access patient edu­
cation materials on the Internet, (3) generate health data into their providers’
EHRs, and (4) view, download, and transmit their provider-managed EHRs.
Taken together, Ford, Hesse, and Huerta (2016) argue that these requirements
outline the basic functionalities of a consumer-managed PHR.

Ford and his colleagues (2016) examined US consumers PHR use over
time, the factors that influence use, and projected the diffusion of PHR under
three scenarios. Not surprisingly, they found that consumers were increas­
ingly using electronic means for storing health data and communicating with
their clinical providers. An estimated 5 percent of consumers used PHRs in
2008, and by 2013, this number had reached 17 percent (Ford et al., 2016),
still relatively low. Using various prediction models, they estimate that PHR
use will increase significantly within the next decade.

PHRs and personal health applications have the potential to positively
affect medication adherence and quality of life for patients with chronic dis­
eases. For example, a recent controlled study examined the impact of a text-
based message reminder system on medication adherence among adolescents
with asthma (Johnson et al., 2016). Compared to adolescents in the control
group, they found improvements in self-reported medication adherence (p =
.011), quality of life (p = .037), and self-effi cacy (p = .016). System use varied
considerably, however, with lower use among African American adolescents
(Johnson et al., 2016).

Consumers are also increasingly capturing health, wellness, and clin­
ical data about themselves using a wide range of mobile technologies and
applications—everything from wrist-worn devices that track steps and sleep
patterns to web-based food diaries, networked weight scales, and blood pres­
sure machines (Rosenbloom, 2016). They also use social media networks to
connect with others who share a similar health condition. Such approaches
are referred to as person-generated health data (PGHD) technologies given
that consumers may use these technologies independent of situations in
which they are patients per se. According to Rosenbloom (2016) the fi eld
of PGHD and related technologies is in its infancy, particularly in studying

K E Y I S S U E S A N D C H A L L E N G E S · 83

the real value these technologies add to health care delivery. Shaw and his
colleagues (2016) found, for example, that individuals with chronic illnesses
(who may have the most to benefi t from using mobile health devices) may be
less likely to adopt and use these devices compared to healthy individuals.
As health care organizations and providers move to managing population
health and cohorts of patients under value-based payment models, the use
of such technologies with certain populations of patients may be incredibly
useful. Chapter Four discusses further the health IT tools needed to support
population health management.

KEY ISSUES AND CHALLENGES

Despite the proliferation in the adoption and use of EHR systems, health care
providers and organizations still face critical issues and challenges related
to interoperability, usability, and health IT safety. Following is a brief dis­
cussion of each.

Interoperability

In simple terms, interoperability is “the ability of a system to exchange elec­
tronic health information with and use electronic health information from
other systems without special effort on the part of the [user]” (Institute
for Electrical and Electronics Engineering [IEEE], n.d.). The ONC’s report
Connecting Health and Care for the Nation: A Shared Nationwide Interoper­
ability Roadmap (ONC, 2015a) describes the importance of interoperability
in a creating a “learning health system” in which “health information fl ows
seamlessly and is available to the right people, at the right place, at the
right time.” The overarching vision of a learning health system is to put
patients at the center of their care—“where providers can easily access and
use secure health information from different sources; where an individual’s
health information is not limited to what is stored in EHRs, but includes
information from other sources (including technologies that individuals use)
and portrays a longitudinal picture of their health, not just episodes of care;
where diagnostic tests are only repeated when necessary, because the infor­
mation is readily available; and where public health agencies and researchers
can rapidly learn, develop and deliver cutting edge treatments” (ONC, 2015a,
p. vi) (see Figure 3.6).

Today, providers are challenged to knit together multiple EHRs, fi nan­
cial systems, and analytic solutions in an effort to effectively manage
population health and facilitate care coordination. As health care providers

Source: ONC (2015a).

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Figure 3.6 The ONC’s roadmap to interoperability

and organizations coalesce to manage performance and utilization risk
in their communities, they need high degrees of interoperability among
these systems (Glaser, 2015). The systems must also fit well into the
clinical workflow and patient care process while ensuring patient safety
and quality. Additionally, interoperability will enable data generated by
personal fitness and wearable devices to be included in the patient’s EHR
(Glaser, 2015).

True interoperability has yet to be realized. Several factors make interop­
erability among health care information systems complicated. EHR systems
are often developed using different platforms with inconsistent use of stan­
dards, no universal patient identifier exists, and pulling together from a wide
range of sources is complicated (Glaser, 2015). Moreover, historically there
has not been a great deal of incentive for providers to share information, nor
for health IT vendors to bridge together a number of different systems, giving
rise to the concept of information blocking. According to the ONC, informa­
tion blocking occurs “when persons or entities knowingly and unreasonably
interfere with exchange or use of electronic health information” (ONC, 2015b).
The concept of information blocking implies that the entity intentionally and
knowingly interferes with sharing the data and is objectively unreasonable
in light of public policy. The ONC has developed comprehensive strategies for
identifying, deterring, and remedying information blocking and coordinat­
ing with other federal agencies that can investigate and take action against
certain types of information blocking.

The ONC Roadmap to Interoperability postulates that work is needed in
three critical areas: (1) requiring standards, (2) motivating the use of those
standards through appropriate incentives, and (3) creating a trusted environ­
ment for collecting, sharing, and using electronic health information. Broad

K E Y I S S U E S A N D C H A L L E N G E S · 85

stakeholder involvement is critical to achieving interoperability. Stakeholders
include those who receive or support care, those who deliver care, those who
pay for care, and people and organizations that support health IT capabilities,
oversight of health care organizations, and those who develop and maintain
standards (ONC, 2015b). (See the following Perspective.) In addition to the
ONC, which resides in the Department of Health and Human Services, CMS
and state governments also play key roles in advancing interoperability.
Statewide health information exchanges can be found in Massachusetts, New
York, and Delaware (Glaser, 2015). Interoperability efforts and standards
development are discussed more fully in Chapter Ten.

Partnerships are also occurring within the private sector to advance
interoperability among systems by creating standards and promoting the
sharing of data. CommonWell Health Alliance has created and implemented
patient identification and record-locating service capabilities, Carequality
is developing an interoperability and governance framework, and the Argo­
naut Project is testing the next generation of interoperability standards.
Glaser (2015) argues that we must focus on several important goals in
making interoperability in health care a reality by doing the following:

• Advancing standards development and pursuing new technical

approaches to effecting standards-based interoperability

• Strengthening sanctions, perhaps through the certifi cation process, to
minimize business practices that thwart interoperability

• Increasing transparency of vendor and provider progress in achieving
interoperability

• Developing a trust framework that balances the need for effi cient
exchange with the privacy rights of patients

• Promoting collaborative multi-stakeholder efforts, such as
CommonWell Health Alliance, Carequality, and eHealth Initiative

• Encouraging provider-led activities within communities to broaden
the range of interconnections and include stakeholders such as safety
net providers

• Creating a governance mechanism that ensures an effective
interchange across a wide range of health information exchanges

• Making reimbursement changes that emphasize care coordination and
population health management, all of which must continue to evolve
and be implemented

Unfortunately, there is no silver bullet or easy road to achieving true
interoperability. However, with collaboration among stakeholders, appropriate

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Connecting Health and Care for the Nation: A Shared Nationwide Interoper­
ability Roadmap (ONC, 2015b) was released by the Office of the National
Coordinator for Health Information Technology in 2015. This document
was published as a companion to the Connecting Health and Care for the
Nation: A 10-Year Vision to Achieve an Interoperable Health IT Infrastruc­
ture. The following facts are taken from the Roadmap and its companion
infographic, Shared Nationwide Interoperability Roadmap: The Journey to
Better Health and Care. This outline lists progress toward interoperability
since 2009, the current state of health care supporting the need for interop­
erability, and the future goals and selected payer and outcome milestones
for achieving the ultimate in interoperability, “learning health systems
in which health information flows seamlessly and is available to the right
people, at the right place, at the right time” (ONC, 2015a).

Selected Historical Interoperability Achievements

2009 16% of hospitals and 21% of providers adopted basic EHRs.
2011 27% of hospitals and 34% of providers adopted EHRs.
2013 94% of nonfederal acute care hospitals use a certifi ed EHR.

78% of offi ce-based physicians use an EHR.
62% of hospitals electronically exchanged health information
with providers outside their system.

2014 80% of hospitals can electronically query other organizations
for health information.
14% of office-based providers electronically share patient
information with other providers.

Current State of Health Care

• One in three consumers must provide his or her own health informa­
tion when seeking care for a medical problem.

• A typical Medicare benefi ciary sees seven providers annually.

• A typical primary care physician has to coordinate care with 229
other physicians working in 117 practices.

• Eighty to ninety percent of health determinants are not related to
health care.

K E Y I S S U E S A N D C H A L L E N G E S · 87

PERSPECTIVE
The ONC Roadmap to Interoperability

• One in eight Americans tracks a health metric using technology.

• It takes seventeen years for evidence to go from research to practice.

Barriers to Interoperability

• States have different laws and regulations making it diffi cult to share
health information across state lines.

• Health information is not suffi ciently standardized.

• Payment incentives are not aligned to support interoperability.

• Privacy laws differ and are misinterpreted.

• There is a lack of trust among health care providers and consumers.

2015–2017 Goal and Milestones
Goal: Send, receive, find, and use priority data domains to improve health
care quality and outcomes

Roadmap Milestones for a Supportive Payment and Regulatory
Environment and Outcomes

CMS will aim to administer 30 percent of all Medicare payments to
providers through alternative payment models that reward quality
and value and encourage interoperability by the end of 2016.

A majority of individuals are able to securely access their elec­
tronic health information and direct it to the destination of their
choice.

Providers evolve care processes and information reconciliation to
ensure essential health information is sent, found, or received to
support safe transitions in care.

ONC, federal partners, and stakeholders develop a set of measures
assessing interoperable exchanges and the impact of interoperability
on key processes that enable improved health and health care.

2018–2020 Goal and Milestones
Goal: Expand interoperable health IT and users to improve health and
lower cost

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Roadmap Milestones for a Supportive Payment and Regulatory
Environment and Outcomes

CMS will administer 50 percent of all Medicare payments to pro­
viders through alternative payment models that reward quality and
value by the end of 2018.

Individuals regularly access and contribute to their longitudinal
electronic health information via health IT, send and receive that
information through a variety of emerging technologies, and use that
information to manage their health and participate in shared deci­
sion making with their care, support, and service teams.

Providers routinely and proactively seek outside information about
individuals and can use it to coordinate care.

Public and private stakeholders report on progress toward interop­
erable exchange, including identifying barriers to interoperability,
lessons learned, and impacts of interoperability on health outcomes
and costs.

incentives, and keeping the patient at the center of our work and efforts,
secure and efficient interoperability is certainly within reach.

Usability

In addition to interoperability concerns, clinicians often express frustration
with the usability of EHR systems and other clinical information systems. In
fact, 55 percent of physicians reported that it was difficult or very diffi cult
to use. Common frustrations include confusing displays, iconography that
lacks consistency and intuitive meaning, and the feeling that systems do not
support clinicians’ cognitive workflow or inhibit them from easily drawing
insights or conclusions from the data. Similarly, physicians who participated
in a Rand study (Friedberg et al., 2013) felt that EHR data entry was time-con­
suming, interfered with face-to-face patient care, and was overall ineffi cient.
They also reported that inability to exchange health information and the deg­
radation of clinical documentation were of concern. Others argue that poor
usability of EHR systems not only contributes to clinician frustration but also
can lead to errors and patient safety concerns (Meeks, Smith, Taylor, Sittig,

K E Y I S S U E S A N D C H A L L E N G E S · 89

2020–2024 Goal and Milestones
Goal: Achieve nationwide interoperability to enable a learning health system

Roadmap Milestones for a Supportive Payment and Regulatory
Environment and Outcomes

The federal government will use value-based payment models as the
dominant mode of payment for providers.

Individuals are able to seamlessly integrate and compile longitudi­
nal electronic health information across online tools, mobile plat­
forms, and devices to participate in shared decision making with
their care, support, and service teams.

Providers routinely use relevant info from a variety of sources,
including environmental, occupational, genetic, human service, and
cutting-edge research evidence, to tailor care to the individual.

Public and private stakeholders report on progress on key metrics
identifi ed to achieve a learning health system.

Source: ONC (2015a).

Scott, & Singh, 2014; Sittig & Singh, 2011). In essence, usability refers to “the
effectiveness, efficiency, and satisfaction with which the intended users can
achieve their tasks in the intended context of produce use” (Bevan, 2001).
Smartphones are typically viewed as having high usability, because they
require little training and are intuitive to use. In fact, we often see young
children navigating them before they can even talk!

Given the importance of system usability, a task force was formed by the
American Medical Informatics Association (Middleton et al., 2013) to study
the issue. They identified key recommendations on critical usability issues,
particularly those that may adversely affect patient safety and the quality of
care. The recommendations fall into four categories: (1) usability and human
factors research, (2) policy recommendations, (3) industry recommendations,
and (4) clinical end user recommendations. (See the Perspective.)

As one can discern from AMIA’s task force recommendations, usability is
a multifaceted issue and one that requires thoughtful research, standardiza­
tion and interoperability, a common user interface style guide, and systems
for identifying best practices and monitoring use as well as adverse events
that may affect patient safety.

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PERSPECTIVE
AMIA EHR Usability Recommendations

1. Usability and human factors research agenda in health IT

a. Prioritize standardized use cases.

b. Develop a core set of measures for adverse events related to
health IT use.

c. Research and promote best practices for safe implementation of
EHR.

2. Policy recommendations

d. Standardization and interoperability across EHR systems should
take account of usability concerns.

e. Establish an adverse event reporting system for health IT and
voluntary health IT event reporting.

f. Develop and disseminate an educational campaign on the safe
and effective use of EHR.

3. Industry recommendations

g. Develop a common user interface style guide for select EHR
functionalities.

h. Perform formal usability assessments on patient-safety sensitive
EHR functionalities.

4. Clinical end user recommendations

i. Adopt best practices for EHR implementation and ongoing
management.

j. Monitor how IT systems are used and report IT-related adverse
events.

Source: Middleton et al. (2013). Reproduced with permission of Oxford Univer­
sity Press.

K E Y I S S U E S A N D C H A L L E N G E S · 91

Health IT Safety

In 2011, the Institute of Medicine published a report titled Health IT and
Patient Safety: Building Safer Systems for Better Care in which they outlined
a number of recommendations to ensure health IT systems are safe. In brief,
they suggest that safety is a shared responsibility between vendors and health
care organizations and requires the following:

• Building systems using user-centered design principles with adequate
testing and simulation

• Embedding safety considerations throughout the implementation
process

• Developing and publishing best practices

• Having accreditation agencies (such as the Joint Commission) assume
a signifi cant role in testing as part of their accreditation criteria

• Focusing on shared learning and transparency

• Creating a nonpunitive environment for reporting (IOM, 2011)

Since then, the topic of health IT safety has grown in importance as more
EHR systems have been deployed. Health IT patient safety concerns include
adverse events that reached the patient, near misses that did not reach the
patient, or unsafe conditions that increased the likelihood of a safety event
(Meeks et al., 2014). Such events are often difficult to define and detect.
Consequently, Singh and Sittig (2016) have developed a health IT safety
measurement framework that takes into account eight technological and
nontechnological dimensions or sociotechnical dimensions (see Table 3.3).

The Health IT Safety Framework provides a conceptual framework for
defining and measuring health IT–related patient safety issues. The frame­
work is also built on continuous quality improvement methods that require
stakeholders to ask themselves, How are we doing? Can we do better? How
can we do better (Singh & Sittig, 2016)? In fact, Singh and Sittig (2016) argue
that it is essential that clinicians and leaders make health IT patient safety
an organizational priority by ensuring that the governance structure facil­
itates measuring and monitoring and creating an environment that is con­
ducive to detecting, fixing, and learning from system vulnerabilities. Meeks
and colleagues (2014) used a variation of the Health IT Safety Framework
in analyzing one hundred different EHR-related safety concerns reported to
and investigated by the VA’s Informatics Patient Safety Office, which is a
voluntary reporting system. The major categories of errors were because of
(1) unmet display needs (mismatch between information needs and content

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Table 3.3 Sociotechnical dimensions

Dimension Description

Hardware and software Computing infrastructure used to support and operate
clinical applications and devices

Clinical content The text, numeric data, and images that constitute the
“language” of clinical applications, including clinical
decision support

Human-computer All aspects of technology that users can see, touch, or
interface hear as they interact with it

People Everyone who is involved with patient care and/or
interacts in some way with health care delivery
(including technology). This would include patients,
clinicians and other health care personnel, IT
developers and other IT personnel, informaticians

Workfl ow and Processes to ensure that patient care is carried out
communication effectively, effi ciently, and safely

Internal organizational Policies, procedures, the physical work environment,
features and the organizational culture that govern how the

system is configured, who uses it, and where and
how it is used

External rules and Federal or state rules (e.g., CMS’s Physician Quality
regulations Reporting Initiative, HIPAA, and Meaningful Use

program) and billing requirements that facilitate or
constrain the other dimensions

Measurement and Evaluating both intended and unintended
monitoring consequences through a variety of prospective and

retrospective, quantitative, and qualitative methods

Source: Reproduced from Measuring and Improving Patient Safety through Health
Information Technology: The Health IT Safety Framework, Singh and Sittig, 25: p.228,
2016. With permission from BMJ Publishing Group Ltd.

display; (2) software modifications (concerns about upgrades, modifi ca­
tions, or configurations); (3) system-to-system interfacing (concerns about
failure of interfacing between systems); and (4) hidden dependencies on
distributed systems (one component of the EHR is unexpectedly or unknow­
ingly affected by the state or condition of another component) (Meeks et al.,
2014). They concluded that because EHR-related safety concerns have soci­
otechnical origins and are multifaceted, health care organizations should
build a robust infrastructure to monitor and learn from them.

K E Y T E R M S · 93

Numerous factors can affect the safety and effective use of health care
information systems—everything from poor usability to software glitches
to unexpected downtime or cyber attacks. Health care executives should be
aware of these issues and vulnerabilities and ensure their organizations have
in place mechanisms to prevent, detect, monitor, and address adverse events
that may affect patient safety and quality of care.

SUMMARY

This chapter provided an overview of health care information systems
including administrative and clinical information systems. We gave a brief
history of the evolution of the use of information systems in health care.
Special attention was given to the adoption, use, and features of EHR
systems, patient portals, and PHR systems. We also summarized recent
literature on the value of EHR systems, which may be categorized into
three main areas: (1) quality, outcomes, and safety; (2) effi ciency, improved
revenues, and cost reduction; and (3) provider and patient satisfaction.
Limitations to research findings were noted along with the need for future
research. Key issues related to the use of health care information systems
were discussed including interoperability, usability, and health IT safety. The
chapter concludes with a discussion of a health IT safety framework that may
be useful to health care leaders in preventing, detecting, and monitoring
health IT–related patient safety issues.

KEY TERMS
Administrative information system Information blocking
Best of breed Interoperability
Clinical information systems Learning health systems
Computerized provider order entry Mainframe computers

(CPOE) Microcomputer
Electronic health record (EHR) Minicomputers
Health IT safety Patient portals
HIMSS Analytics EMR Adoption Personal health record (PHR)

Model (EMRAM) Usability

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LEARNING ACTIVITIES

1. Search the literature and find at least one article describing the
adoption and use of one administrative or clinical information
system. Summarize the article for your classmates and discuss it with
them. What are the key points of the article? What learned lessons
does it describe?

2. Visit a health care organization that uses one of the clinical
applications described in this chapter. Find out how the application’s
value is measured or assessed. What do the providers think of it?
Health care executives? Nurses? Support staff members? What impact
has it had on quality? Patient safety? Effi ciency? Satisfaction?

3. Conduct a literature review on interoperability in health care. What
progress has been made to date? What challenges lie ahead? How do
you think we may overcome these challenges?

4. Interview a CIO or health IT professional in your community
regarding interoperability and health information exchange. To
what extent is the organization exchanging health information
electronically with others? What are the barriers and facilitators to
the exchange?

5. Visit a health care organization (outside of a hospital or physician
practice) to examine the types and use of information systems
used. What are the major management issues related to the use of
information systems in this setting? Discuss strategies for addressing
these issues.

6. Interview a CMIO or other health care executive to investigate how
health IT safety events are detected, monitored, and addressed in his
or her organization. How does the organization’s approach take into
consideration the factors described in the Singh and Sittig’s Health IT
Safety Framework?

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CHAPTER 4

Information Systems

to Support Population

Health Management

LEARNING OBJECTIVES

• To be able to understand the data and information needs of
health systems in managing population health effectively under
value-based payment models.

• To be able to discuss key health IT tools and strategies for
population health management including EHRs, registries,
risk stratifi cation, patient engagement, and outreach, care
coordination and management, analytics, health information
exchange, and telemedicine and telehealth.

• To be able to discuss the application and use of data analytics to
monitor, predict, and improve performance.

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The enactment of the Affordable Care Act (ACA) brought about sweeping
legislation intended to reduce the numbers of uninsured and make health
care accessible to all Americans. It also ushered in an era in which chang­
ing reimbursement and care delivery models are driving providers from the
current fragmented system focused on volume-based services to an outcomes
orientation. As a result, the health care system now taking shape is one in
which value-based payment models financially reward patient-centered, coor­
dinated, accountable care.

Against this backdrop, providers’ increasing use of evidence-based med­
icine and growing capabilities in managing volumes of clinical evidence
through sophisticated health IT systems will mean that treatments can be
tailored for the individual and interventions can be made earlier to keep
patients well. Furthermore, patient engagement is fast becoming a critical
component in the care process, particularly in the area of population health
management (PHM).

Health care providers’ interest in improving population health appears to
be increasing because of the sudden ubiquity of the phrase, because many
are participating in accountable care organizations (ACOs), and because
even hospitals not participating in an ACO increasingly have incentives to
reduce their number of potentially unavoidable admissions, readmissions,
and emergency department visits (Casalino, Erb, Joshi, & Shortell, 2015).

In this chapter we’ll not only seek a common understanding of PHM but
also explore how the advent of shared accountability fi nancial arrangements
between providers and purchasers of care has created significant focus on
PHM. We’ll also review the core processes associated with accountable care
and examine the strategic IT investments and data management capabilities
required to support population health management and enable a successful
transition from volume-based to value-based care.

PHM: KEY TO SUCCESS

Although the ACO model is still new and evolving, approximately 750 ACOs
are in operation today, covering some 23.5 million lives under Medicare,
Medicaid, and private insurers. Although not all ACOs have demonstrated
success in delivering better health outcomes at a lower cost, many have
achieved promising results (Houston & McGinnis, 2016). As such, signifi cant
ACO growth is expected. In fact, it is predicted that upward of 105 million
people will be covered by an ACO by 2020 (Leavitt Partners, 2015).

Similarly, although the industry’s move to value-based payment is also in
its early stages, value-based contracts are expected to substantially increase
throughout the next decade. CMS has a stated goal that 50 percent of Medicare

P H M : K E Y T O S U C C E S S · 101

payments will be tied to alternative payment models by the end of 2018 (US
DHHS, 2015). In fact, the projected impact of MACRA, which we discussed
in Chapter One, on the adoption of value-based payment models is expected
to rival the impact of Meaningful Use on adoption of EHRs. In addition, the
substantial payment reform activity at the federal level is paralleled by private
insurers’ efforts to support value-based payment and new models of care. For
example, Aetna expects that 75 percent of its contracts will be value-based
by 2020 (Jaspen, 2015).

These trends will accelerate the demand for services and technology
that enable health systems and other organizations (health plans, Medicaid,
community-based organizations, employers, and so forth) to jointly manage
the health and care of populations—either as an ACO or in an ACO-like
fashion. Although diverse, these organizations will all have a common need
to improve operational efficiency, drive better patient outcomes while reduc­
ing the overall cost of care, and effectively engage consumers in managing
their health and care.

Although the new reimbursement system is still taking shape, it’s clear
that population health management will become a required core competency
for provider organizations in a post fee-for-service payment environment
(Institute for Health Technology Transformation, 2012).

Understanding Population Health Management

Population health as a concept first appeared in 2003 when David Kindig and
Greg Stoddart (2003) defined it as “the health outcomes of a group of individ­
uals, including the distribution of such outcomes within the group” (p. 380).

It is important to note that medical care is only one of many factors
that affect those outcomes. Other factors include public health interventions;
aspects of the social environment (income, education, employment, social
support, and culture); the physical environment (urban design, clean air and
water); genetics; and individual behavior (Institute for Health Technology
Transformation, 2012). “Improving the health of populations” was later iden­
tified as one element in the Institute for Healthcare Improvement’s triple aim
for improving the US health care system, along with improving the individual
experience of care and reducing the per capita cost of care (Berwick, Nolan
& Whittington, 2008, p. 759).

Today, population health management comprises the proactive application
of strategies and interventions to defined groups of individuals (e.g., diabetics,
cancer patients with tumor regrowth, the elderly with multiple comorbidities)
to improve the health of individuals within the group at the lowest cost. PHM
interventions are designed to maintain and improve people’s health across

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the full continuum of care—from low-risk, healthy individuals to high-risk
individuals with one or more chronic conditions (Felt-Lisk & Higgins, 2011).
PHM also seeks to minimize the need for expensive encounters with the
health care system, such as emergency department visits, hospitalizations,
imaging tests, and procedures. This not only lowers costs but also redefi nes
health care as an activity that encompasses far more than sick care, because
it systematically addresses the preventative and chronic care needs of every
patient—not just high-risk patients who generate the majority of health care
costs (Institute for Health Technology Transformation, 2012).

Although population health can also mean the health of the entire popu­
lation in a geographic area, the population health efforts most health systems
and ACOs are undertaking are aimed at providing better preventive and
medical care for the “population” of patients “attributed” to their organiza­
tions by Medicare, Medicaid, or private health insurers (Casalino et al., 2015).

New Care Delivery and Payment Models: The Link to PHM

As we know, historically, there has been a lack of accountability for the total
care of patients, the outcomes of their treatment, and the  effi ciency with
which health resources are used. The fact that health care services are paid
primarily on a fee-for-service basis has contributed to the fragmentation and
lack of accountability. Fee-for-service emphasizes the provision of health ser­
vices by individual hospitals or providers rather than care that is coordinated
across providers to address the patient’s needs. Providers are rewarded for
volume and for conducting procedures that are often more complex, when
simpler, lower-cost, better methods may be more appropriate (Guterman  &
Drake, 2010).

Value-based care is emerging as a solution to address rising health care
costs, clinical inefficiency and duplication of services, and to make it easier
for people to get the appropriate care they need. As the federal government
continues to test and implement several new payment models designed to
achieve optimal health outcomes at a sustainable cost, commercial insurers
are also partnering with health care providers in various arrangements that
similarly seek to reward value rather than volume of services.

As discussed in Chapter One, two popular models of delivery system
reform are the patient-centered medical home (PCMH) and the ACO. The
PCMH emphasizes the central role of primary care and care coordination,
with the vision that every person should have the opportunity to easily
access high-quality primary care in a place that is familiar and knowledge­
able about his or her health care needs and choices. The ACO emphasizes
the urgent need to think beyond patients to populations, providing a vision

P H M : K E Y T O S U C C E S S · 103

for increased accountability for performance and spending across the health
care system (Patient-Centered Primary Care Collaborative, 2011). Both models
rely on health care organizations and physicians providing coordinated and
integrated care in an evidence-based, cost-effective way. This, of course, has
significant implications for an organization’s ability to manage information
effectively.

In conjunction with new models of care are new or modifi ed forms
of payment for health care services, which are being piloted in various
communities around the nation. These include  bundled payments,  pay for
performance, shared savings programs, capitation or global payment, and
episode-of-care payments.

Bundled payments may take different forms such  as making a single
payment for hospital and physician services instead  of separate payments,
bundling payments for inpatient and post-acute care, or paying based on diag­
nosis instead of treatment. Bundled payments are often applied to surgical
procedures such as hip replacements.  Pay-for-performance (P4P) programs
reward hospitals, physician practices, and other providers with fi nancial and
nonfinancial incentives based on performance on select measures. These
performance measures can cover various aspects of health care delivery:
clinical quality and safety, efficiency, patient experience, and health infor­
mation technology adoption. Most P4P programs, however, are still a bonus
to a fee-for-service model (Miller, 2011). An integral part of the ACA, shared
savings programs are intended to reward providers by paying them a bonus
that is explicitly connected to the amount by which they reduce the total cost
of care compared to expected levels. Capitation or global payment places full
risk with the provider organization; the provider is responsible for the costs of
all care that a patient receives. An episode-of-care payment system would pay
the provider organization a single payment for all of the services associated
with a hospitalization or other episode of acute care, such as a heart attack,
including inpatient and post-acute care (Miller, 2011).

The revised payments associated with these programs signal the federal
government’s most all-encompassing effort thus far to distribute risk and
hold providers financially accountable for the quality of care they deliver.
Although an in-depth discussion of these and other proposed payment reform
systems is beyond the scope of this book, the following resources can provide
a wealth of detailed information on health care payment reform initiatives:

• Centers for Medicaid & Medicare Services (www.CMS.gov)

• Healthcare Financial Management Association (www.hfma.org)

• American College of Healthcare Executives (www.ache.org)

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Progress to Date: PCMHs

Growing support for the PCMH has arisen across the vast majority of the US
health care delivery system to include commercial insurance plans, multiple
employers, state Medicaid programs, numerous federal agencies, the Depart­
ment of Defense, hundreds of safety net clinics, and thousands of small
and large clinical practices nationwide (Grundy, Hacker, Langner, Nielsen,
& Zema, 2012). Private and public payer initiatives together have grown
from eighteen states in 2009 to forty-four states in 2013, and they now cover
almost twenty-one million patients. These heterogeneous initiatives overall
are becoming larger, paying higher fees, and engaging in more risk sharing
with practices (NCQA, 2015).

Because the patient-centered medical home is foundational to ACOs—
with ACOs often described as the “medical neighborhood”—the PCMH is
likely to gain even greater prominence as ACOs continue to develop in the
marketplace (Grundy et al., 2012). Moreover, a growing body of scientifi c
evidence shows that PCMHs are saving money by reducing hospital and
emergency department visits, mitigating health disparities, and improving
patient outcomes. Examples of specific outcomes achieved by various PCMHs
include the following:

• Lower Medicare spending

• More effective care management and optimized use of health care
services

• Improved care management and preventative screenings for
cardiovascular and diabetes patients

• Reduced socioeconomic disparities in cancer screening (NCQA, 2015)

Additionally, more than nine thousand primary care practices and for­
ty-three thousand clinicians (doctors and nurse practitioners) across the
country have earned the PCMH designation from the National Committee for
Quality Assurance (NCQA), the nation’s largest credentialing organization.
The designation is earned by demonstrating achievement of goals related to
accessible, coordinated, and patient-centered care (Olivero, 2015).

Progress to Date: ACOs

In the value-based care world, ACOs are expected to play a leadership role in
improving population health—whether participating in contracts with Medi­
care, Medicaid, or managed care organizations (MCOs) or health plans. These
arrangements are often complex and may differ widely, including elements

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such as governance requirements, payment structures, quality metrics,
reporting requirements, and data sharing (Houston & McGinnis, 2016).

Several different ACO models, including the Pioneer ACO program and the
Medicare Shared Savings Program (MSSP), are testing and evaluating various
risk-sharing agreements. In December 2011, CMS signed agreements with
thirty-two organizations to participate in the Pioneer ACO model, designed to
show how particular ACO payment arrangements can best improve care and
generate savings for Medicare. As of May 1, 2016, there are nine Pioneer ACOs
participating in the model for a fifth and final performance year (CY2016). The
MSSP is a key component of the Medicare delivery system reform initiatives
included in the Affordable Care Act and is designed to facilitate coordination
and cooperation among providers to improve the quality of care for Medicare
fee-for-service (FFS) beneficiaries and reduce unnecessary costs. Eligible
providers, hospitals, and suppliers may participate in the MSSP by creating
or participating in an ACO.

Although there has been considerable debate among policymakers as to
the success of the ACO model, some of these ACOs are already reporting pos­
itive results for improving patient outcomes and controlling costs, as shown
in Table 4.1 (Houston & McGinnis, 2016).

ACO Challenges

Now with years of observation and learnings to draw from, several key chal­
lenges facing ACOs have been identified, including difficulties working across
organizational boundaries, building the requisite infrastructure for effective
data sharing, and truly engaging patients in the care process. One of the more
notable challenges currently being worked on is the alignment and consolida­
tion of myriad quality measures being used in public and private programs.

Effective quality measures are imperative to accountability in organized
systems of care, especially when performance affects the ability of the pro­
vider to share in savings or determines whether a provider avoids penalties
or receives bonus payments (Bipartisan Policy Center, 2015). However, the
notion of “measurement fatigue” and the increasing administrative burden
it places on providers is a legitimate concern (Buelt, Nichols, Nielsen, &
Patel, 2016). Another challenge with quality metrics is that although they
tend to capture performance on specific outcomes, such as lower avoidable
readmissions, or processes, such as screening for depression, they may not
accurately measure the overall health of the patient, making it diffi cult
to assess the true impact and efficacy of ACO arrangements (Houston &
McGinnis, 2016).

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Table 4.1 Key attributes and broad results of current ACO models

Medicare
Commercial Medicaid

Attribute MSSP Pioneer ACO ACOs ACOs

ACO
prevalence

333 ACOs in 47 states

Key model
features

Shared savings
payment methodology

33 quality metrics

Results to
date

CMS has reported
results for different
cohorts of MSSP ACOs
based on start date,
which have shown
signifi cant savings,
but it is diffi cult
to aggregate these
results, though only
26% of ACOs received
shared savings
payments

ACOs consistently
improved on 27 of 33
quality metrics.

Increases in patient
satisfaction relative to
patients not enrolled
in ACOs

18 ACOs in 8
states

Designed for large
hospital systems

Shared savings
system with
higher risk and
reward potential
than MSSP

Same 33 quality
metrics as MSSP

$304 million
in savings over
three years

ACOS consistently
improved on
28 of 33 quality
metrics.

Increases
in patient
satisfaction
relative to
patients not
enrolled in ACOs

Began with 32
participants; 14
have left program

528
commercial
contracts

Often
independent
contracts
between
ACOs and
MCOs

Many feature
narrow
provider
networks.

Not many
publicly
reported
results
available
across
programs
due to
proprietary
information
and
diffi culty
comparing
results

66 ACOs
in 9 active
state-based
programs

Various
approaches
to payment
including
shared savings
and capitation

Various
approaches
to quality
measurement

CO, MN,
and VT have
collectively
reported
$129.9 million
in savings.

ED visits in
OR decreased
by 22%.

Source: R. Houston and T. McGinnis. January 2016. “Accountable Care Organizations: Looking
Back and Moving Forward.” Center for Health Care Strategies. Used with permission.

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Implications for Health Care Leaders

Through the combination of changing health care business models and
payment mechanisms, we are witnessing transformational change in the
nature of health care delivery. It is evolving from one of reactive care with
fragmented accountability and a dependence on full beds to a model of health
management, care that extends over time and place and rewards for effi ciency
and quality. This transformation poses potent challenges for providers and
has enormous implications for today’s health care leaders, particularly by
placing greater emphasis on these issues:

• Keeping patients well and managing and preventing disease

• Establishing more effi cient organization and utilization of care teams

and venues of care

• Creating a care culture that is comfortable with change and ongoing

automation

• Engaging patients in managing their care and overall health

• Ensuring the most cost-effective care is provided and that clinical

processes are streamlined and follow the best evidence

More specifically, accountable care and the move to population health
management will require industry perspectives and health care delivery
practices to shift from

• Care providers working independently to collaborative teams of providers

• Treating individuals when they get sick to keeping groups of people

healthy

• Emphasizing volumes to emphasizing outcomes

• Maximizing the use of resources and assets to applying appropriate

levels of care at the right place

• Offering care at centralized facilities to providing care at sites

convenient to patients

• Treating all patients the same to customizing health care for each patient

• Avoiding the sickest chronically ill patients to providing special

chronic care services

• Being responsible for those who seek services to being responsible for
the needs of the community

• Putting forth best efforts to becoming high-reliability organizations

(Glaser, 2012b)

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Additionally, accountability will bring new performance and utilization
risks to providers as the focus shifts from optimizing business unit perfor­
mance to optimizing network performance. At the same time, instead of
maximizing the profitability of care, organizations will increase the volume
of desired bundled episodes while controlling costs. At an operational level,
organizations must change their structure as well as workflows to imple­
ment PHM and adopt new types of automation tools and reporting. This will
require setting clear goals, the active participation of leadership—including
physician leaders, an assessment of technology requirements, and an effective
rollout strategy (Institute for Health Technology Transformation, 2012).

Health IT clearly plays a vital role in the success of new models of care
and payment reform and should be an integral part of the organization’s
planning process. Whether participating in an ACO or not, all health care
organizations should be thinking about building a population health man­
agement strategy and addressing related gaps in their information technology
(IT) capabilities. Minimally, this would include acquiring the capabilities and
tools to do the following:

• Know, characterize, and predict the health trajectory that will happen
within a population.

• Engage members, families, and care providers to take action.

• Manage outcomes to improve health and care.

ACCOUNTABLE CARE CORE PROCESSES

Accountable care frameworks are based on risk and reward, with providers
and organizations agreeing to share the financial risk for a population in
return for the opportunity to access rewards on meeting health care quality
and cost goals. ACOs are responsible for tracking and measuring specifi c
quality metrics to indicate that patient outcomes are improving or evidence‐
based processes are being used. Some, but not necessarily all, metrics may
be tied directly to the payment methodology, meaning that performance on
these metrics will trigger either a quality incentive (such as an increased
percentage of shared savings) or a disincentive (such as not receiving any
shared savings) (Houston & McGinnis, 2016).

To accomplish the goals of PHM, a provider must deliver proactive pre­
ventive and chronic care to its attributed patient population. As such, the care
team must maintain regular contact with patients and support their efforts
to manage their own health. At the same time, care managers must closely
monitor high-risk patients to prevent them from deteriorating or developing
complications. The use of evidence-based protocols to diagnose and treat

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patients in a consistent, cost-effective manner is also central to PHM efforts.
In many respects, success in population health management depends largely
on a provider’s ability to manage several core processes in an accountable
care environment. We’ll review these core processes in the next sections.

Identifying, Assessing, Stratifying, and Selecting
Target Populations

To manage population health effectively, an organization must be able to
track and monitor the health of individual patients, while also stratifying its
population into subgroups that require particular services at specifi ed inter­
vals. ACOs typically stratify their patient population by common care needs,
conditions, and expenditure levels and then deploy tailored interventions
based on these characteristics (Houston & McGinnis, 2016). For example, a
high-risk pregnancy may require more frequent interventions (offi ce visits,
fetal heart monitoring, etc.) than standard prenatal care warrants.

Stratifi cation also involves the ability to identify a patient or cohort at
risk for a negative health event (e.g., myocardial infarction, stroke, mental
health crisis) or preventable health care utilization (e.g., surgical proce­
dure or hospitalization) (Gibson, Hunt, Knudson, Powell, Whittington, &
Wozney, 2015). The Agency for Healthcare Research and Quality (AHRQ)
describes another method of stratification as being able to identify subpop­
ulations of patients who might benefit from additional services. Examples of
these groups include patients needing reminders for preventive care or tests,
patients overdue for care or not meeting management goals, patients who have
failed to receive follow-up after being sent reminders, and patients who might
benefit from discussion of risk reduction (Institute for Health Technology
Transformation, 2012).

Although there are numerous ways to identify and segment patients,
having the ability to identify risk, alert appropriate stakeholders, and inter­
vene in the care process at the right time is a key component of population
health management.

Providing High-Quality Care and Care Management
Interventions across the Continuum

A key tenet of accountable care is to ensure that the health and wellness
of a population is managed, the most cost-effective care is provided, clini­
cal processes are streamlined and follow the best evidence, the necessary
reporting is in place, and payments and reimbursement are appropriate.
Although this is an obvious goal for all providers, ACOs must facilitate

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cross-continuum medical management of patients for active episodes and
acute disease processes or for any patient outside of the defined goals of
a target population. An ACO must demonstrate, in a variety of ways, its
commitment to being patient centered and to engaging patients in their
care and overall health.

To effectively care for populations, care management involves the
patient-centered management and coordination of care events and activities
in multiple care settings by one or more providers (e.g., fi ne-tuning coor­
dination among care team members, identifying care gaps and situations
requiring additional interventions, as well as managing care transitions). For
example, research indicates that poorly executed transitions of care between
different locations (e.g., from hospital to primary care) are associated with
increased risks of adverse medication events, hospital readmissions, and
higher health care costs. Determining which transitions present the greatest
risks and targeting care management services to patients undergoing those
transitions should conserve resources and lead to better cost and quality
outcomes (AHRQ, 2015).

Additionally, lack of follow-up care after hospital discharge can result
in complications, worsening of patients’ conditions, and a higher chance
of readmission (Nielsen & Shaljian, 2013). Therefore, another example of a
care management intervention is ensuring that hospitals notify primary care
practices when patients are discharged and that primary care teams follow
up with patients shortly thereafter.

The overall aim of care management is to manage the most complex
patients through the health care system, as well as managing the overall
health of a select population (e.g., diabetics and elderly), taking their prefer­
ences and overall situation into consideration. Care management ensures that
all patients from the lowest risk level to high-risk “super users” receive care at
the right time, in the right place, and in a manner best suited for the patient.
This requires proactive care, communication, education, and outreach.

Managing Contracts and Financial Performance

Under new payment models, proactively understanding patient coverage and
fi nancial responsibility will be more critical than ever. Financial teams must
have a solid handle on estimating reimbursement and associated payment
distributions, carrying out predictive modeling for reimbursement contracts,
measuring performance against contracts and predicting profitability, as well
as integrating with other key processes to share information.

For example, profit maximization under a shared savings-risk model
requires a shift away from revenue-focused strategies to cost-containment

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strategies (Houston & McGinnis, 2016). To effectively manage costs, health
care executives will need tools and data to support different types of fi nan­
cial modeling, such as modeling the implications of moving patient care to
settings other than the hospital or physician’s office. ACOs will also need
actuarial cost and utilization predictors to effectively manage the care of a
defi ned population.

These changes represent a significant cultural shift for provider organi­
zations that must be prepared to handle a complex mix of public and private
sector payment mechanisms.

Measuring, Predicting, and Improving Performance

Data analytics is an integral part of PHM. ACOs typically measure quality
and outcomes data against national guidelines or peer groups, and they seek
to demonstrate longitudinal improvements. They might also measure costs,
utilization, and patient experience on a population-wide basis, and they
may use these reports as the basis for quality reporting to payers and other
outside entities.

With payment so tightly linked to quality and outcomes, predicting, mon­
itoring, and measuring system performance in key areas becomes paramount
in an accountable care environment. Under value-based payment programs,
there will be real ramifications for poor care and rewards for improved care.
In fact, even low-performing areas can qualify for high payments if they
demonstrate year-over-year improvement.

Therefore, providers must have the ability to forecast which patients are
likely to become high-risk so they can intervene before a patient’s condition
worsens. They must also understand in real time if they are complying
with a certain set of measures and monitor their continual performance.
For example, ACOs will want to measure the effectiveness of care protocols,
such as exercise compliance, for a population of diabetic patients. Surgical
services providers will need to understand the costs and quality of proposed
procedure bundles. Understanding what works and what does not is key to
ensuring reimbursements, controlling costs, and, most important, providing
the best care for patients (Glaser, 2012a).

Equally important is retrospective monitoring—finding out what
didn’t happen and why. For example, if a care provider failed to respond
to an alert in a timely fashion or deviated from a given standard of care
process, they can use these data to determine if new care interventions are
necessary or if they need to alter an individual’s plan of care. Likewise,
knowing that a patient failed to keep an appointment or was unexpect­
edly seen in the emergency room will enable the care team to engage

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patients in new ways to better manage chronic disease. With providers
facing penalties for readmission, it will be more important than ever to
understand if it’s the treatment that failed, the discharge plan that failed,
or the patient who did not follow through on the post-discharge plan
(Chopra & Glaser, 2013).

Preparation and Automation Is Key

Overall, the accountable care movement demands that providers be more
focused and aggressive in managing their organization and their patients.
Among other challenges, changes in reimbursement will require providers
to predict which patients will need extra care, more intensively engage and
manage high-risk patients, model the financial implications of delivering
sub-par care, assess the performance of core organizational processes such as
transitions of care, determine conformance to medical evidence, and report
quality measures to purchasers of care.

The long-term success of the transition to value-based payment models
and PHM relies largely on health care providers investing in the IT tools
and infrastructure—as well as acquiring the data management and analysis
expertise—needed to automate and support these core processes. In addition,
as with any IT endeavor, expertise in change management and workfl ow
redesign is also a core requirement.

Even for providers that may not be participating in an ACO, building the
organizational and IT competencies to support accountable care is critical
to staying competitive. Organizations that fail to develop and demonstrate
accountable care capabilities may not fulfill their obligations to the commu­
nity they serve—in fact, they may not survive.

Yet, organizations embracing the transformation from traditional fee-for­
service to value-based PHM are fi nding significant gaps in their IT capabil­
ities (Gibson et al., 2015). In the following section we examine the core IT
building blocks and capabilities necessary to support accountable care and
the move to PHM.

DATA, ANALYTICS, AND HEALTH IT CAPABILITIES

AND TOOLS

As more providers and health systems evolve into ACOs, they are becom­
ing increasingly aware of what it takes to manage care from a population
health perspective. As we know, this includes establishing new partner net­
works, targeting populations, aligning providers and contracts, developing

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cross-continuum protocols for care management, and enabling effi cient data
sharing.

It’s All about the Data

For a PHM program to be effective there is a critical need to focus on the data
and information that will increasingly power clinical decisions. This includes
aggregating and normalizing clinical data, claims data, administrative data,
and self-reported patient data to create a holistic view of the patients within
a health care network. These data enable the network to identify populations
of patients whose conditions can be managed through evidence-based care
plans that are coordinated across care settings.

For example, the risk of progression from glucose intolerance to dia­
betes mellitus can be influenced by diet and exercise. Individuals within
this “rising risk” population are at different stages of readiness to change
and consequently at different stages of modifiable risk. Having this insight
enables providers to offer services at the appropriate level and time
(AHRQ, 2015).

However, for many organizations, obtaining population health data can
be difficult because it must be collected and organized from many disparate
sources (e.g., laboratory information systems, EHRs, practice management
systems, and home-monitoring devices). Data types that require aggrega­
tion and normalization include labs, radiology reports, medications, vital
signs, diagnoses, demographic information, and more. Returning to our
diabetes example, although a diabetic’s blood glucose result is discrete
data that can be found in an EHR, the results of the same patient’s foot or
eye exam may be found only in text format within a practice management
system.

Data management for PHM purposes is also challenging because there’s
no guarantee the various IT systems talk to each other, and each provider and
health plan may have a different system for patient identification and provider
attribution. An important first step in connecting patient data across different
care settings is to establish master patient indices (Glaser & Salzberg, 2011).
Patient indices can serve as a crosswalk among the different medical record
numbers and identifiers that may be used by various provider organizations
to correctly identify patients. In addition, a record locator service may be
used to determine which patient records exist for a member and where the
source data is located. The key concept behind having a record locator service
is that a patient’s health information is housed on computers at the various
sites of his or her care and this information is queried and aggregated from
these sites at the time of a request.

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Beyond the EHR: Core PHM Solution Components

Although a certified EHR certainly provides the necessary foundation for
effectively responding to new payment models, population health requires a
range of IT applications, PHM solution components, and analytical capabili­
ties. In fact, early adopters of PHM solutions are already seeing the need for
next-generation capabilities to support the following transitions:

• From management of the sickest patients to management of all
patients

• Static risk categorization to risk categorization that follows a patient’s
evolving risk

• Focus on a single disease or condition based on simple data values
and events to a focus on multi-disease or condition using evidence-
based care plans

• “List” generation with signifi cant manual work for care managers to
signifi cant process automation

• Loosely connected care “actors” to a care team that includes the
patient and family

• Retrospective analysis to concurrent analysis (Glaser, 2016a)

As organizations look to enhance their population health management
strategies, they should make investments that enable the IT platform to do
the following:

• Collect data from multiple, disparate sources in near–real time,
including any EHR, devices used in the home and at work, and other
data sources, such as pharmacy benefi t managers or insurance claims.

• Support organizations in not only aggregating but also transforming
and reconciling data to establish a longitudinal record for each
individual within a population.

• Identify and stratify populations to pinpoint gaps in care, enabling
providers to act on information and match the right care programs to
the right individuals (Glaser, 2016a).

In addition to having an EHR that spans the continuum of care, pro­
viders pursuing PHM might invest in a PHM platform that sits above the
EHR and other sources of data and must be EHR agnostic. In general,
the following key technologies will enable the core accountable care
processes.

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Revenue Cycle Systems and Contract Management
Applications

One could argue that the revenue cycle system forms the foundation of a
provider’s response to accountable care and payment reform. As the reim­
bursement environment becomes more complex, revenue cycle systems must
evolve to support payments based on quality and performance, requiring new
capabilities such as these:

• Aggregating charges to form bundles and episodes, with the

aggregation logic enabling different groupings for different payers

• Managing the distribution of payment for a bundle to the physicians,

hospitals, and non-acute facilities that delivered the care

• Streamlining transitions between disparate reimbursement

methodologies and contracts when billing and collecting

• Providing tools for retrospective analysis of clinical and administrative
data to identify areas for improving the quality of care and reducing
the cost of care delivered

These new capabilities must complement routine activities such as
registering patients, scheduling appointments, and administering patient
billing.

Care Management Systems

Used by care managers and discussed previously, care management systems
enable proactive surveillance, automation, coordination, and facilitation of
services for many different subpopulations across the care continuum. Spe­
cific capabilities might include helping to facilitate transitions of care more
efficiently, use of automated campaigns (e-mail, text, phone) to better manage
high-risk patients, and supporting care teams in delivering evidence-based
interventions to reduce high-cost utilization.

According to time-motion studies published in the journal Population
Health Management by Prevea Health, automation of routine care manage­
ment tasks enables care managers to manage two to three times as many
patients as they can with manual methods (Handmaker & Hart, 2015).

Rules Engines and Workfl ow Engines

Processes that are efficient, predictable, and robust enable an organization
to thrive in an accountable care environment. Workflow and rules engines

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can monitor process performance, alerting staff members to missed steps,
sequence issues, or delays.

Workflow engines specialize in executing a business process, not just
decisions made at a discrete point in time. The technology can greatly
assist in clinical decision making by not only presenting clinicians with
alerts and reminders, such as a rules engine, but also by encouraging
teamwork in clinical decisions, assisting with the time management and
task allocation in process delivery, stating changes in patient or opera­
tional conditions, and creating behind-the-scenes automation of process
steps.

In a value-based purchasing world where each core measure needs to
be associated with what’s happening today, performance improvement inter­
ventions must occur in real time—that is, while the patient is still in the
acute care cycle. Therefore, sophisticated IT tools such as workflow and rules
engines that push information to the front lines, guiding decisions at the point
of highest possible impact, will be required.

Data Warehouse, Analytics, and Business Intelligence

Analytics will facilitate proactive management of key performance metrics,
because accountable care creates a greater need to assess care quality and
costs, examine variations in practice, and compare outcomes.

An enterprise data warehouse will fuel a wide range of analytic needs
and provide intelligence to enable continual care process improvement
initiatives. For example, it will be imperative that an organization can
compare a hypertensive patient’s total cost of care relative to its peers and
national benchmarks, and perhaps even more important, predict if those
costs will significantly increase because of comorbidities, complications, or
gaps in care.

Applied to the data in registries or warehouses, predictive analytics tools
can also help caregivers identify patients who are likely to present in the
ER or be readmitted so they can tailor appropriate interventions and avoid
penalties for excessive readmissions.

Although most providers lack experience with the tools and techniques
associated with advanced data analysis, the application of business intelli­
gence (BI) in health care will become the platform on which the organization
not only monitors performance but also makes critical decisions to uncover
new revenue opportunities, reduce costs, reallocate resources, and improve
care quality and operational efficiency. Thus, enhancing an organization’s
competency in data analytics and BI will become essential for success in
population health management.

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Health Information Exchange (HIE)

Essential to successful implementation of new models of care and payment reform
is the exchange of clinical and administration information among different health
care entities and between providers and patients. Although there has been some
success in the regional health information exchange (HIE) movement, much of
the focus now is on HIE capabilities at the integrated delivery system or ACO
level. This enables providers to obtain a composite clinical picture of the patient
regardless of where that patient was seen. By participating in an HIE or sharing
health information, a number of potential important benefits may be realized:

• Serves as a building block for improved patient care, quality, and safety

• Makes relevant health care information readily available when and

where it is needed

• Provides the means to reduce duplication of services that can lead to

reduced health care costs

• Enables automation of administrative tasks

• Provides governance and management over the data exchange process

• Facilitates achievement of meaningful use requirements (HIMSS, 2010)

The concept of HIE is not new. For nearly two decades organizations
and collaborators have tried to facilitate HIE, but unfortunately a number
of HIE initiatives have failed to be sustainable over the long term (Vest &
Gamm, 2010). The HITECH Act placed renewed interest in the success of HIE
by providing incentive payments to eligible providers for Meaningful Use
of electronic health records, which includes having the ability to exchange
information electronically with others in order to have a comprehensive view
of the patient’s health and care (Rudin, Salzberg, Szolovitis, Volk, Simon, &
Bates, 2011). However, despite investment at the national, state, and local
levels, the increase in HIE utilization remains modest.

In fact, a recent survey of organizations facilitating health information
exchange found that 30  percent of hospitals and 10  percent of ambulatory
practices now participate in one of the 119 operational health information
exchange efforts across the United States (Adler-Milstein, Bates, & Jha, 2013).
Although this is substantial growth from prior surveys, the researchers also
found that 74  percent of HIE efforts report struggling to develop a sustain­
able business model. These findings suggest that despite progress, there
is a substantial risk that many current efforts to promote health informa­
tion exchange will fail when public funds supporting these initiatives are
depleted. Adding to the challenge, HIE efforts have struggled to engage payers,

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Figure 4.1 Percent of nonfederal acute care hospitals that electronically exchanged
laboratory results, radiology reports, clinical care summaries, or medication lists
with ambulatory care providers or hospitals outside their organization: 2008–2015

Source: Henry, Patel, Pylypchuk, and Searcy (2016).

and only 40 percent of HIE efforts in the country have one or more payers
providing financial support (Adler-Milstein, Cross, & Lin, 2016).

Still, there is reason to remain optimistic, with more recent data showing
that hospitals’ rates of electronically exchanging laboratory results, radiol­
ogy reports, clinical care summaries, or medication lists with ambulatory
care providers or hospitals outside their organization has doubled since
2008 (see Figure 4.1). Moreover, this exchange has signifi cantly increased
annually since 2011 (Henry, Patel, Pylypchuk, & Searcy, 2016).

Although there is still signifi cant progress to be made to improve the use
of exchanged information and to address barriers to interoperability, HIE is
critically important to the success of care transformation efforts nationwide.
Thus, the industry must continue its efforts toward achieving sustainable
HIE approaches to ensure that the massive national investment in health IT
throughout the past decade delivers its intended return—higher-quality care,
improved outcomes, and lower cost.

Registries and Scorecards

Serving as a kind of central database for PHM, registries can be used for
patient monitoring, care gap assessment, point-of-care reminders, care man­
agement, and public health and quality reporting, among other uses. By
integrating clinical, financial, and operational data across disparate sources
into a single chronic condition and wellness registry solution, data can be
normalized and turned into meaningful, actionable information.

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For example, registries and scorecards enable providers to identify, score,
and predict risks of individuals or populations to allow targeted interventions
to be implemented. When applied to a population, the registry can show, for
example, how all of a particular provider’s patients with type 2 diabetes are
doing, which diabetic patients are out of control, or how well an entire orga­
nization is treating patients with that condition (Nielsen & Shaljian, 2013).

Longitudinal Record and Care Plan

As we know, even if a provider is diligently capturing patient information in
an EHR, the data are valuable only in the world of collaborative, accountable
care if the information can be integrated with patient data from other sources
and harmonized to produce a single, consolidated record at the member level.
The longitudinal record presents a complete picture of the patient’s medical
history in an organized, coherent view.

Serving as the sister solution to the longitudinal record, a longitudinal
care plan provides a consolidated, normalized view of indicators to be mon­
itored, events due to happen, and actions to be taken to ensure that a patient
maintains and improves his or her level of health.

Patient Engagement Tools

Medical interventions that occur solely through offi ce-based patient-provider
interactions will no longer provide the level of monitoring and scrutiny needed
to manage the health of individuals and populations. As such, providers must
continue to harness the power of technology to engage patients in their care
via tools such as home-monitoring devices, patient portals, and personal
health records (PHRs), as well as through the use of social media, texting,
and e-mail.

Portals and PHRs

Although patient portal use is still considered modest at best, given later-stage
meaningful-use requirements and the anticipated benefits of patient engagement
in the value-based care world, many providers are ramping up their portal efforts
and seeing adoption rates well above 20 percent (Buckley, 2015). Another recent
study predicts that PHR adoption will exceed 75 percent by 2020, an optimistic
projection that outpaces the PHR goals set under the Meaningful Use incentive
program (Ford, Hesse, & Huerta, 2016). These consumer-centric technologies are
designed to help patients and consumers better manage their own health and
care, securely communicate with providers, pay bills, obtain test results, view
doctors’ notes, refill prescriptions, schedule appointments, and so on.

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Despite the fact that the environment for building, creating, and developing
an HIE organization has never been better, the concerns about long-term
sustainability and the impact and value of exchanging health information
persist. The National eHealth Collaborative (NeHC) conducted a comprehen­
sive study of twelve fully operationally HIEs across the nation to find out from
their leaders what factors have led to their success (NeHC, 2011). In-depth
structured interviews were conducted with senior executives representing the
business, clinical, and technical areas of each HIE. The key critical success
factors these leaders identifi ed in sustaining an HIE are as follows:

• Aligning stakeholders with HIE priorities in an intensive and ongoing
effort. Create a shared vision that all stakeholders can embrace and that
serves as the cornerstone to success. Foster an environment that is built
on trust and that promotes learning and resolves differences when they
arise. Make ongoing and effective stakeholder engagement a priority.

• Establishing and maintaining consistent brand identity and role as a
trusted, neutral entity dedicated to protecting the interests of participants.
Data use and data integrity are two critical elements. The culture, policies,
and procedures regarding the use of data must ensure that no entity will
gain competitive advantage at the expense of others. Consent and security
policies must meet the requirements of various stakeholders and regions or

Some patient portals and PHRs are integrated into a provider’s existing
website, and others are extensions of the organization’s EHR system. For
example, New York-Presbyterian (NYP) Hospital’s award-winning patient
portal, myNYP.org, was built to expand on its existing EHR. Use of the portal
led to a 42 percent increase of appointments scheduled using myNYP.org, and
it lowered the no-show rated from 20 percent to 12 percent over a period of six
months after it was made available in January 2012 (Glaser, 2013). Additional
applications of the same appointment-alert technology can provide custom­
ized patient education material and personalized reminders to patients who
fit a specific clinical profile, such as patients who missed an immunization.

Social Media

Additionally, with one-third of consumers using online forums and social
media sites such as Facebook, Twitter, and YouTube for health-related matters

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PERSPECTIVE
The HIE Lessons

states. The HIE infrastructure must ensure that patient data are accurate,
reliable, and trustworthy.

• Ensuring alignment with vision in making strategic choices. Assess the
stakeholders’ alignment with the initiative and congruence with the
vision before deciding to pursue them. Regardless of how promising a
source of funding may have initially appeared, some HIEs chose not to
pursue it because the funding source did not have the full support of all
stakeholders.

• Considering structural characteristics and dynamics of the HIE market.
The geographic location, composition of stakeholders, and resource
capabilities are all factors to consider.

• Understanding clinical workflow and managing change. The imple­
mentation of an HIE requires that clinicians and administrative staff
members understand the impact of HIE applications on workfl ow and
identify opportunities to improve effi ciencies.

Different business models, governance structures, and strategies may be
used to create value for the HIE participants.

Source: NeHC (2011).

(PwC, 2012), many providers are actively engaged in using social media to
communicate with patients and disseminate information on everything from
emergency department wait times to new clinical offerings and research
endeavors. They might also use social media channels to provide useful
links to self-management tools and invitations to chronic care management
programs. In fact, nearly 95 percent of hospitals have a Facebook page and
just over 50 percent have a Twitter account (Griffis et al., 2014).

Automated Messaging

Similar to social media, the use of automated messaging tools (via text,
e-mail, or phone) can be equally beneficial in urging patients to sched­
ule necessary appointments, fill their prescriptions, and comply with dis­
charge orders. For example, one study showed that diabetic and hypertensive
patients were two to three times more likely to attend a chronic care visit if

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Given the modest adoption rates of PHRs and patient portals to date,
research firm KLAS asked providers what best practices for patient portal
adoption they would pass along to other providers trying to improve their
rates. The following are their suggestions:

1. Educate patients.
“What contributes to adoption is educating our patients about the portal,
helping them sign up, and encouraging them to use it. But education is key.
Patients have embraced the portal and use it for much of our communica­
tion, bill pay, results review, and more.”

2. Educate patients—again and again.
“We ask patients on the phone whether they have signed up for the portal,
and at their appointments we check to see whether they have fi lled things
out on the portal. Then the medical assistants who greet the patients ask
whether they have put their information on the portal. We promote the
portal five or six times. On their way out, the doctors tell the patients that
they are going to send their results to the portal.”

3. Educate staff members as if they were patients.
“The patients get inundated and get tired of hearing it, but it was the kickoff
that got everybody in the practice used to pushing the portal. We also made
everyone here register on the portal to see what the patients would go
through and so we could make changes and adjustments to fit our needs. It
is an ongoing process, and we try to do contests every quarter. That is what
contributes to our success, and it is pretty impressive.”

4. Give patients a reason to use the portal.
“We are apparently doing something right in encouraging patients to come
to our portal. They come to the portal to fill out the patient history and

successfully contacted using automated provider communications (Nielsen
& Shaljian, 2013).

PHM is most effective when a symbiotic relationship exists between
human interventions and automation tools. Patient engagement tools and
outreach programs enable providers to correspond with each person in their
patient populations, with the goal of raising the percentages of patients
receiving the recommended care as reflected in the quality measures payers
use to evaluate provider and health system performance. More important,

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PERSPECTIVE
Top Tips for Portal Adoption

the medication list. I think that is because of the way our front desk staff
members make new-patient appointments and the way they present the
portal to the patients. They tell them that we can give them less waiting
time when they come in if they get on the portal. We have an aggressive
sign-up process. We give patients a Chromebook in the waiting room and
help them sign up for the portal right away. We have a similar process in
the ED and inpatient areas. We try to push as much content to the portal
as possible.”

5. Talk to your vendor and physicians.
“We drove adoption from the top down. In our initial phase, the adoption
didn’t go well because we thought we knew what we were doing and could
do it ourselves. We went back and listened to Medfusion. We took the
portal to the doctors who understand technology. They came back from a
CMS meeting and said we had to do the portal. They said we might not like
it, but we have to do it.”

6. Hold your vendor accountable.
“When we started to deploy Empower in our ambulatory area, we hit chal­
lenges and barriers with the physician group. The physicians really wanted
to yank the product out; they didn’t want anything to do with it. They were
beyond frustrated. We worked with MEDSEEK and the physicians, and in the
last year and a half, we went from having a handful of patients on the portal
to having sixty-five thousand. We were fi nally able to leverage the solution in
the ambulatory space after we made changes to the product and the interface.
There were deal breakers in how the product looked and felt from a patient
perspective, and we worked through those.”

Source: Buckley (2015). Used with permission.

such programs assist providers in keeping patients as healthy as possible for
as long as possible, a core tenant of PHM.

Telemedicine and Telehealth

The growing use of telemedicine can make patient interactions more convenient,
expand geographic horizons particularly where needed medical specialists are
few in number, and make care more accessible to those with mobility issues.

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With an abundance of patient-generated health information now available
through online patient communities, social media can play a vital role in
improving our understanding of disease and accelerating new approaches
to treatment. Consider the following ways patient and consumer use of
social media is benefi ting health care.

Creates a Sense of Community
For those seeking emotional support and tips for coping with a disease,
social media delivers on many fronts. It can enable the formation of com­
munities regardless of member locations and enable members to commu­
nicate asynchronously.

Sites such as PatientsLikeMe and Inspire provide virtual medical com­
munities focused on chronic diseases where patients can discuss their con­
ditions, track key health information, share side effects of medications and
therapies, and bond with others as they chronicle the highs and lows of
their health care journeys.

In fact, a 2014 survey of PatientsLikeMe members found that the vast
majority of adult social media users with health conditions embrace the
idea of sharing their health information online if it helps clinicians improve
care, assists other patients, or advances medical research.

Users of online health communities also frequently cite as reasons for
their membership the accountability the sites provide them in managing
their own health and reaching their health-related goals, as well as the
motivation, support, and advice they receive from others. Online commu­
nities can also lessen the feeling of isolation that often accompanies those
with rare conditions or parents with a critically ill child.

Delivers New Clinical Research Insights
As more and more patients use social media to track their health conditions
and actively participate in their care, there is a greater opportunity to use
this real-world data to better inform new treatments and treatment deci­
sions, enhance symptom management, and ultimately improve outcomes.

For example, in analyzing the results of observational data housed on
PatientsLikeMe, researchers found that lithium therapy had no impact on
ALS disease progression, which was later confirmed by subsequent rand­
omized trials (Chretien & Kind, 2013).

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PERSPECTIVE
Five Reasons to “Like” Consumers’ Use of Social Media

Although PatientsLikeMe began as a social network enabling people to
crowdsource the collective wisdom of others, it has developed into a pow­
erful analytical platform for clinicians and researchers. In fact, the network
is quite transparent with its members about how it makes money—by
sharing the information members provide about their experience with dis­
eases and selling it to their partners (companies that are developing or
selling products to patients). This may include drugs, devices, equipment
insurance, or medical services.

In addition to helping patients find and take advantage of clinical
trials, health care social networks also provide an opportunity for par-
ticipant-led research, in which members initiate new fields of study. For
instance, Inspire members with spontaneous coronary artery dissection
(SCAD) persuaded researchers at the Mayo Clinic to launch new research
about their condition, which led to the creation of a SCAD registry, a key
step in the further study of this rare disease (Tweet, Gulati, Aase, & Hayes,
2011). Indeed, there is tremendous potential for online patient communities
to contribute to the notion of a continuously learning health system.

Builds Awareness of Cause-Related Issues or Personal Health Care Crises
Social media can also serve as the birthplace for beneficial social move­
ments, as well as hubs for galvanizing emotional and financial support for
a personal health care crisis.

The ALS Ice Bucket Challenge is a terrific example of social media’s
power to deliver on the fund-raising aspect of the campaign and on the
equally important goal of helping the public become more aware of ALS
and efforts to find a cure.

The simple act of pouring ice on one’s head, capturing it on video, and
calling out another person to do the same spread across social media chan­
nels like wildfire. With everyone from schoolchildren to celebrities getting
in on the act, the ALS Association raised $115 million in 2014, a staggering
increase from its $23.5 million intake in 2013 (ALS Association, 2015).

On a smaller scale, sites such as GoFundMe and My Cancer Circle can
help keep family and friends abreast of a loved one’s illness and treatment
status, provide tools to coordinate meal deliveries and rides to medical
appointments, as well as enable financial contributions to help offset per­
sonal health care expenses.

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Provides Assistance with Treatment, Physician, or Hospital Selection
Although physician rating sites have been around for many years, social

media has given health care consumers a more active voice and an ever-

present tool set for broadcasting opinions on all things health care–related—

from physicians and hospitals to medications, devices, and insurance plans.

Like it or not, social media is proving to be a vehicle that can help scale

positive and negative attitudes about one’s health care experience at Inter­
net speed. In fact, a 2012 survey by Demi & Cooper Advertising and DC

Interactive found that 41 percent of people said social media would affect

their choice of a specifi c doctor, hospital, or medical facility.

Of course, the downside here is that the negative opinions of a vocal

minority could cause unjust reputation management issues for providers.

With the viewpoints of those in online social networks playing such

a key role in influencing health care decisions, providers ought to ensure

they are optimizing their social media channels and actively participating

in helping consumers share positive opinions online.

Complements Traditional Approaches to Measuring Patient Satisfaction
Beyond just randomly monitoring opinions shared on social media,

savvy providers may want to turn to social media to supplement their

The American Telemedicine Association defines telemedicine or tele­
health as exchanging medical information via electronic communications to
improve a patient’s clinical health status. Health care providers are embrac­
ing telemedicine because they see it as an efficient and cost-effective way to
deliver quality care and improve patient satisfaction (Glaser, 2015a). Today’s
telehealth framework spans the continuum of care and can include services
such as the following:

• Telepsychiatry

• Remote image interpretation (teleradiology, teledermatology)

• e-Visits or televisits between providers and their patients

• Video visits for semi-urgent care

• Clinician-to-clinician consultations

• Critical care (virtual ICU, telestroke)

• Remote monitoring of a patient with a chronic disease

• Cybersurgery or telesurgery

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traditional means of capturing patient satisfaction and feedback on inpa­
tient experience.

In fact, researchers at Boston Children’s Hospital conducted a study
to determine if Twitter could provide a reasonable form of complemen­
tary quality measurement, given the real-time nature of tweets. The team
amassed unsolicited knowledge (versus data gleaned from very targeted
survey questions) about what pleased or angered consumers by collecting
more than 400,000 tweets directed at the Twitter handles of nearly 2,400
US hospitals between 2012 and 2013 (Ulrich, 2015).

Although certainly no replacement for patient satisfaction surveys,
according to the researchers the data are suggestive and provide proof of
principle that Twitter and the right analytical tools may provide a valua­
ble means for complementing standard approaches to measuring quality.
Moreover, the ability to correlate social media data points such as tweets
with actual outcomes measures (e.g., patient length of stay in the emer­
gency department or readmission rates) provides an interesting avenue for
further exploration.

Source: Glaser (2016b). Reprinted from H&HN Daily by permission, April 11, 2016,
Copyright 2016, by Health Forum, Inc.

Let’s take a closer look at some of the more popular applications of tele­
medicine and telehealth. Two-way interactive video-conferencing or other
web-based technologies can be used when a face-to-face consultation is
necessary. In addition, a number of peripheral devices can be linked to com­
puters to aid in interactive examination. For example, a stethoscope can be
linked to a computer, enabling the consulting physician to hear the patient’s
heartbeat from a distance. Electronic monitoring of physiological vital signs
can be done through electronic intensive care unit (eICU) patient-monitoring
systems, and telesurgery can enable a surgeon in one location to remotely
control a robotic arm to perform surgery in another location.

Telehealth is also being used to capture and monitor data from patients
at home. Examples include monitoring patient blood sugar levels through
glucometers attached to cell phones and conducting teledermatology visits
with the aid of cell phone cameras.

According to the American Hospital Association (AHA), 52 percent of
hospitals used some form of telehealth in 2013, and another 10 percent were
beginning to implement such services (AHA, 2015). Its growth potential is

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also notable. Business information provider IHS predicts the US telehealth
market will grow from $240 million in revenue in 2013 to $1.9 billion in
2018—an annual growth rate of more than 50 percent (EY, 2014).

In addition to the growing demand for access and convenience, the need
for telemedicine is driven by other factors such as the following:

• Signifi cant increase in the US population

• Shortage of licensed health care professionals

• Increasing incidence of chronic diseases

• Need for effi cient care of the elderly, homebound, and physically
challenged patients

• Lack of specialists and health facilities in rural areas and in many
urban areas

• Avoidance of adverse events, injuries, and illnesses that can occur
within the health care system

These factors become increasingly important as new health care delivery
and payment models evolve and providers are challenged to better manage
chronic diseases, avoid readmissions, improve quality, and remove low acuity
care from high-cost venues. As we know, the long-term benefits of population
health programs are predicated in large part on managing high-cost, chron­
ically ill patient populations more effectively. Furthermore, the rapid deploy­
ment of high deductible health plans, which make consumers more conscious
and accountable for their health care consumption and spending, has added
to the pressure on providers to provide low-cost, convenient options.

Despite all its promise, several major barriers must be addressed if tele­
medicine is to be used more widely and become available. Concerns about
provider acceptance, interstate licensure, overall confidentiality and liability,
data standards, and lack of universal reimbursement for telemedicine services
from public and private payers are among the complex and evolving issues
affecting the widespread use of telemedicine. Furthermore, its cost-effectiveness
has yet to be fully demonstrated.

Nonetheless, the barriers are beginning to erode under mounting pres­
sure from all health care constituents. Licensure portability will further ease
the barriers to accessing services, whereas regulatory and payment policy
changes in support of telehealth are widely expected in the coming years.
For instance, on the private payer side, telemedicine use has been bolstered
by a growing number of states enacting parity laws, which require health
insurers to treat telehealth services the same way they would in-person
services.

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TRANSITIONING FROM THE RECORD TO THE PLAN

As we reviewed in this chapter, the profound changes in reimbursement and
care models are altering the structure of care provision, requiring providers
to make investments in a comprehensive IT portfolio—beyond the EHR—to
support PHM and enable the core processes associated with accountable care.
These changing business and payment models are leading not only to signif­
icant changes in organization and practice but also to changes in the funda­
mental nature and design of the EHR itself. These changes can be characterized
as a transition from the electronic health record to the electronic health plan
(Glaser, 2015b).

The EHR does not disappear as a result of this shift. We will still need
traditional EHR capabilities: providers need to review a radiology report
and document a patient’s history and the care delivered. Problems must be
recorded and medications reconciled. However, the strategic emphasis will
move to technologies and applications that assist the care team (including
the patient) in developing and managing the longitudinal, cross-venue health
plan and assessing the outcomes of that plan.

For example, evidence-based pathways and decision-support logic have
been embedded into EHRs to guide provider decisions according to a plan
based on patient condition. EHRs can now include or be enhanced by the
specific PHM technologies we discussed that enable the organization to
understand its aggregate performance in undertaking disease-specifi c plans
for multiple patients.

Provider organizations will not thrive in an era of health reform because
they have a superb and interoperable EHR. They will thrive because the care
they deliver consistently follows a plan designed to ensure desired outcomes.
The EHR must evolve so it focuses on individual patients’ care plans—the
steps required to maintain or create health.

Every patient’s EHR should clearly display the master care plan—a long-
term care plan to maintain health integrated with short-term plans for transient
conditions. The EHR should be organized according to this master plan: it
should highlight the steps needed to recover or maintain health, list the expec­
tations of every caregiver the patient interacts with, and include tools such
as decision support and a library of standard care plans. Interoperability is a
necessity, because various providers must be able to use the plan-based EHR.

Care Plan Attributes

The care health plan has attributes that need to be present to ensure health
and should be based on some fundamental ideas.

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First, all people have a foundational plan. If the person is a healthy young
man, the plan may be simple: establishing health behaviors such as exercise.
If the person is a middle-aged man with high cholesterol and sleep apnea,
the plan may be annual physicals, statins, a CPAP machine, and a periodic
colonoscopy. If a person is frail and elderly with multiple chronic diseases,
the plan may be merging the care for each chronic condition, ensuring proper
diet, and providing transportation for clinic visits.

Second, plans are a combination of medical care strategies with goals to
maintain health (such as losing weight) along with public health campaigns
(such as immunizations).

Third, on top of foundational plans there may be transient plans. For the
patient undergoing a hip replacement there is a time-bounded plan beginning
with presurgery testing and ending when rehabilitation has been completed.
A patient undergoing a bad case of the flu has a time-bounded plan.

Fourth, people who have a common plan are members of the same pop­
ulation. These populations may be all patients undergoing a coronary artery
bypass graft in a hospital, all patients with a certain chronic disease, or all
patients at high risk of coronary artery disease. Moreover, a particular person
may be a member of multiple populations at the same time.

Fifth, risk is the likelihood that the plan will not be followed or will not
result in desired outcomes. A patient motivated to manage his or her blood
pressure has a lower risk than a patient who is not motivated. A frail person
with multiple chronic diseases is at greater risk that the plans will not keep
him or her out of the hospital than a person whose health is generally good
despite having multiple chronic diseases.

Sixth, not all care will be amenable to a predefined patient plan. Life-
threatening trauma, diseases of mysterious origin, sudden complications—all
require skilled caregivers to make the best decisions possible at the moment.

Seventh, plans should be based on the evidence of best care and health prac­
tices. And the effectiveness of a plan should be measurable, either in terms of
plan steps being completed or desired outcomes being achieved (Glaser, 2015a).

The Plan-Centric EHR

The EHR needs to evolve into plan-centric applications. Among others, these
applications will have several key characteristics.

A Library of Plans That Cover a Wide Range of Situations

This library will include, for instance, plans for managing hypertension,
removing an appendix, losing weight, and treating cervical cancer. There

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will be variations in plans that reflect variations in patient circumstances
and preferences, for example, plans that depend on whether the patient is a
well-managed diabetic or plans that reflect the slower surgical recovery time
of an elderly person.

Algorithms to Form a Patient’s Master Plan

A master plan will combine, for example, the patient’s asthma, hysterectomy,
depression, and weight-reduction plans into a single plan. These algorithms
will identify conflicts and redundancies among the plans and highlight the
care steps that optimize a patient’s health for all plans. For example, if each
of the five plans has six care steps, the algorithms can determine which steps
are the most important.

Team-Based

The master plan will cover the steps to be carried out by a patient’s primary
care provider, specialists, nurse practitioners, pharmacists, case managers, and
the patient. Each team member can see the master plan and his or her specifi c
portion of the plan. Team members can assign tasks to each other (Glaser, 2015a).

Business Models in Other Industries

Major changes in an industry’s business model invariably lead to major changes
in the focus and form of the core applications used by that industry. For
example, financial services, retailers, and music distributors, along with many
other industries, have also experienced massive shifts in their business models.

Several decades ago, financial deregulation enabled banks to offer bro­
kerage services. The business model of many banks shifted from banking
(offering mortgages as well as checking and savings accounts) to wealth
management. As banks shifted from transaction-oriented services to services
that optimized a customer’s financial assets, their core applications broad­
ened to include an additional set of transactions (buying and selling stocks)
and new services (financial advisory services).

Prior to the web, most retailers’ business models focused on establishing
a brand, offering an appropriate set of well-priced products, and building
attractive stores in convenient locations. The web enabled retailers to gather
significantly richer data about a customer’s buying patterns and interests (and
to use real-time logic to guide purchasing decisions). Retailers’ core applica­
tions broadened to include well-designed e-commerce sites and analytics of
customer behavior.

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In both examples, even though there was a significant shift in the business
model, applications needed for the previous model continued to be necessary.
Banks still had to handle savings account and mortgage payment transactions.
Retailers still needed to manage inventory. And advances in these legacy
applications—expanding inventory breadth and reducing inventory-carrying
costs—continue to be important. In each case, a critical new set of applica­
tions were added to the legacy applications. Often, these new applications
were more important than legacy applications.

The business model changes in health care will lead to a shift from appli­
cations focused on the patient’s record to applications focused on the patient’s
plan for health. This evolution in the nature of the EHR is a key component
to achieving success in population health management.

SUMMARY

As the health care industry continues its transition from a fragmented, volume-
based system toward one that embraces the notion of patient-centered,
accountable care driven by value-based payment models, providers must
consider what new relationships, processes, and IT assets and skills will be
required to succeed—particularly when it comes to managing the health and
care of attributed populations.

By implementing a PHM strategy, organizations have enormous oppor­
tunity to use data and analytics to improve inefficiency and waste, thereby
reducing costs, and monitor adherence to evidence-based protocols to drive
better outcomes. Several PCMHs and ACOs are already showing promising
performance in the emerging world of value-based payment and population
health management.

In addition to having a robust EHR, organizations looking to enhance
their PHM strategies should consider several key solution components.
PHM technologies can help providers stratify and select target populations,
identify gaps in care, predict outcomes and apply early interventions, and
actively engage patients in their care. Moreover, they can enable an orga­
nization to  understand its aggregate performance in undertaking disease-
specific plans for multiple patients and better manage contracts and fi nancial
performance.

Additionally, because value-based payment is based on conformance to
chronic disease protocols, providers must have the ability to aggregate and
normalize real-time, accurate, cross-continuum data from disparate sources
illustrating how well the data conform to those protocols. As we know, many
hospitals and health systems do not operate from a position of excess revenue,
and as outcomes become increasingly tied to the reimbursement stream, it

L E A R N I N G A C T I V I T I E S · 133

will become critical that providers can rely on their data and IT tools to detect
and remedy variations in care.

Population health management solutions are intended to complement—
not replace—the traditional EHR. They represent a shift from applications
focused on documenting the patient’s record of care to applications focused
on developing the patient’s plan for health.

KEY TERMS

Accountable care organizations Patient-centered medical home
(ACOs) (PCMH)

Analytics Population health management
Business intelligence (BI) (PHM)
Care management Stratifi cation
Health information exchange (HIE) Telemedicine and telehealth
Patient engagement Value-based care

LEARNING ACTIVITIES

1. Interview a health care executive or CEO in your local community.
To what extent is the organization involved in population health
management? How is that person using health IT to further his or
her PHM initiatives? To what extent does the organization’s health IT
capabilities facilitate PHM? What other capabilities are needed?

2. Investigate the adoption and use of telemedicine and telehealth
in your state. How is it being used? What benefi ts have been
realized? What challenges or obstacles still exist? How important is
telemedicine and telehealth in providing access to care? In improving
quality of care? And in reducing costs?

3. Explore the health IT products on the market that are designed
to facilitate care management. What are their key features
and functions? In what specifi c ways do these tools facilitate
communication among providers and patients and families?

4. Conduct a literature review on the use of social media in health care.
How are consumers using social media to learn more about their
health or health conditions? How are health care organizations using
social media to connect with consumers? Where do you see the future
of social media in health care evolving?

5. Evaluate different models of care within your local community or
state. Did you find any examples of accountable care organizations or

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patient-centered medical homes? Explain. Working as a team, visit or
interview a leader from a site that uses an innovative model of care.
Describe the model, its uses, challenges, and the degree of patient
coordination and integration. How is health IT used to support the
delivery of care and the reporting of outcomes?

6. Explore the extent to which health information exchange is occurring
within your community, region, or state. Who are the key players? To
what extent is information being exchanged across organizations for
patient care purposes? What challenges have they faced? How have
they overcome them, if at all?

7. Visit a health care organization that uses an EHR system and
provides patients access to their information via a patient portal. To
what extent are patients using the portal? For what purposes are they
using them? What are the demographic characteristics of the portal
users and nonusers? What strategies might you employ to promote
greater usage?

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Selection,
Implementation,
Evaluation, and
Management
of Health Care

Information Systems

PART TWO

139

CHAPTER 5

System Acquisition

LEARNING OBJECTIVES

• To be able to explain the process a health care organization
generally goes through in selecting a health care information
system.

• To be able to describe the systems development life cycle and its
four major stages.

• To be able to discuss the various options for acquiring a health
care information system (for example, purchasing, leasing,
contracting with vendor for cloud computing services, or building
a system in-house) and the pros and cons of each option.

• To be able to discuss the purpose and content of a request for
information and request for proposal in the system acquisition
process.

• To gain insight into the problems that may occur during the
system acquisition process.

• To gain an understanding of the health care IT industry and
the resources available for identifying health care IT vendors

141

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and learning about their history, products, services, and
reputation.

• To gain insight into the importance of understanding IT
architecture.

By now you should have an understanding of the various types of health care
information systems and the value they can bring to health care organizations
and the patients they serve. This chapter describes the typical process a health
care organization goes through in acquiring or selecting a new clinical or
administrative application. Acquiring an information system (IS) application
can be an enormous investment for health care organizations. In addition to
the initial cost, there are a host of long-term costs associated with maintaining,
supporting, and enhancing the system. Health care professionals need access
to reliable, complete, and accurate information in order to provide effective and
efficient health care services and to achieve the strategic goals of the organiza­
tion. Selecting the right application, one that meets the organization’s needs, is
a critical step. Too often information systems are acquired without exploring
all options, without evaluating costs and benefits, and without gaining suffi ­
cient input from key constituent user groups. The results can be disastrous.

This chapter describes the people who should be involved, the activities
that should occur, and the questions that should be addressed in acquir­
ing any new information system. The suggested methods are based on the
authors’ years of experience and on countless case studies of system acqui­
sition successes and failures published in the health care literature.

SYSTEM ACQUISITION: A DEFINITION

In this book system acquisition refers to the process that occurs from the
time the decision is made to select a new system (or replace an existing
system) until the time a contract has been negotiated and signed. System
implementation is a separate process described in the next chapter, but both
are part of the systems development life cycle. The actual system selection,
or acquisition, process can take anywhere from a few days to a couple
of years, depending on the organization’s size, structure, complexity, and
needs. Factors such as whether the system is deemed a priority and whether
adequate resources (time, people, and funds) are available can also directly
affect the time and methods used to acquire a new system (Jones, Koppel,
Ridgley, Palen, Wu, & Harrison, 2011).

S Y S T E M S D E V E L O P M E N T L I F E C Y C L E · 143

Prior to arriving at the decision to select a new system, the health care
executive team should engage in a strategic IS planning process in which the
strategic goals of the organization are formulated and the ways in which
information technology (IT) will be employed to aid the organization in
achieving its strategic goals and objectives are discussed. We discuss the
need for aligning IT plans with the strategic goals of the organization and
for determining IT priorities in Chapter Twelve. In this chapter, we assume
that a strategic IT plan exists, IT priorities have been established, the new
system has been adequately budgeted, and the organization is ready to move
forward with the selection process. We also assume that the organization
has conducted a readiness assessment and is well equipped to move forward
with the health IT project or initiative. The AHRQ National Resource Center
for Health IT has available a number of tools publicly available that can be
helpful to health care organizations in assessing their readiness for health
IT projects such as EHR implementations and for ensuring that they have
in place the personnel, technical, and financial resources to embark on
the initiative. These tools can be found at https://healthit.ahrq.gov/health­
it-tools-and-resources. Additionally, the Office of the National Coordinator
for Health Information Technology (ONC) has readiness tools available and
implementation blueprints that serve as excellent resources at https://www
.healthit.gov/providers-professionals/ehr-implementation-steps.

SYSTEMS DEVELOPMENT LIFE CYCLE

No board of directors would recommend building a new health care facility
without an architect’s blueprint and a comprehensive assessment of the orga­
nization’s and the community’s needs and resources. The architect’s blueprint
helps ensure that the new facility has a strong foundation, is well designed,
fosters the provision of high-quality care, and has the potential for growth
and expansion. Similarly, the health care organization needs a blueprint to
aid in the planning, selection, implementation, and support of a new health
care information system. The decision to invest in a health care information
system should be well aligned with the organization’s overall strategic goals
and should be made after careful thought and deliberation. Information
systems are an investment in the organization’s infrastructure, not a one­
time purchase. Health care information systems require not only up-front
costs and resources but also ongoing maintenance, support, upgrades, and
eventually, replacement.

The process an organization generally goes through in planning, select­
ing, implementing, and evaluating a health care information system is known
as the systems development life cycle (SDLC). Although the SDLC is most

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commonly described in the context of software development, the process also
applies when systems are purchased from a vendor or leased through cloud-
based computing services. Cloud computing is a general term that refers to
a broad range of application, software, and hardware services delivered over
the Internet. Regardless of how the system is acquired, most health care
organizations follow a structured process for selecting and implementing a
new computer-based system. The systems development process itself involves
participation from individuals with different backgrounds and areas of exper­
tise. The specific mix of individuals depends on the nature and scope of the
new system.

Many SDLC frameworks exist, some of which employ an incremental
approach, but most have four general phases, or stages: planning and analy­
sis, design, implementation, and support and evaluation (Wager & Lee, 2006)
(see Figure 5.1). Each phase has a number of tasks that need to be performed.
In this chapter we focus on the first two phases; Chapter Six focuses on the
last two.

The SDLC approach assumes that this four-phase life of an IS starts
with a need and ends when the benefits of the system no longer outweigh
its maintenance costs, at which point the life of a new system begins (Oz,
2012). Hence, the entire project is called a life cycle. After the decision has
been made to explore further the need for a new information system, the
feasibility of the system is assessed and the scope of the project defi ned (in
actuality it is at times difficult to tell when this decision making ends and
analysis begins). The primary focus of this planning and analysis phase

Figure 5.1 Systems development life cycle

S Y S T E M S D E V E L O P M E N T L I F E C Y C L E · 145

is on the business problem, or the organization’s strategy, independent of
any technology that can or will be used. During this phase, it is important
to examine current systems and problems in order to identify opportunities
for improvement. The organization should assess the feasibility of the new
system—is it technologically, financially, and operationally feasible? Further­
more, sometimes it is easy to think that implementing a new IS will solve
all information management problems. Rarely, if ever, is this the case. But
by critically evaluating existing systems and workflow processes, the health
care team might find that current problems are rooted in ineffective proce­
dures or lack of sufficient training. Not always is a new system needed or
the answer to a problem.

Once it is clear that a new IS is needed, the next step is to assess the
information needs of users and define the functional requirements: What
functions must the system have to fulfill the need? This process can be very
time-consuming. However, it is vital to solicit widespread participation from
end users during this early stage—to solicit and achieve buy-in. As part of the
needs assessment, it is also helpful to gather, organize, and evaluate informa­
tion about the organization in which the new system is to operate. Through
defining system requirements, the organization specifies what the system
should be able to do and the means by which it will fulfill its stated goals.

Once the team knows what the organization needs, it enters the second
stage, the design phase, when it considers all its options. Will the new system
be designed in-house? Will the organization contract with an outside devel­
oper? Or will the organization purchase a system from a health information
systems vendor or contract with a vendor for cloud-based services? A large
majority of health care organizations purchase a system from a vendor or
at least look first at the systems available on the market. Contracting with
the vendor to host the applications, software, hardware, and infrastructure
via cloud computing is also growing in popularity in health care (Griebel
et al., 2015). System design is the evaluation of alternative solutions to address
the business problem. It is generally in this phase that all alternatives are
considered, a cost-benefi t analysis is done, a system is selected, and vendor
negotiations are fi nalized.

After the contract has been finalized or the system has been chosen, the
third phase, implementation, begins. The implementation phase requires
significant allocation of resources in completing tasks, such as conducting
work-flow and process analyses, installing the new system, testing the
system, training staff members, converting data, and preparing the organi­
zation and staff members for the go-live of the new system. Finally, once
the system is put into operation, the support and evaluation phase begins.
It is common to underestimate the number of staff and resources needed to

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effectively keep new and existing information systems functioning properly.
No matter how much time and energy were spent on the design and build
of the application, you can count on the fact that changes will need to be
made, glitches fixed, and upgrades installed. Likewise, most mission-critical
systems need to be functioning 99.99 percent of the time—that is, with
little downtime. Sufficient resources (people, technology, infrastructure, and
upgrades) need to be allocated to maintain and support the new system.
Moreover, maintaining and supporting the new system is not enough.
Health care executives and boards often want to know the value of the IT
investment, thus the degree to which the new system has achieved its goals
and objectives should be assessed. Eventually, the system will be replaced
and the SDLC process begins again.

With this general explanation of the SDLC established, we begin by
focusing on the first two phases—the planning and analysis phase and the
design phase. Together they constitute what we refer to as the system acqui­
sition process.

SYSTEM ACQUISITION PROCESS

To gain an understanding of and appreciation for the activities that occur
during the system acquisition process, we will follow a health care facility
through the selection process for a new information system—specifi cally, an
electronic health record (EHR) system. In this case the organization, which
we will call Valley Practice, is a multiphysician primary care practice.

What process should the practice use to select the EHR? Should it pur­
chase a system from a vendor, contract with a vendor for cloud-based ser­
vices, or seek the assistance of a system developer? Who should lead the
effort? Who should be involved in the process? What EHR products are
available on the market? How reputable are the vendors who develop these
products? These are just a few of the many questions that should be asked
in selecting a new IS.

Although the time and resources needed to select an EHR (or any health
care information system) may vary considerably from one setting to another,
some fundamental issues should be addressed in any system acquisition
initiative. The sections that follow the case study describe in more detail
the major activities that should occur (see Exhibit 5.1), relating them to the
multiphysician practice scenario. We assume that the practice wishes to
purchase (rather than develop) an EHR system. However, we briefl y describe
other options and point out how the process may differ when the EHR
acquisition process occurs in a larger health care setting, such as integrated
health systems.

Exhibit 5.1 Overview of system acquisition process

• Establish project steering committee and appoint project
manager.

• Define project objectives and scope of analysis.

• Screen the marketplace and review vendor profi les.

• Determine system goals.

• Determine and prioritize system requirements.

• Develop and distribute a request for proposal (RFP) or a request
for information (RFI).

• Explore other options for acquiring system (e.g., leasing, hiring
system designer, building in-house).

• Evaluate vendor proposals.

o Develop evaluation criteria.

o Hold vendor demonstrations.

o Make site visits and check references.

o Prepare vendor analysis.

• Conduct cost-benefi t analysis.

• Prepare summary report and recommendations.

• Conduct contract negotiations.

S Y S T E M A C Q U I S I T I O N P R O C E S S · 147

Establish a Project Steering Committee

One of the first steps in any major project such as an EHR acquisition effort
is to create a project steering committee. This committee’s primary function
is to plan, organize, coordinate, and manage all aspects of the acquisition
process. Appointing a project manager with strong communication skills,
organizational skills, and leadership abilities is critical to the project. In our
Valley Practice case, the project manager was a physician partner. In larger
health care organizations such as hospitals, it would likely be a CIO involved
in the effort and that person might also be asked to lead it.

Increasingly, clinicians such as physicians and nurses with training
in informatics are being called on to lead clinical system acquisition and
implementation projects. Known as chief medical informatics offi cers (CMIOs)
or chief nursing informatics offi cers (CNIOs), these individuals bring to the

148 · C H A P T E R 5 : S Y S T E M A C Q U I S I T I O N

CASE STUDY

Replacing an EHR System

Valley Practice provides patient care services at three locations, all within
a fifteen-mile radius, and serves nearly one hundred thousand patients.
Valley Practice is owned and operated by seven physicians; each physi­
cian has an equal partnership. In addition to the physicians, the practice
employs nine nurses, fifteen support staff members, a business offi cer
manager, an accountant, and a chief executive offi cer (CEO).

During a two-day strategic planning session, the physicians and man­
agement team created a mission, vision, and set of strategic goals for Valley
Practice. The mission of the facility is to serve as the primary care “medical
home” of individuals within the community, regardless of the patients’
ability to pay. Valley Practice wishes to be recognized as a “high-tech,
high-touch” practice that provides high-quality, cost- effective patient care
using evidence-based standards of care. Consistent with its mission, one
of the practice’s strategic goals is to replace its legacy EHR with an EHR
system that adheres to industry standards for security and interoperability
and that fosters patient engagement, with the long-term goal of supporting
health fi tness applications.

Dr. John Marcus, the lead physician at Valley Practice, asked Dr. Julie
Brown, the newest partner in the group, to lead the EHR project initiative.
Dr. Brown joined the practice two years ago after completing an internal
medicine residency at an academic medical center that had a fully inte­
grated EHR system available in the hospital and its ambulatory care clinics.
Of all the physicians at Valley Practice, Dr. Brown has had the most expe­
rience using EHR applications via portable devices. She has been a vocal
advocate for migrating to a new EHR and believes it is essential to enabling
the facility to achieve its strategic goals.

Dr. Brown agreed to chair the project steering committee. She invited other
key individuals to serve on the committee, including Dr. Renee Ward, a senior
physician in the practice; Mr. James Rowls, the CEO; Ms. Mary Matthews,
RN, a nurse; and Ms. Sandy Raymond, the business offi cer manager.

After the project steering committee was formed, Dr. Marcus met with
the committee to outline its charge and deliverables. Dr. Marcus expressed
his appreciation to Dr. Brown and all of the members of the committee
for their willingness to participate in this important initiative. He assured
them that they had his full support and the support of the entire physician
team.

S Y S T E M A C Q U I S I T I O N P R O C E S S · 149

Dr. Marcus reviewed with the committee the mission, vision, and stra­
tegic goals of the practice as well as the committee’s charge. The committee
was asked to fully investigate and recommend the top three EHR products
available in the vendor community. He stressed his desire that the com­
mittee members would focus on EHR vendors that have experience and a
solid track record in implementing systems in physician practices similar to
theirs and that have Office of the National Coordinator for Health Informa­
tion Technology (ONC)–certified EHR products. He is intrigued with the idea
of cloud-based EHR systems provided they can ensure safety, security, and
confidentiality of data; are reliable and scalable; and have the capacity to
convert data easily from the current system into the new system. The vendor
must also be willing to sign a business associates’ agreement ensuring com­
pliance with HIPAA security and privacy regulations.

Dr. Marcus is also interested in exploring what opportunities are
available for health information exchange within the region. He envi­
sions that the practice will likely partner with specialists, hospitals, and
other key stakeholders in the community to provide coordinated care
across the continuum under value-based reimbursement models. Under
the leadership of Dr. Brown, the members of the project steering com­
mittee established five project goals and the methods they would use to
guide their activities. Ms. Moore, the consultant, assisted them in clearly
defining these goals and discussing the various options for moving
forward. They agreed to consider EHR products only from those vendors
that had five or more years of experience in the industry and had a solid
track record of implementations (which they defined as having done
twenty-five or more). Dr. Ward, Mr. Rowls, and Ms. Matthews assumed
leadership roles in verifying and prioritizing the requirements expressed
by the various user groups.

The five project goals were based on Valley Practice’s strategic goals.
These project goals were circulated for discussion and approved by the
CEO and the physician partners. Once the goals were agreed on, the project
steering committee appointed a small task group of committee members to
carry out the process of defining system functionality and requirements.
Because staff time was limited, the task group conducted three separate
focus groups during the lunch period—one with the nurses, one with the
support staff members, and a third with the physicians. Ms. Moore, the

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consultant, conducted the focus groups, using a semi-structured nominal
group technique.

Concurrently with the requirements definition phase of the project,
Mr. Rowls and Dr. Brown, with assistance from Ms. Moore, screened the
EHR vendor marketplace. They reviewed the literature, consulted with
colleagues in the state medical association, and surveyed practices in
the state that they knew used state-of-the-art EHR systems. Mr. Rowls
made a few phone calls to chief information offi cers (CIOs) in surround­
ing hospitals who had experience with ambulatory care EHR to get their
advice. This initial screening resulted in the identification of eight EHR
vendors whose products and services seemed to meet Valley Practice’s
needs.

Given the fairly manageable number of vendors, Ms. Moore suggested
that the project steering committee use a short-form RFP. This form had
been developed by her consulting firm and had been used successfully

project a clinical perspective as well as an understanding of IT and informa­
tion management processes. (The roles of CMIOs and CNIOs are described
more fully in Chapter Eight.) Regardless of the discipline or background of
the project manager (for example, IT, clinical, or administrative), he or she
should bring to the project passion, interest, time, strong interpersonal and
communication skills, and project management skills and should be someone
who is well respected by the organization’s leadership team and who has the
political clout to lead the effort effectively.

Pulling together a strong team of individuals to serve on the project
steering committee is also important. These individuals should include rep­
resentatives from key constituent groups in the practice. At Valley Practice, a
physician partner, a nurse, the business officer manager, and the CEO agreed
to serve on the committee. Gaining project buy-in from the various user
groups should begin early. This is a key reason for inviting representatives
from key constituent groups to serve on the project steering committee. They
should be individuals who will use the EHR system directly or whose jobs
will be affected by it.

Consideration should also be given to the size of the committee; typically,
having five to six members is ideal. In a large facility, however, this may not
be possible. The committee for a hospital or health systems might have fi fteen
to twenty members, with representatives from key clinical areas such as
laboratory medicine, pharmacy, and radiology in addition to representatives
from the administrative, IT, nursing, and medical staffs.

S Y S T E M A C Q U I S I T I O N P R O C E S S · 151

by other physician practices to identify top contenders. The short-form
RFPs were sent to the eight vendors; six responded. Each of these six pre­
sented an initial demonstration of its EHR system on site. Following the
demonstrations, the practice staff members completed evaluation forms
and ranked the various vendors. After reviewing the completed RFPs and
getting feedback on the vendor presentations, the committee determined
that three vendors had risen to the top of the list.

Dr. Brown and Dr. Ward visited four physician practices that used
EHR systems from these three finalists. Mr. Rowls checked references
and prepared the final vendor analysis. A detailed cost-benefi t analysis
was conducted, and the three vendors were ranked. All three vendors, in
rank order, were presented in the fi nal report given to Dr. Marcus and the
other physician partners. Dr. Marcus, Dr. Brown, and Mr. Rowls spent four
weeks negotiating a contract with the top contender. It was fi nalized and
approved after legal review and after all the partners agreed to it.

It is important to have someone knowledgeable about IT serving on the
project steering committee. This may be a physician, a nurse, the CEO, or
an outside consultant. In a physician group practice, having an in-house IT
professional is not always possible. The committee chair might look internally
to see if someone has the requisite IT knowledge, skills, interests, and also
the time to devote to the project, but the chair also might look externally for
a health care IT professional who might serve in a consultative role and help
the committee direct its activities appropriately.

Define Project Objectives and Scope of Analysis

Once the project steering committee has been established, its fi rst order
of business is to clarify the charge to the committee and to defi ne project
goals. The charge describes the scope and nature of the committee’s activ­
ities. The charge usually comes from senior leadership or a lead physician
in the practice. Project goals should also be established and communicated
in well-defined, measurable terms. What does the committee expect to
achieve? What process will be used to ensure the committee’s success? How
will milestones be acknowledged? How will the committee communicate
progress and resolve problems? What resources (such as time, personnel,
and travel expenses) will the committee need to carry out its charge? What
method will be used to evaluate system options? Will the committee con­
sider contracting with a system developer to build a system or outsourcing

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the system to an application service provider? Or is the committee only
considering systems available for purchase from a health care information
systems vendor?

Once project goals are formulated, they can guide the committee’s activ­
ities and also clarify the resources needed and the likely completion date for
the project. Here are some examples of typical project goals:

• Assess the practice’s information management needs and establish
goals and objectives for the new system based on these needs.

• Conduct a review of the literature on EHR products and the market
resources for these products.

• Investigate the top-ten EHR system products for the ambulatory care
arena.

• Visit two to four health care organizations similar to ours that have
implemented an EHR system.

• Schedule vendor demonstrations for times when physicians, nurses,
and others can observe and evaluate without interruptions.

As part of the goal-setting process, the committee should determine the
extent to which various options will be explored. For example, the Valley
Practice project steering committee decided at the onset that it was going to
consider only EHR products available in the vendor community and ONC-
certified. Users can be assured that certified EHR products meet certain
standards for content, functionality, and interoperability.

The committee further stipulated that it would consider only vendors with
experience (for example, five or more years in the industry) and those with a
solid track record of system installations (for example, twenty-five or more
installations). The committee members felt the practice should contract with
a system developer only if they were unable to find a suitable product from
the vendor community—their rationale being that the practice wanted to be
known as high-tech, high-touch. They also believed it was important to
invest in IT personnel who could customize the application to meet practice
needs and who would be able to assist the practice in achieving project and
practice goals.

Screen the Marketplace and Review Vendor Profi les

Concurrently with the establishment of project goals, the project steering
committee should conduct its first, cursory review of the EHR marketplace
and begin investigating vendor profiles. Many resources are available to

S Y S T E M A C Q U I S I T I O N P R O C E S S · 153

aid the committee in this effort. For example, the Valley Practice commit­
tee might obtain copies of recent market analysis reports—from research
firms such as Gartner or KLAS—listing and describing the vendors that
provide EHR systems for ambulatory care facilities. The committee might
also attend trade shows at conferences of professional associations such
as the Healthcare Information and Management Systems Society (HIMSS)
and the American Medical Informatics Association (AMIA). (Appendix A
provides an overview of the health care IT industry and describes a variety
of resources available to health care organizations interested in learning
about health care IT products, such as EHR systems, available in the vendor
community.)

Determine System Goals

Besides identifying project goals, the project steering committee should defi ne
system goals. System goals can be derived by answering questions such as,
What does the organization hope to accomplish by implementing an EHR
system? What is it looking for in a system? If the organization intends to
transform existing care processes, can the system support the new processes?
Such goals often emerge during the initial strategic planning process when
the decision is made to move forward with the selection of the new system.
At this point, however, the committee should state its goals and needs for
a new EHR system in clearly defi ned, specific, and measurable terms. For
example, a system goal such as “select a new EHR system” is very broad and
not specific. Here are some examples of specific and measurable goals for a
physician practice.

Our EHR system should do the following:

• Enable the practice to provide service to patients using evidence-based
standards of care.

• Aid the practice in monitoring the quality and costs of care provided
to the patients served.

• Provide clinicians with access to accurate, complete, relevant patient
information, on-site and remotely.

• Improve staff member effi ciency and effectiveness.

• More fully engage patients in their own care by providing patients
with ready access to their test results, immunization records, patient
education materials, and other aids.

• Enable the practice to manage chronic disease patient care more
effectively.

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These are just a few of the types of system goals the project steering com­
mittee might establish as it investigates a new EHR for the organization. The
system goals should be aligned with the strategic goals of the organization
and should serve as measures of success throughout the system acquisition
process.

Determine and Prioritize System Requirements

Once the goals of the new system have been established, the project steering
committee should begin to determine system requirements. These require­
ments may address everything from what information should be available
to the provider at the point of care to how the information will be secured
to what type of response time is expected. The committee may use any
of a variety of ways to identify system requirements. One approach is to
have a subgroup of the committee conduct focus-group sessions or small-
group interviews with the various user groups (physicians, nurses, billing
personnel, and support staff members). A second approach is to develop
and administer a written or an electronic survey, customized for each user
group, asking individuals to identify their information needs in light of their
job role or function. A third is to assign a representative from each specifi c
area to obtain input from users in that area. For example, the nurse on the
Valley Practice project steering committee might interview the other nurses;
the business office manager might interview the support staff members.
System requirements may also emerge as the committee examines templates
provided by consultants or peer institutions, looks at vendor demonstrations
and sales material, or considers new regulatory requirements the organiza­
tion must meet.

The committee may also use a combination of these or other approaches.
At times, however, users do not know what they want or will need. Hence,
it can be extremely helpful to hold product demonstrations, meet with con­
sultants, or visit sites already using EHR systems so that those who will
use or be affected by the EHR can see and hear what is possible. Whatever
methods are chosen to seek users’ information system needs, the end result
should be a list of requirements and specifications that can be prioritized or
ranked. This ranking should directly reflect the specific strategic goals and
circumstances of the organization.

The system requirements and priorities will eventually be shared with
vendors or the system developer; therefore, it is important that they be
clearly defined and presented in an organized, easy-to-understand format.
For example, it may be helpful to organize the requirements into catego­
ries such as software (system functionality, software upgrades); technical

S Y S T E M A C Q U I S I T I O N P R O C E S S · 155

infrastructure (hardware requirements, network specifi cations, backup,
disaster recovery, security); and training and support (initial and ongoing
training, technical support). These requirements will eventually become a
major component of the RFP submitted to vendors or other third parties
(discussed next).

Develop and Distribute the RFP or RFI

Once the organization has defined its system requirements, the next step
in the acquisition process is to package these requirements into a structure
that a third party can respond to, whether that third party be a development
partner or a health information systems vendor. Many health care organiza­
tions package the requirements into a request for proposal. The RFP provides
the vendor with a comprehensive list of system requirements, features, and
functions and asks the vendor to indicate whether its product or service
meets each need. Vendors responding to an RFP are also generally required
to submit a detailed and binding price quotation for the applications and
services being sought.

RFPs tend to be highly detailed and are therefore time-consuming and
costly to develop and complete. However, they provide the health care
organization and each vendor with a comprehensive view of the system
needed. Health care IT consultants can be extremely resourceful in assist­
ing the organization with developing and packaging the RFP. An RFP for
a major health care information system acquisition generally contains the
following information (sections marked with an asterisk [*] are completed
by the vendor; the other sections are completed by the organization issuing
the RFP):

• Instructions for vendors:

o Proposal deadline and contact information: where and when the
RFP is due; whom to contact with questions

o Confi dentiality statement and instructions: a statement that the RFP
and the responses provided by the vendor are confi dential and are
proprietary information

o Specifi c instructions for completing the RFP and any stipulations
with which the vendor must comply in order to be considered

• Organizational objectives: type of system or application being sought;
information management needs and plans

• Background of the organization:

156 · C H A P T E R 5 : S Y S T E M A C Q U I S I T I O N

o Overview of the facility: size, types of patient services, patient
volume, staff composition, strategic goals of organization

o Application and technical inventory: current systems in use,
hardware, software, network infrastructure

• System goals and requirements: goals for the system and functional
requirements (may be categorized as mandatory or desirable and
listed in priority order). Typically this section includes application,
technical, and integration requirements. Increasingly, health care
providers are interested in assessing and testing system usability.
Incorporating scripted scenarios in the requirements section of the
RFP that are based on existing workfl ow and business processes can
provide meaningful information during the selection process (Corrao,
Robinson, Swiernik, & Naeim, 2010; Eisenstein, Jurwishin, Kushniruk,
& Nahm, 2011; IOM, 2011).

• Vendor qualifi cations: *general background of vendor, experience,
number of installations, financial stability, list of current clients,
standard contract, and implementation plan

• Proposed solutions: *how vendor believes its product meets the goals
and needs of the health care organization. Vendor may include case
studies, results from system analysis projects, and other evidence of
the benefi ts of its proposed solution.

• Criteria for evaluating proposals: how the health care organization
will make its final decisions on product selection

• General contractual requirements: *warranties, payment schedule,
penalties for failure to meet schedules specifi ed in contract, vendor
responsibilities, and so forth

• Pricing and support: *quote on cost of system, using standardized
terms and forms

The RFP may become the basis for a legally binding contract or obligation
between the vendor and the solicitor, so it is important for both parties to
carefully consider the wording of questions and the corresponding responses
(AHIMA, 2007).

RFPs are not the only means by which to solicit information from vendors.
A second approach that is often used is the request for information. An RFI
is less formal, considerably shorter than an RFP, and less time-consuming to
develop. It is often used as part of the fact-finding process to obtain basic infor­
mation on the vendor’s background, product descriptions, and service capa­
bilities. Some health care organizations send out an RFI before distributing

S Y S T E M A C Q U I S I T I O N P R O C E S S · 157

the RFP in order to screen out vendors whose products or services are not
consistent with the organization’s needs or to narrow the field of vendors to
a manageable number. The RFI can serve as a tool in gathering background
information on vendors’ products and services and providing the project steer­
ing committee with a better sense of the health IT marketplace. How does
one decide whether to use an RFP, an RFI, both, or neither during the system
acquisition process? Several factors should be considered. Although time-
consuming to develop, the RFP is useful in forcing a health care organization
to define its system goals and requirements and prioritize its needs. The RFP
also creates a structure for objectively evaluating vendor responses and pro­
vides a record of documentation throughout the acquisition process. System
acquisition can be a highly political process; by using an RFP the organization
can introduce a higher degree of objectivity into that process. RFPs are also
useful data collection tools when the technology being selected is established
and fully developed, when there is little variability between vendor products
and services, when the organization has the time to fully evaluate all options,
and when the organization needs strong contract protection from the selected
vendor (DeLuca & Enmark, 2002). However, not all vendors may wish to
submit a response to an RFI or RFP because of costs or suitability.

There are also drawbacks to RFPs. In addition to taking considerable time
to develop and review, they can become cumbersome and so detail oriented
that they lose their effectiveness. For instance, it is not unusual to receive
three binders full of product and service information from one vendor. If ten
vendors respond to an RFP (about five is ideal), the project steering committee
may be overwhelmed and find it difficult to wade through and differentiate
among vendor responses. Having too much information to summarize can be
as crippling to a committee in its deliberations as having too little.

Therefore a scaled-back RFP or an RFI might be a desirable alternative.
An RFI might be used when the health care organization is considering only a
small group of vendors or products or when it is still in the exploratory stages
and has not yet established its requirements. Some facilities use an even less
formal process consisting primarily of site visits and system demonstrations.

Regardless of the tool(s) used, it is important for the health care orga­
nization to provide sufficient detail about its current structure, strategic IT
goals, and future plans so that the vendor can respond appropriately to its
needs. Additionally, the RFP or RFI (or variation of either) should result
in enough specific detail that the organization gets a good sense of the
vendor—its services, history, vision, stability in the marketplace, and system
or product functionality. The organization should be able to easily screen
out vendors whose products are undeveloped or not yet fully tested (DeLuca
& Enmark, 2002).

158 · C H A P T E R 5 : S Y S T E M A C Q U I S I T I O N

Explore Other Acquisition Options

In our Valley Practice case, the physicians and staff members opted to acquire
an EHR system from the vendor community. Organizations such as Valley
Practice often turn to the market for products that they will run on their own
IT infrastructure. But there are times when they do not go to the market—
they choose to leverage someone else’s infrastructure (by contracting with an
application service provider or vendor who offers cloud computing services)
or they build the application (by contracting with a system developer or using
in-house staff members).

Option to Contract with Vendor for Cloud

Computing Services

In recent years, there has been a wider availability of high-speed or broadband
Internet connections, more sophisticated vendor solutions, and a growing
number of options for hosting software, hardware, and infrastructure via
the Internet. These services are generally referred to as cloud computing,
a general term that refers to the applications delivered as services over the
Internet and the hardware and software in the data centers that provide those
services. Vendors and companies may use different terms to describe cloud-
based services. Common options include application service provider (ASP),
software as a service (SaaS), infrastructure as a service, and platform as a
service. The scope of services and payment methods also can vary consider­
ably. However, cloud computing options generally require less upfront capital
expenses, fewer IT staff members and resources, and greater scalability and
access to analytic capabilities (Armbrust et al., 2010). Essentially the health
care provider contracts with the vendor to host and maintain the clinical or
administrative application and related hardware; the health care organization
or provider simply accesses the system remotely over a network connection and
pays the monthly or negotiated fees.

Why might a health care organization consider contracting with a vendor
in a cloud-based service arrangement rather than purchasing an EHR system
(or other application) from a vendor? There are several reasons. First, the facil­
ity may not have the IT staff members needed to run or support the desired
system. Hiring qualified personnel at the salaries they demand may be dif­
ficult, and retaining them may be equally challenging. Second, cloud-based
options enable health care organizations to use clinical or administrative
applications with fewer up-front costs and less capital. For a small physician
practice, these financial arrangements can be particularly appealing. Because

S Y S T E M A C Q U I S I T I O N P R O C E S S · 159

many vendors offer cloud-based services on fi xed monthly fees or fees based
on use, organizations are better able to predict costs. Third, by contracting
with a vendor to host, manage, or support IT, the health care organization
can focus on its core business and not get bogged down in IT support issues,
although it may still have to deal with issues of system enhancements, user
needs, and the selection of new systems. Other advantages are rapid deploy­
ment and 24/7 technical support. They also offer scalability and fl exibility,
so as the practice or organization grows or shrinks in size or volume, they
pay only for the services used. Other benefits include upgrades that can be
made once and applied across a network of users instantaneously; users can
access services from any standardized device no matter their location; and a
cloud-based network can easily accommodate changes in use (increase and
decrease during certain periods).

However, cloud computing services have some disadvantages and limita­
tions that the health care organization should consider in its deliberations.
Although rapid deployment of the application can be a tremendous advantage
to an organization, the downside is the fact that the application will likely be
a standard, off-the-shelf product, with little if any customization. This means
that the organization has to adapt or mold its operations to the application
rather than tailoring the application to meet the operational needs of the
organization. A second drawback deals with technical support. Although
technical support is generally available, it is unrealistic to think that the
vendor’s support personnel will have intimate knowledge of the organiza­
tion and its operations. Frustrations can mount when one lacks in-house IT
technical staff members when and where they are needed. Third, health care
providers have long been concerned about data ownership, security, and
privacy—worries that increase when another organization hosts their clinical
data and applications. How the vendor will secure data and maintain patient
privacy should be clearly specified in the contract. Likewise, to minimize
downtime, the vendor should have clear plans for backing up data, preventing
disasters, and recovering data.

As the industry matures, we will likely see different variations and greater
choices among organizations offering cloud-based services. A recent review of
the literature found cloud computing used in six primary domains: (1) telemed­
icine and teleconsultation, (2) medical imaging, (3) public health and patients’
self-management, (4) clinical information systems, (5) therapy, and (6) second­
ary use of data (Griebel et al., 2015). Additionally, cloud computing is designed
to support cooperation, care coordination, and information sharing.

The health care executive considering a move to cloud computing should
carefully consider the type of application moving to the cloud (clinical,