Policy Adoption by State Governments: an Event History Analysis of Factors Influencing States to Enact Inpatient Health Care Transparency Laws Lisa Jean Eaton

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2013 Policy Adoption by State Governments: An Event History Analysis of Factors Influencing States to Enact Inpatient Health Care Transparency Laws Lisa Jean Eaton

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COLLEGE OF SOCIAL SCIENCES

POLICY ADOPTION BY STATE GOVERNMENTS: AN EVENT HISTORY ANALYSIS

OF FACTORS INFLUENCING STATES TO ENACT

INPATIENT HEALTH CARE TRANSPARENCY LAWS

LISA JEAN EATON

A Dissertation submitted to the Reubin O’ D. Askew School of Public Administration and Policy in partial fulfillment of the requirements for the degree of Doctor of Philosophy

Degree Awarded: Spring Semester, 2013

Lisa Jean Eaton defended this dissertation on March 21, 2013. The members of the supervisory committee were:

Frances Stokes Berry Professor Directing Dissertation

Carol Weissert University Representative

Lance deHaven-Smith Committee Member

Keon-Hyung Lee Committee Member

The Graduate School has verified and approved the above-named committee members, and certifies that the dissertation has been approved in accordance with university requirements.

I dedicate this work to my husband, Chris, who made it possible through his patience, sacrifice, humor, and support. I also dedicate it to my parents, Thomas and Sandra Thieman, who inspired me to start this endeavor and instilled the determination within me to see it through to its fruition. I also dedicate this to my brother, Tim Thieman; my aunt, Jill Clark; and other family and friends for their continuous support and encouragement. Additionally, I dedicate this to my children, Lindsay and Jocelyn, whose zest for life and peals of laughter energize me. Finally, I dedicate this to the teachers and mentors who have influenced me over the years.

iii ACKNOWLEDGEMENTS

I would like to express my sincere gratitude to the members of my dissertation committee: Frances Stokes Berry, Carol Weissert, Lance deHaven-Smith, and Keon- Hyung Lee for their willingness to serve on my committee and for their constructive suggestions and comments. I am especially thankful for the guidance and unwavering support provided by my major professor, Frances Stokes Berry. Additionally, I would like to acknowledge the Justice Research Center for providing assistance in building some of the preliminary datasets for this study.

iv TABLE OF CONTENTS

List of Tables ...... vii

Abbreviations ...... ix

Abstract ...... x

1. INTRODUCTION ...... 1 Identification of the Problem ...... 1 Research Question ...... 2 Significance of the Research ...... 4 Preview of the Chapters ...... 5

2. THEORETICAL FRAMEWORK...... 7 Introduction ...... 7 Brief History of Public Reporting, Public Health Care Reporting, and Health Care Transparency Laws ...... 7 Review of Policy Innovation and Diffusion in the States ...... 19 Introduction to Internal Determinants and Diffusion Models ...... 20 The Unified Model of State Policy Innovation ...... 21 States’ Adoption of Health Care Innovations in the 1990s – 2000s ...... 22

3. RESEARCH DESIGN ...... 23 Introduction ...... 23 Research Question ...... 23 The Unified Model of State Policy Innovation ...... 23 The Study Hypotheses ...... 25 The Description of the Variables Used in the Model, the Data Sources, and the Dataset ...... 34 Event History Analysis ...... 43 The Statistical Model ...... 48

4. DATA ANALYSIS AND RESULTS ...... 50 Introduction ...... 50 Descriptive Information about the Enactment of Inpatient Health Care Transparency Laws by States ...... 50 Descriptive Statistics ...... 53 Multicollinearity Assessment of the Data ...... 55 Model Assessment ...... 57 Results of the Statistical Analysis and Explanation by Hypothesis ...... 73

v 5. NAHDO: A CASE STUDY ...... 81 Introduction ...... 81 Diffusion Strategies and Practical Politics of Professional Associations: A Focused Review of Literature ...... 82 National Association of Health Data Organizations (NAHDO): A Descriptive History ...... 85 Qualitative Content Analysis ...... 91 Case Study Telephone Interviews ...... 92 Discussion ...... 98

6. CONCLUSION ...... 100 Introduction ...... 100 Overview ...... 100 Implications for Innovation and Diffusion Theory ...... 102 Study Limitations and Future Research ...... 104 Conclusion ...... 106

APPENDICES ...... 108

A: Human Subjects Committee Approval Letter ...... 108

B: Introduction Email for Case Study Telephone Interviews ...... 109

C: Informed Consent Form for Case Study Telephone Interviews ...... 110

D: Discussion Guide for Case Study Telephone Interviews ...... 111

E: Case Study Telephone Interviewee Responses ...... 112

REFERENCES ...... 118

BIOGRAPHICAL SKETCH ...... 128

vi LIST OF TABLES

1. Institute of Medicine’s “10 Simple Rules for the 21st Century Health Care System” ...... 10

2. Mandated versus Voluntary Systems: Strengths and Weaknesses ...... 12

3. F.S. Berry & W.D. Berry’s (1999) Unified Model of State Policy Innovation: Definition of the Equation Terms ...... 24

4. Unified Model of Inpatient Health Care Transparency Law Enactments with Study Hypotheses ...... 33

5. States and the “Year of Inpatient Health Care Transparency Law Enactment” (1971-2006), presented Alphabetically ...... 35

6. States and the “Year of Inpatient Health Care Transparency Law Enactment” (1971-2006), presented Chronologically ...... 35

7. The 48 Continental U.S. States and Their “Neighbors” ...... 41

8. Variable Name, Variable Description, Hypothesis Number, and Data Sources ...... 44

9. Year State Enacted an Inpatient Health Care Transparency Law by Decade during the Study (1971-2006), accompanied by the Number of Previous Enactments by Neighbors of Inpatient Health Care Transparency Laws and the States’ Total Number of Neighbors ...... 52

10. States that Did Not Enact Inpatient Health Care Transparency Laws during the Study (1971-2006), accompanied by the States’ Total Number of Neighbors ...... 53

11. Descriptive Statistics for Complete Dataset Used in the Event History Analysis 53

12. Correlation Matrix of the Data (1971-2006) ...... 56

13. Tolerance and Variance Inflation Factor (VIF) ...... 57

14. Condition Index and Variance Proportions ...... 58

15. Case Summary for Model 3: Using PROFASSN1 without IOMRPT ...... 61

vii 16. Omnibus Tests of Model Coefficients, Model 3, Block 0 ...... 62

17. Omnibus Tests of Model Coefficients, Model 3, Block 1 ...... 62

18. Output for Model 3: Using PROFASSN1 without IOMRPT ...... 63

19. Case Summary for Model 4: Using PROFASSN2 without IOMRPT ...... 64

20. Omnibus Tests of Model Coefficients, Model 4, Block 0 ...... 65

21. Omnibus Tests of Model Coefficients, Model 4, Block 1 ...... 65

22. Output for Model 4: Using PROFASSN2 without IOMRPT ...... 65

23. Correlations Output for 5 Distinct Years of Professional Association Data (1989, 1995, 1998, 2002, 2006) ...... 68

24. Case Summary for Model 5: Without PROFASSN1, PROFASSN2, and IOMRPT ...... 69

25. Omnibus Tests of Model Coefficients, Model 5, Block 0 ...... 69

26. Omnibus Tests of Model Coefficients, Model 5, Block 1 ...... 70

27. Output for Model 5: Without PROFASSN1, PROFASSN2, and IOMRPT ...... 70

28. Covariate Means for Model 5: Without PROFASSN1, PROFASSN2, and IOMRPT ...... 70

29. Comparison of Output from Model 3, Model 4, and Model 5 ...... 72

30. Timeline of NAHDO’s Primary Data used to Educate its Members about Health Care Transparency Laws (1998 through 2006) ...... 91

viii ABBREVIATIONS

Acronym Acronym Defined AFDC Aid to Families with Dependent Children AHA American Hospital Association AHRQ Agency for Healthcare Research and Quality AHRQ IQIs Agency for Healthcare Research Inpatient Quality Indicators AHRQ PSIs Agency for Healthcare Research Patient Safety Indicators AMA American Medical Association CABG Coronary Artery Bypass Graft surgery CHIP Children’s Health Insurance Program CMS Centers for Medicare and Medicaid Services EHA Event history analysis HCFA Health Care Financing Administration HCUP Healthcare Cost and Utilization Project HCUPNet An online interactive query system based on Healthcare Cost and Utilization Project (HCUP) data. HHS U.S. Department of Health and Human Services HIPAA Health Insurance Portability and Accountability Act HMO Health Maintenance Organization IOM Institute of Medicine JCAHO Joint Commission on Accreditation of Health Organizations NAHDO National Association of Health Data Organizations NCQA National Committee for Quality Assurance NCQA HEDIS National Committee for Quality Assurance Healthcare Effectiveness Data and Information Set RAND The RAND Corporation; The RAND name originated as a contraction of “R”esearch “and D”evelopment. STEEEP The IOM’s Quality Chasm report’s six aims: Care should be safe, timely, effective, efficient, equitable, and patient-centered. UB Uniform Bill, e.g. UB92, UB04; the national hospital bill or claim that physicians, other health care professionals, and facilities are required to complete when submitting a claim for payment. UTAH-IBIS-PH Utah’s Indicator-Based Information System for Public Health Data Resources VHI Virginia Health Information

ix ABSTRACT

This dissertation provides an analysis and evaluation of factors influencing states to enact inpatient health care transparency laws between 1971 and 2006 inclusive, using event history analysis. The primary research question investigates “What factors influence a state legislature to enact a health care transparency law?” To narrow the scope of study, I focus on factors influencing states to enact health care transparency laws to collect and publicly report inpatient data. The Unified Model of State Policy Innovation, developed by F.S. Berry and W.D. Berry (1990, 1999), provides the framework for the study hypotheses and the analysis of inpatient health care transparency law enactments by states. The Unified Model of State Policy Innovation posits a unified explanation for state policy adoptions. The model unifies the internal determinants and regional diffusion approaches of analysis for state policy adoption. This study tests eight hypotheses using event history analysis (EHA). EHA is an analytical technique that allows for the testing of a state government innovation theory that incorporates internal determinants and regional influences on state policy adoption. Although there are numerous methods to conduct event history analysis, this study uses the Cox proportional hazards model (also known as Cox regression). Cox regression is a popular method for studying time-to-event data for policy adoption and diffusion studies. This study’s quantitative analysis provides support for legislative ideology and unified party control of state government as factors influencing inpatient health care transparency law enactments by states. Additionally, the health care crisis and neighbors variables were statistically significant, but in an opposite direction than predicted. The findings of this research suggest that state adopters of an inpatient health care transparency law are more likely to enact an inpatient health care transparency law when liberalism is increasing in state government and when unified political party control is increasing in the state legislature.

x To generate new insights into the enactment of inpatient health care transparency laws, I conduct a case study of a national health care data professional association using several techniques, including telephone interviews. The qualitative analysis provides support for professional associations and policy champions as diffusion agents for inpatient health care transparency law enactments by states. This dissertation supports variables traditionally used in policy adoption research including legislative ideology and unified political party control in state government. However, it will be interesting to see whether internal determinants such as professional associations gain traction over the traditional regional diffusion influences such as states sharing borders as factors influencing state policy adoption. Meanwhile, as evidenced in this study, there continues to be support for a model incorporating both internal and regional influences to explain policy adoption by states. The outlook for the future of the health care transparency movement looks promising. The health care transparency movement promotes improved access to information, patient empowerment, improved patient safety and quality of care, improved provider accountability, and lower health care costs. This movement is not a fad, but rather a permanent change being implemented in all health care settings across the United States. Improved health through reliable, accessible data and data- supported decisions is increasingly becoming the norm and less an idealistic scenario to be realized in the distant future.

xi CHAPTER ONE

INTRODUCTION

The demand for transparency in public policy and decision making has been gaining momentum, especially in the most recent decades. The word “transparency” has been added to the modern American lexicon, especially in government circles, to mean that information or a process, such as a decision making process, should be accessible and understandable. While the buzzword may be relatively new, the concept of open and available decision making and public reporting of information certainly is not. In the past, the public outcry for information resulted in several early laws requiring some form of public reporting of hospital data in certain states and/or Government in the Sunshine laws. More recently, policy champions for transparency have advocated for more comprehensive and sophisticated health care public reporting laws, including the display of public health care information on comparative consumer websites.

Identification of the Problem Health care decision making by consumers is difficult and complicated on its own without delving into the emotional issues that often convolute the process (Starr, 1982). Among the difficulties that consumer-patients face in making health care decisions is the lack of available, clear, simple, and reliable health care data. The United States health care system has been described as “…opaque, abstruse, variable, incredibly complex, and weirdly fragmented” (Sipkoff, 2004b, p.1). Critics state that U.S. health care system has so many problems that there may not be a single solution to resolve them all. However, many stakeholders, including “…purchasers, health plans, patients, and even some providers, are starting to…” (Sipkoff, 2004b, p.1) promote “universal transparency” as a significant first step to addressing these issues. “Universal transparency,” also known as transparency, refers to “…standardized performance metrics and outcomes reports that are…” (Sipkoff, 2004b, p.1) easy to access by any interested party. In health care, some of the more frequently used

1 performance and outcomes metrics include reports concerning “…hospital and surgical mortality and morbidity rates, physician compliance with chronic disease management, charges, and reimbursements…” (Sipkoff, 2004b, p.1). Transparency is a paradigm shift from an acute model to a chronic model of health care management (Sipkoff, 2004b, p.1). The current U.S. health care system is an acute model of health care where health care is delivered in response to sick or injured people seeking treatment. In this model, “more care, which generates income, does not mean better care. In fact, we are paying more for bad care because when medicine fails, it means more hospitalization, more doctor visits, and more drugs…” (Sipkoff, 2004b, p.4). Whereas, in a chronic health care management model, health care is provided to prevent and/or manage a person’s health care for the duration of person’s life. In this model, providers can be rewarded for healthy outcomes via incentive payments through “pay-for-performance” programs (Sipkoff, 2004a, p. 39; Sipkoff, 2004b, p.1). In the current U.S. health care system, the patient-consumer is unaware of the true cost of his/her health care. This is because we have an employer-subsidized health care system where the patient-consumer is distanced from the costs of his/her health care. Health care transparency advocates believe that if the patient-consumer is provided health care information, s/he would comparison shop and choose to purchase services from providers with the highest outcomes with the lowest charges; thereby, improving the overall quality and lowering the overall cost of health care (Sipkoff, 2004b, p.1). Succinctly stated, some stakeholders are advocating for the adoption of state health care transparency legislation to address the challenges of the lack of clear, accessible, reliable data to make health care decisions in the present U.S. health care system. In this dissertation, I examine the issue of health care transparency enactments to collect and publicly report inpatient data by states.

Research Question The question central to my research investigates “What factors influence a state legislature to enact a health care transparency policy?” To narrow the scope of my

2 study, I focus on factors influencing states to enact health care transparency laws to collect and publicly report inpatient data. This dissertation provides an analysis of the state inpatient health care transparency law enactments in the United States during the 1970s through 2000s. Health care transparency policies, specifically the collection and public reporting of inpatient data, which became mandated by law in numerous states during 1971 through 2006, are discussed. Thirty-eight states enacted inpatient health care transparency laws during that 36 year period. While the inpatient health care transparency laws vary, I make the assumption that they can be studied as a group because they all have the element of inpatient health care in common. This dissertation provides an analysis and evaluation of state inpatient health care transparency law enactments between 1971 through and including 2006, using event history analysis, to explain what factors were most influential. For this study, I limit the analysis to studying whether the inpatient health care transparency laws are enacted. I do not study the long term impacts on health care costs. In this dissertation, I analyze the possible determinants of inpatient health care transparency law enactments by 38 states. I use the Unified Model of State Policy Innovation, provided by F.S. Berry and W.D. Berry (1990, 1999), which unifies the internal determinants and regional diffusion approaches of analysis for state policy adoption, as the framework for my study hypotheses and the analysis of inpatient state health care transparency law enactments by states. The Unified Model of State Policy Innovation posits a unified explanation of state policy adoptions. The model unifies the internal determinants and regional diffusion approaches of analysis for state policy adoption. This study tests eight hypotheses using event history analysis (EHA). EHA is an analytical technique that allows for the testing of a state government innovation theory that incorporates internal determinants and regional influences on state policy adoption. Although there are numerous methods to conduct event history analysis, this study uses the Cox proportional hazards model (also known as Cox regression). Cox regression is a popular method for studying time-to-event data for policy adoption and diffusion studies.

3 Significance of the Research Health care transparency is important to study for a number of reasons. On a macro level, this issue is significant as it may lead to the empowerment of individuals to make health care decisions via the provision of open, accessible, and accurate health care information. Through this accessibility to health care data, it may be possible to help restore public trust in government. Additionally, health care transparency is important as it relates to holding health care providers accountable for the quality and costs of their services. Finally, health care transparency is of great importance as it addresses improving health outcomes and decreasing some of the highest federal costs including Social Security, Medicare, Medicaid, Children’s Health Insurance Program (CHIP) programs, and state costs such as Medicaid and CHIP programs. The issue of transparency is ripe for discussion and progress. “…We are in a much different place today than we were just a few years ago when people were challenging the idea that quality could even be measured, let alone that it should be reported,” stated “…Debra Ness, Executive Vice President of the National Partnership for Women and Families and co-chairwoman of the Consumer-Purchaser Disclosure Project…” (Sipkoff, 2004b, p.2). Dr. Kizer, “…president and CEO of the National Quality Forum,” (Sipkoff, 2004b, p.2) agreed and stated his support from an economic viewpoint. He stated that U.S. spends an enormous “…amount of money on a fragmented, uncoordinated system of care. Some studies say as much as 45% of each health care dollar is wasted. It isn’t that it’s too costly to make transparency happen. It’s too costly not to” (Sipkoff, 2004b, p.5). Numerous stakeholders and researchers agree that transparency has great potential as a cost-saver, and establishing transparency into the health system is the most significant cost-saver they have identified. At the time of this research, no study had focused solely on the enactment of state health care transparency laws. In this study, an analysis of inpatient health care transparency laws is conducted and health care transparency policy innovation and diffusion is explored. The findings of this study explain why states enact inpatient health care transparency laws. Additionally, this research serves to expand the use of the Unified Model of State Policy Innovation (F.S. Berry & W.D. Berry, 1990, 1999) as a

4 powerful explanatory model for state policy innovation and diffusion in the analysis of inpatient health care transparency issues. Finally, this research serves to expand the use of event history analysis and specifically the Cox regression method as their value as analytical methods and appropriateness for use in public policy studies is repeatedly demonstrated. This research is important to the discipline of public administration and policy as it contributes to the fields of health care policy, innovation and diffusion theory, and open government, including its subfield of transparency laws. I seek to add to the existing literature of health care policy and innovation and start a discussion focusing on the important political, economic, and social issues of health care transparency laws.

Preview of the Chapters Chapter two presents a brief history of public reporting, public health care reporting, and health care transparency laws. Additionally, it provides a review of the theoretical perspectives of policy innovation and diffusion in states, and a brief discussion of the states’ adoption of health care innovations during the past four decades. Chapter three describes the research design and the research question for this study. A description of the Unified Model of State Policy Innovation is provided; its applicability to state inpatient health care transparency laws is explored; and the study hypotheses are articulated. Next, the variables are defined, and their data sources and datasets are provided. Then, the event history analysis technique and Cox regression method are described. Finally, the statistical model is presented. Chapter four details the descriptive information regarding the enactment of inpatient health care transparency laws by states from 1971 through and including 2006. Next, the hypotheses are examined and the statistical analysis results are presented. Finally, the hypotheses are interpreted and discussed. Chapter five provides a focused review of literature about diffusion strategies and the practical politics of professional associations to determine their role and importance in state inpatient health care transparency law enactments. Then, I conduct a qualitative analysis of professional associations using three techniques to determine

5 what their role was and whether it was consistent with the literature. The three techniques that I use to conduct the qualitative analysis include: 1) a descriptive history of the National Association of Health Data Organizations (NAHDO) where I examine its role as a diffusion agent and the diffusion process strategies it employs; 2) a qualitative content analysis of primary data from NAHDO’s reports and newsletters that capture strategies and practices it has used to educate its members about health care transparency laws; and 3) telephone interviews of three of NAHDO’s key stakeholders to gather first-hand knowledge of diffusion strategies, practical politics, and other activities NAHDO has implemented to support states in their pursuit to enact inpatient health care transparency laws. Finally, I summarize and discuss the qualitative analysis results and compare them to the review of the professional association diffusion literature. Chapter six begins with a brief overview of this dissertation. Then, it describes the impact that this study has on innovation and diffusion research. Next, the study limitations and suggestions for future research are explored. Finally, a conclusion is presented addressing the outlook for the field of innovation and diffusion, as well as, the health care transparency movement.

6 CHAPTER TWO

THEORETICAL FRAMEWORK

Introduction This chapter presents a brief history of public reporting, public health care reporting, and health care transparency laws. Additionally, it provides a review of the theoretical perspectives of policy innovation and diffusion in the states, and a brief discussion of the states’ adoption of health care innovations during the past four decades.

Brief History of Public Reporting, Public Health Care Reporting, and Health Care Transparency Laws Origins of Public Reporting The public reporting of information is not a new concept. The public has often requested, and at times demanded, openness of information. In fact, in Florida in 1967, Governor Askew made it a priority to enact the “Government in the Sunshine” laws (Chapter 286, F.S.) to enable the citizens of Florida to participate and be aware of the public’s business as it was taking place. Governor Askew believed that this would promote the principles of accountability, fairness, and honesty. Today, Florida’s Government in the Sunshine laws are still in effect and other laws have been added which follow its intent such as Florida’s Plain Language Initiative (Executive Order 07-01) and Florida’s Health Care Transparency law. Promulgated by Governor Crist in 2007, the Plain Language Initiative requires Florida’s government to write succinct and clear policies and correspondence to encourage open communication. Previously as Attorney General, Crist successfully advocated for the development of a public pharmacy price comparison website to accompany the gas price comparison website that was already in existence. The pharmacy price comparison website, a public website to compare pharmacy price information, is a

7 requirement of Florida’s Health Care Transparency law (Chapter 2004-297, Laws of Florida). Florida’s Health Care Transparency law requires the public health care data collection and reporting to promote health care quality and to enable the consumers and purchasers to comparison shop. Additionally, Florida’s Health Care Transparency law required hospital, inpatient, physician, pharmacy, and other health care data to be publicly reported.

Origins of Public Reporting of Health Care Information Similar to Governor Askew’s Government in the Sunshine laws, health care public reporting laws are not a new concept. However, recently the demand for the public reporting of health care information is on the rise. In fact, the origins of public reporting and public health care reporting by states began at approximately the same time during the 1970s and 1980s. These systems were originally developed for health care cost containment and planning purposes (e.g., certificate of need and other resource-allocation decisions). The primary source of the health care data was hospital facility utilization data because the information was available and comparable (The State Health Experience in Health Quality Data Collection, 2004, p.3); thus, two of the major milestones of the 1980s were the establishment of the reporting requirements for the State Hospital Financial Utilization Report and the Health Care Financing Administration (HCFA) Mortality Reports. Though many critics have attacked the heavy reliance on inpatient care and inpatient surgery data, the importance of facility data is significant. “After all, it is in the facilities that the sickest are treated; the most expensive care is rendered; and where patient risk is the greatest” (NAHDO website, 2/2/2012).

The First Wave of Health Care Transparency (The 1990s) The 1990s may be viewed as the first wave of transparency. “In the 1990s, the uses of the data expanded beyond (or in lieu of) regulatory purposes to support market and comparative analyses; leverage provider accountability and quality initiatives; and supply public health agencies and researchers with population-based health care data” (The State Health Experience in Health Quality Data Collection, 2004, p. 5). During this first wave of transparency, the main goals of the movement were “ to make

8 comparative information about hospital performance publicly available and to raise awareness about the enormous variation that exists in health care, especially health care use and outcomes” (Love, 2006, p.2). Additionally during the 1990s, it became quite noticeable that all states were not “…equal when it comes to comparative, quality health care reporting” (Love, 2006, p.2). These facts were brought to the forefront and evidenced in the “U.S. News and World Report’s Best Hospitals” issue, Pennsylvania’s Coronary Artery Bypass Graft surgery (CABG) Outcomes, the results of the Dartmouth Atlas measures, and the Institute of Medicine’s pioneering reports (Love, 2006, p.2). The Institute of Medicine (IOM) is generally credited for kick-starting the transparency movement by launching “…a concerted, ongoing effort focused on assessing and improving the nation's quality of care in…” 1996 (Institute of Medicine, 2005, p.1). The first phase of the quality initiative began with a review of literature by the RAND corporation to understand the research concerning quality and health care services, and defining “…the nature of the problem as one of overuse, misuse, and underuse of health care services” (Institute of Medicine, 2005, p.1; Chassin, 1998).

The Second Wave of Health Care Transparency (The 2000s): The IOM’s Pioneering Reports and the STEEEP Measures During 1999 through 2001, the Committee on Quality of Health Care in America “…stated that the health care environment must be radically transformed to close the chasm between what we know to be good quality care and what actually exists in practice” (Institute of Medicine, 2005, p.1). During the second phase of the quality movement, the Institute of Medicine’s (IOM) To Err is Human: Building a Safer Health System and Crossing the Quality Chasm: A New Health System for the 21st Century reports were published in 2000 and 2001 respectively. The To Err is Human report highlighted “…how tens of thousands of Americans die each year from medical errors and effectively put the issue of patient safety and quality on the radar screen of public and private policymakers” (Institute of Medicine, 2005, p.1). The Quality Chasm report described six goals: “…care should be safe, timely, effective, efficient, equitable, and patient-centered (STEEEP)” (Institute of Medicine, 2005, p.1), and stated that to achieve

9 STEEEP, the IOM recommended ten simple rules for reform and health care delivery redesign. In Crossing the Quality Chasm: A New Health System for the 21st Century (2001), the third phase of the IOM quality initiative was described. In it, the authors campaign for Congress to set aside $1 billion for STEEEP projects (Kerr White Health Care Collection & IOM, 2001, pp.5-6; Sipkoff, 2004b, p.3). In the Quality Chasm report, the authors compared the current health care delivery approach to their recommended health care delivery approach which uses the transparency paradigm and the STEEP principles. Table 1, “Institute of Medicine’s ‘The 10 Simple Rules for the 21st Century Health Care System’’” displays the differences between the old and new way of thinking and delivering health care.

st Table 1. Institute of Medicine’s “10 Simple Rules for the 21 Century Health Care System” Current Approach New Rule 1. Care is based primarily on visits. Care is based on continuous healing relationships. 2. Professional autonomy drives Care is customized according to patient variability. needs and values.

3. Professionals control care. The patient is the source of control.

4. Information is a record. Knowledge is shared and information flows freely.

5. Decision making is based on training Decision making is based on evidence. and experience.

6. “Do no harm” is an individual Safety is a system property. responsibility. 7. Secrecy is necessary. Transparency is necessary.

8. The system reacts to needs. Needs are anticipated.

9. Cost reduction is sought. Waste is continuously decreased.

10. Preference is given to professional Cooperation among clinicians is a priority. roles over the system. SOURCE: Kerr White Health Care Collection & Institute of Medicine (U.S.). (2001). Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, D.C.: National Academy Press.

10 “Peter Lee, president and CEO of the Pacific Business Group on Health, a powerful purchaser coalition in San Francisco and co-chairman of the Consumer- Purchaser Disclosure Project” (Sipkoff, 2004b, p.3) stated that “STEEEP epitomizes for me what transparency is” and the purpose of transparency is “…to get valid information that can be used for quality improvement, resulting in rewards by purchasers and informed choices by consumers” (Sipkoff, 2004b, p.3). Next, I discuss the characteristics of the health care data laws and data systems.

Administration and Funding of State Health Care Data Programs Differences exist among the state health data initiatives’ characteristics for a variety of reasons and the reasons often explain why the states vary markedly in the quality of the state health care data programs. This discussion begins with an explanation of the structure, administration, and funding of the state health care data programs. One differences noted is that state health care data programs are not all administered the same way. While a majority of the state health care data programs are operated by state government, some are operated by private organizations. The decision as to how the state health care data program operates tends to be closely connected with whether the state has a mandate for the collection of state health care data. When there is a state law and health care data collection is mandatory, the state agency usually operates the program. However, when a state has voluntary reporting, a private organization usually operates the health care data program. Occasionally, a state with a mandate to collect and maintain a health care database will delegate the data collection responsibility and the maintenance of the database to a private organization. This is known as a “delegated” model of operation, which is a useful organizational relationship when the state has resource limitations (The State Health Experience in Quality Data Collection, 2004, p.5). “State health care data programs are currently funded through a variety of mechanisms, but unlike state vital records data systems, [they] receive no federal funding” (The State Health Experience in Health Quality Data Collection, 2004, p.6). In mandated systems, the states and providers are responsible for the operating costs which they obtain through a general fund appropriation (if funded), assessment, or

11 industry fee. Then, “…these costs are passed on to the taxpayers, consumers, and purchasers” (The State Health Experience in Quality Data Collection, 2004, p.6). In voluntary reporting systems, the funding is generated mainly through “…membership fees, data product sales, or grants/contracts” (The State Health Experience in Quality Data Collection, 2004, p.6). Some examples of organizations with voluntary reporting systems include the states without legislative mandates, the physician association societies, the Centers for Medicare and Medicaid Services (CMS), and some consulting organizations (The State Health Experience in Health Quality Data Collection, 2004, p.6). Table 2 displays the strengths and weaknesses of the mandatory and voluntary reporting systems.

Table 2. Mandated versus Voluntary Systems: Strengths and Weaknesses Type of Collection Strengths Weaknesses Mandated  All providers must participate  May take years to enact and Collection*  Compliance with timelines and data implement mandated systems reporting specifications is required  Changes in scope may require time-  Specifies penalties for non-compliance consuming amendment of state law  Defines the framework for data  Trade-offs during legislative process collection, reporting, access may result in restrictions in public  Exempt from federal Health Insurance reporting, such as prohibition of Portability and Accountability Act collection or disclosure of provider- (HIPAA) privacy regulations in most level data cases  Program funding subject to political pressure or state revenue shortfall Voluntary  Increased flexibility to meet current  May be a closed process, with limited Collection* market needs transparency in collection and analytic  Funding not dependent on the methods legislative process  Some providers may refuse to  May meet with less resistance from participate provider community  Subject to HIPAA privacy regulations for data exchange/disclosure  Unlikely to publish provider-specific information  Products/services must generate sufficient revenue to sustain effort  Funding subject to non-public process and “dealmaking” * = Mandated collection means that data collection is mandated by state law. Voluntary collection means that the data collection is not mandated by state law. SOURCE: The Consumer-Purchaser Disclosure Project: Improving Health Care Quality Through Public Reporting of Performance, C/O National Partnership for Women and Families. (2004, May). The State Experience in Health Quality Data Collection (p.7).

12 Like other characteristics of state health care data programs, funding for state health care data programs is not uniform. Sometimes funding is reallocated within the state government or outsourced to achieve efficiencies or savings. In some states, the state health care data program mandate or its funding has been repealed by the legislatures. In other states, the legislation was enacted but never funded. Yet, most “…states have successfully maintained programs because the program has demonstrated value to various stakeholders” (The State Health Experience in Quality Data Collection, 2004, p.6). Regardless of the funding source(s), financial stability is a key factor to maintaining a valuable state health care data program. States must have ongoing financial support to collect data over time because it takes time to create a reputable data repository (The State Experience in Health Quality Data Collection, 2004, p.6). Next, I describe the different types of data collected in health data systems and their importance.

Scope of the Databases: Types of Data While there may be many variations between state health care data programs, there are some foundational principles that they all share. “At a minimum, all state programs that exist collect inpatient hospital discharge data” (The State Experience in Health Quality Data Collection, 2004, p.9). . This dataset provides complete demographic, clinical, and financial records for all patients admitted to an acute care hospital, regardless of whether the patient is insured or not. This dataset is also known as administrative or billing data. The national Uniform Bill (UB) 92 is a hospital bill or claim that physicians, other health care professionals, and facilities are required to complete when submitting a claim for payment. It is the source for the discharge data. The dataset reflects the “…hospital use and outcome experience of the entire state’s population and supports comparisons across payers, providers, and sub-populations such as rural, urban, specific minority group, etc., over time” (The State Experience in Health Quality Data Collection, 2004, p.9). Thus, discharge data repositories are used by states as the main source of health care data. During the first wave of transparency, state health quality reporting initiatives

13 used the discharge dataset to create public use files for secondary uses or users; financial and utilization reports and websites; and state-sponsored quality reports and websites (The State Experience in Health Quality Data Collection, 2004, p.9). Administrative health care datasets have advantages and limitations associated with their use. There are numerous advantages to using administrative datasets such as: a) it is readily available; b) it is politically feasible as it has a low reporting burden for facilities; c) there is relative uniformity of data across providers; d) the dataset contains provider reimbursement information; and e) the dataset can be enhanced with additional data. Some limitations to using an administrative dataset include: a) the data elements lack clinical detail because the UB92 form and UB40 forms were created for billing purposes; b) the data contains coding variation and bias; and c) the data can lack timeliness (Love, 2005, p.9).

State Health Care Data Uses Similar to the variation present in the governance, funding, and scope of the data collected, the state health care data programs develop their data repositories and use their data for a variety of purposes. The purposes range from state planning to helping with a specific sub-population such as asthma or diabetes patients’ treatment. State health data programs assist purchasers and consumers with obtaining better information to make decisions; public officials to help control the rise in health care expenditures; and the community by providing provider performance reports. Towards the end of the first wave of transparency (i.e., late 1990s), states often publicly reported provider-specific performance measurement data for comparison purposes (The State Experience Health Quality Data Collection, 2004, pp.12-13).

State Health Care Data Access and Release Once the dataset in made publicly available and released, the state programs or organizations use the data to calculate a variety of different measures. All of the measures have been created with the purpose of making the data meaningful. This information is used in various public reports, available in a printed format or a website. Which measures to display (or not display); whether the measures require additional

14 calculations such as risk adjustment prior to their display; the level of provider-specific detail to display; how to graphically display the measures; whether to display explanatory information with the measures; whether and which symbols or scales to use to assist the audience in understanding the information; and who has access to the information are all issues that receive considerable attention when publicly reporting health care information (The State Experience Health Quality Data Collection, 2004, p.13).

Types of Performance Measures: Structural, Process, and Outcome Measures One of the performance measurement issues frequently debated is whether structural measures, process measures, and/or outcome measures should be used to publicly report the information. Structural measures and process measures provide important information used to improve the quality of care. However, critics state that they do not provide the information that the consumers and purchasers desire most: whether or not the care they purchase is effective and efficient. For example, structural measures use data such as the presence of an electronic prescribing machine and process measures use data such as “Did the doctor perform a foot exam?”. This information can be used to guide future decision making concerning quality improvement efforts. Outcome measures use data such as “Did the patient recover full function after a knee replacement?” and/or “How much did the whole episode of treatment cost?” which address questions concerning the effectiveness and efficiency of treatment. The main argument that providers state against the use of outcome measures is that outcomes are often influenced by factors other than the treating provider (e.g., patient behavior). They are correct. However, providers are not the only group confronted with this issue. For example, the airline industry also wrestles with this same challenge of multiple factors influencing outcomes (e.g., a commercial airline’s average on-time record is affected by weather conditions); however, this industry’s outcomes are nonetheless used for accountability because outcomes are the consumers’ primary purpose in buying the service or product. Therefore, like the airline industry, providers

15 should be assessed using outcome measures because they are the most meaningful marker of value for consumers. Outcomes, especially when compared to process measures, are the ultimate measures of quality of care and spending.

Public Reporting of Health Care Data: The State Perspective In a National Association of Health Data Organizations (NAHDO) Quality Survey conducted in August 2005, state health data organizations described what types of data and measures they intended to publicly report. Ten states indicated that they planned to release select Agency for Healthcare Research Inpatient Quality Indicator (AHRQ IQI) measures and five said they were uncertain. Eight states reported they planned to release select Agency for Healthcare Research Patient Safety Indicator (AHRQ PSI) measures and three replied that they were uncertain. The states described an increase in “…interest in public reporting on all aspects of health care, not just hospital information” (Love, 2006, p.22). Additionally, states reported that they are moving towards collecting administrative data due to the lower cost and availability of the administrative data. States also announced that they are prepared to modify and/or augment the administrative data. Finally, states said that while “…process measures avoid the outcomes/risk adjustment issue,” they do not think they are “…sufficient for most state reporting agendas” (Love, 2006, p.22).

Public Reporting in Health Care: Data Categories Clinical reports, utilization/access reports, and financial reports are the three main categories of public reporting in health care. In 2006, NAHDO reported that the following clinical reports were conducted: a) Pennsylvania, Florida, and Illinois were publishing infection reports; b) New Jersey, New York, California, Massachusetts, and Pennsylvania (for CABG) were publishing mortality reports; c) Minnesota was publishing an adverse events report; and d) CMS, the Joint Commission on Accreditation of Health Organizations (JCAHO), and Leapfrog were publishing process measure reports. Additionally, the following utilization/access reports were conducted: a) The Healthcare Cost and Utilization Project’s online interactive query system (HCUPNet) was publishing national health care cost and utilization project (HCUP) data on inpatient care; b) The

16 majority of states were publishing inpatient data reports and Utah was publishing Utah’s Indicator-Based Information System for Public Health Data Resources (UTAH-IBIS-PH); c) The American Hospital Association (AHA) was publishing the Dartmouth Atlas report; and d) The Agency for Healthcare Research and Quality (AHRQ) was publishing the National Healthcare Quality Report. Finally, Virginia Health Information (VHI) and some consulting groups were publishing margins in financial reports (Love, 2006, p.10).

Public Reporting in Health Care: The Future Outlook After describing some of the similarities and differences among states and other organizations, one can quickly surmise that there are many public health care reporting challenges to address. There are numerous barriers to public reporting on health care quality. Some of the more profound barriers include the documentation requirements and the reporting demands that place a burden on the providers’ information systems. Second, the timeliness of data collection can be a barrier because the timeliness of data collection varies among the different types of data sources. Complying with the Health Insurance Portability and Accountability Act (HIPAA) and securing the data to address privacy concerns can be another barrier. Additionally, there may be political resistance to publicly reporting the data. The statistical issue of small numbers can be a barrier to public reporting, as well. Finally, states may be required or prefer to rely on a set of national consensus measurement standards, but the state may find that a national entity does not provide adequate support to the state (Love, 2006, p.31). Numerous studies are evidencing that public reporting serves as a driver to improve the quality of health care. The results of Hibbard’s (2005) study support this conclusion. Hibbard’s study results detailed that: a) Nine months after a public report was published, hospitals were more likely to engage in quality improvement activities if the results were reported publicly as compared to when the results were reported privately or not reported; b) Two months after a public report was published, consumers surveyed stated that they changed their view of hospitals included in the report. The consumers were able to accurately recall hospitals ranked as high or low performers. Additionally, at two months, 24% of the consumers had talked to others about the results; and later, at two years, about 50% of the consumers had talked to others about

17 the report; c) About one-third of hospitals significantly improved their performance in areas where they were previously reported as a poor performer, while 5% declined in performance; and d) In contrast to the public report, only one-fourth of the internal reporting hospitals evidenced significant improvement, while 14% declined (Hibbard, et al., 2005, pp.1154-58; Love, 2006, p.38). Therefore, with evidence that public reporting acts as a driver to improve health care performance, states and organizations continue to move forward with their health care public reporting initiatives. States are expanding the scope and improving the quality of their databases, and they are working towards developing uniform and reliable data elements with their stakeholders. Additionally, states are enhancing their reporting requirements to include nationally measures, such as those used in the National Committee for Quality Assurance Healthcare Effectiveness Data and Information Set (NCQA HEDIS) to make data comparable among states. Third, states are benefiting from the HIPAA electronic data standards mandates to improve the integrity of the data and reduce the collection burden on the providers. Fourth, states are publicly reporting provider-specific comparative health care data. Overall, the states have made significant gains towards improving quality measurement for all entities along the health care continuum (The State Experience in Health Quality Data Collection, 2004, p.14). In the future, providers will be confronted with an increased demand for public reporting. The scope of data collected will continue to expand, including the collection of physician information and data from other outpatient sites. Measurement will expand to include measures across the continuum of care. It is possible that the electronic health record movement will gain momentum which would reduce abstraction efforts, but would likely further increase the demand for data. The providers who embrace rather than resist the transparency movement and take proactive steps towards measurement and reporting will have a competitive advantage in the future (Love, 2006, p.36).

18 Review of Policy Innovation and Diffusion in the States Review of Policy Innovation in the States The present study is part of an emerging area of research that focuses primarily on the determinants of state policy choices (e.g., why some states enact certain policy options, while others do not), rather than policy impact (F.S. Berry & W.D. Berry, 1990, 1992; Wong & Shen, 2002). While there are numerous policy adoption frameworks that investigate the causes of state policy action including the multiple streams and punctuated equilibrium policy adoption frameworks, this study builds upon the innovation and diffusion policy adoption framework (Baumgartner & Jones, 1993; F.S. Berry & W.D. Berry, 1990, 1999; Kingdon, 1984, 1985). Policy adoption, as opposed to policy innovation, refers to the stage in policymaking where someone or some group, which has the authority and power to do so, accepts the policy. Walker (1969, p.881), one of the pioneers of public policy innovation research, described ‘innovation’ “…as a program or policy which is new to the states adopting it, no matter how old the program may be or how many states have adopted it.” In his 1969 landmark study, Walker analyzed policy diffusion in fifty U.S. states. He is recognized as the first political scientist to examine innovation by state governments, and due to his study, state policy diffusion by neighbors became an area of numerous studies. Following Walker, Gray (1973) conducted an influential study where she attempted to explain state adoption of new policies with a general theory of innovation that could be applied to many different state policies. She studied state adoptions of welfare policies, education policies, and civil rights laws and concluded that the community of American states as a social system was supported, but several factors limited the applicability of the national interaction model for government innovation. While there were numerous studies of state innovations published in a variety of policy arenas during the 1970s through the 1980s, some of the more recent studies of state innovation published include: lotteries by Berry and Berry (1990), taxes by Berry and Berry (1992), tort law by Lutz (1997), interstate compacts by Dodson (1998), school choice by Mintrom and Vergari (1998), death penalty reform by Mooney and Lee (2000),

19 electricity deregulation by Ka and Teske (2002), charter school legislation by Wong and Shen (2002), and higher education reforms by McLendon, Heller, and Young (2005). To date, no study has examined the enactment of health care transparency laws. Therefore, I seek to add to the existing literature of health care policy and innovation and start a discussion focusing on the important political, economic, and social issues of health care transparency laws. To do so, I review the existing models of policy and innovation and apply the relevant models to the current study to develop a framework to explain why states adopt inpatient health care transparency laws.

Introduction to Internal Determinants and Diffusion Models In innovation and diffusion literature, the internal determinants models and the regional diffusion models are the two leading explanations for new policy adoptions by states. The internal determinants models hypothesize that internal or intrastate characteristics, such as the state’s economic, social, and political characteristics, are the factors which influence a state to adopt a new policy. Whereas, the regional diffusion models posit that intergovernmental factors, such as neighboring states’ policy actions, are the factors which influence a state to adopt a new policy (F.S. Berry & W.D. Berry, 1990, 1992, 1994a, 1999).

Internal Determinants Models As mentioned, the internal determinants model is one of the two main explanations for state policy adoption and the internal determinants model assumes “…that political, economic, and social characteristics of a state influence policy adoption” (F.S. Berry & W.D. Berry, 1999). These include factors such as personal income, demographics, political ideology, party competition, strength of the economy, and the needs of a population. The internal socioeconomic status factors include per capita income, size, demographics, and employment rates, while the internal political factors include political party, the presence of unified or divided control of the legislature and governor’s office, election year cycle, state fiscal health, and the presence of policy entrepreneurs. The model assumes “…that once a state is aware of the policy, it is the internal characteristics of the state that determine if and when the adoption will occur

20 instead of pressure by other states’ adoption or explicit evaluations of the impacts of the policy in earlier-adopting states” (F.S. Berry & W.D. Berry, 1999, p.178).

Diffusion Models The second main explanation for the adoption of a new program or policy by a state is the diffusion model. In the diffusion model, policy adoption is the result of states observing one another and borrowing policy options. Specific diffusion influences include sister states, similar agencies, and professional ties. This model assumes that there is no impact by the internal factors or intrastate characteristics. Next, I describe the Unified Model of State Policy Innovation, which provides the framework for the current study.

The Unified Model of State Policy Innovation In 1990, F.S. Berry and W.D. Berry proposed a unified explanation of state policy adoptions: the Unified Model of State Policy Innovation. They offered an explanation that incorporates the internal determinants model and the regional diffusion model to explain the influences on a state’s likelihood to adopt a new policy. Based on Mohr’s theory of organizational innovation, they showed that the two models can be used together to explain state policy adoption. Their central theoretical and methodological objectives were: a) to suggest that the neither internal determinants nor regional diffusion models can be the sole explanation for state policy adoption; b) to introduce a pooled cross-sectional time series approach to state innovation research that would allow for the testing of a unified theory of internal and regional policy adoption; and c) to illustrate the potential of event history analysis for testing explanations of rare political events, such as policy adoptions. None of these three objectives had been proposed and/or successfully implemented in political science until F.S. Berry and W.D. Berry’s research in 1990. A more in depth description of F.S. Berry and W.D. Berry’s Unified Model of State Policy Innovation is presented in Chapter three, which provides the framework for the development of the study hypotheses and the analysis of inpatient health care transparency law enactments by states.

21 States’ Adoption of Health Care Policy Innovations in the 1990s – 2000s The public policy literature numerous studies about state innovation. However, after reviewing the public policy literature for health care policy research, only a limited body of health care policy innovation literature was found to exist. Additionally, to date, I have been unable to identify any studies focusing solely on the enactment of health care transparency laws. The following is a brief summary of the health care innovation and diffusion policy research reviewed. In 1991, Glick and Hays studied living will laws, which was immediately followed by Glick’s (1992) study about the right to die. Then, in 1994, Gray, a classic author in innovation and diffusion policy research, wrote an article concerning federalism and health care. One year later, Mooney and Lee (1995) conducted a study on pre-Roe abortion policy. Shortly thereafter, Stream (1999) published a study examining state small group health insurance market reforms. This study was followed by Arsneault’s (2000) study about welfare policy and Balla’s (2001) study of the Health Maintenance Reorganization Act. Then, in 2002, Satterthwaite published a study of Medicaid managed care programs, Cain and Mittman published a study for the California Health Care Foundation titled, “Diffusion of Innovation in Health Care” and Weissert and Weissert published the book titled: Governing Health: The Politics of Health Policy. Just one year later, in 2003, Volden presented the paper, “States as Policy Laboratories: Experimenting with the Children’s Health Insurance Program” and Holahan, Weil, and Wiener co-authored the book titled: Federalism and Health Policy. A couple of years later, in 2006, Miller published a study of Medicaid nursing facility reimbursement reform.

22 CHAPTER THREE

RESEARCH DESIGN

Introduction This chapter describes the research design for the study. The research question is presented. A description of the Unified Model of State Policy Innovation is provided; its applicability to state inpatient health care transparency laws are explored; and the study hypotheses are articulated. Next, the variables are defined, and the data sources and the dataset are described. Then, the event history analysis technique and the Cox regression model area described. Finally, the statistical model is presented.

Research Question The question central to my research is “What factors influence a state legislature to enact a health care transparency policy, specifically a state policy requiring data collection and the public reporting of inpatient data?”

The Unified Model of State Policy Innovation In this chapter, the Unified Model of State Policy Innovation is described. This model provides the framework for my study hypotheses and the analysis of state inpatient health care transparency law adoptions. Earlier policy adoption models advocated that internal characteristics or regional factors influenced states to adopt a policy. In 1990, F.S. Berry and W.D. Berry presented the Unified Model of State Policy Innovation that explained state policy adoption using both the internal determinants and the regional diffusion models. In 1999, F.S. Berry and W.D. Berry (p.187) proposed that “models of state government innovation should take the following general form:”

ADOPTi,t = f (MOTIVATIONi,t, RESOURCES/OBSTACLESi,t, OTHER POLICIESi,t, EXTERNALi,t)

23 In F.S. Berry and W.D. Berry’s (1999) Unified Model of State Policy Innovation, the unit of analysis “…is the American state eligible to adopt a policy in a particular year…” (p.187); the conceptual “…dependent variable…” or hazard rate, “…ADOPTi,t, is the probability that state i will adopt the policy in year t” (p.188); and the independent variables include the internal determinants: MOTIVATIONi,t, RESOURCES/

OBSTACLESi,t, OTHER POLICIESi,t, and the independent diffusion variable:

EXTERNALi,t (F.S. Berry & W.D. Berry, 1999, p.187).

Next, the independent variables are defined. MOTIVATIONi,t denotes variables signifying the motivation of public officials to adopt in state i at time t. The variables would include public opinion and electoral competition in the state and other ad hoc motivation factors. RESOURCES/OBSTACLESi,t represents the variables indicating potential barriers to policy adoption and resources which would help overcome identified barriers. For many policies, the level of economic development and professionalism of the legislative staff would be included. OTHER POLICIESi,t is a set of dummy variables identifying whether other policies within the state affect the probability of a state adopting a new policy. EXTERNALi,t represents variables indicating diffusion effects on a specific state at a specific time. Table 3 presents the definition of the equation terms for F.S. Berry and W.D. Berry’s (1999) Unified Model of State Policy Innovation.

Table 3. F.S. Berry and W.D. Berry’s (1999) Unified Model of State Policy Innovation: Definition of the Equation Terms UNIT OF ANALYSIS: Is the American state eligible to adopt a policy in a particular year. DEPENDENT VARIABLE: ADOPTi,t Is the probability that state i will adopt the policy in year t. INDEPENDENT VARIABLES:

Internal determinants: MOTIVATIONi,t Represents variables indicating the motivation to adopt the policy of public officials in state i at time t; they would include the character of public opinion and electoral competition in the state and other ad hoc motivation factors.

24 Table 3. Continued RESOURCES/OBSTACLESi,t Denotes variables reflecting obstacles to innovation and the resources available for overcoming them. For many policies, the state’s level of economic development and the professionalism of its legislature would be included.

OTHER POLICIESi,t Is a set of dummy variables indicating the presence or absence in state i of other policies that have implications for the likelihood that the state will adopt the new policy.

External Influences: EXTERNALi,t Denotes variables reflecting diffusion effects on state i at time t; thus, they would measure the behavior of other states at time t, or in the recent past. SOURCE: Berry, F.S. & Berry, W.D. (1999). Innovation and Diffusion Models in Policy Research. In P. Sabatier (Ed.), Theories of the Policy Process (pp. 187-88). Boulder: Westview.

The Study Hypotheses In this dissertation, like the Unified Model of State Policy Innovation created by F.S. Berry and W.D. Berry (1999), my explanations can be considered in terms of motivation, resources, obstacles, other policies, and external influences. This model assumes a state’s internal characteristics and inpatient health care transparency law enactments by nearby states influence whether the state will enact an inpatient health care transparency law. In Table 4, I provide a Unified Model of Inpatient Health Care Transparency Law Enactments with the hypotheses to be tested.

The Motivation to Innovate The F.S. Berry and W.D. Berry’s (1999) Unified Model of State Policy Innovation maintains that policy adoption can be explained in terms of a multitude of factors. The first group of factors studied is the “motivation to innovate” variables. One “motivation to innovate” variable addresses that politicians are more likely to adopt a policy when it is a popular policy with their electorate and an election is nearing. A second “motivation to

25 innovate” variable proposes that politicians are more likely to adopt a policy when the fiscal health of the state’s government is poor. Another “motivation to innovate” variables states that lawmakers are more likely to be motivated to adopt a policy when their state faces a crisis; when the policy agrees with the legislator’s own ideological principles; and following the publication of the Institute of Medicine’s two pioneering reports on health care transparency. The following is a discussion of these factors and the roles that they play in the enactment of inpatient health care transparency laws by states. The Proximity of State Elections. F.S. Berry and W.D. Berry (1992) posited the electoral hypothesis as an explanation to innovate. According to this hypothesis, the proximity of the election is the most important factor that public officials consider when they make their decision whether to enact an inpatient health care transparency law. In other words, politicians are likely to be swayed by public opinion as reelection approaches; therefore, they are more likely to support popular programs right before an election and pass unpopular programs right after an election, thereby allowing as much time as possible to pass between the passage of the unpopular legislation and the next election. The electoral hypothesis is built upon Tufte’s (1978) and Kiewiet and McCubins’ (1985) research which “…asserts that politicians adopt new policies at times within the election cycle that maximize their chances for reelection.” Lawmakers are likely to endorse a health care transparency bill because providing the consumer-taxpayer with health care information, lowering health care costs, and improving health care quality are perceived as being desired among constituents. Additionally, politician is likely to endorse a popular bill, such as an inpatient health care transparency law, in an election year because it is thought to increase their chances for reelection. In order to simplify my analysis, I focus on gubernatorial elections and ignore the effects of the legislative elections. However, it is unclear whether elections influence lawmakers in the enactment of inpatient health care transparency laws. As previously mentioned, the U.S. health care system is a very complex and highly fragmented system. Due to the complexity of health care cost issues, such as health care transparency issues, the public tends to demonstrate a lack of interest (Meier, 1988; Randall, 1999). If the health care

26 transparency issue is not a salient issue, then politicians do not need to worry that health care transparency enactment will generate voter retaliation. Therefore, similar to F.S. Berry and W.D. Berry’s (1990) electoral hypothesis, I hypothesize that

H1: For all states, an inpatient health care transparency law is most likely to be enacted in an election year. In states with more than two years between gubernatorial elections, enactment is least likely in the year immediately following an election.

Short-Term Fiscal Health of the State’s Government. In addition to political conditions, the economic environment is predicted to affect the motivation of state political officials to adopt an inpatient health care transparency law. The short-term fiscal health of the state’s government should be the primary economic motivation (F.S. Berry & W.D. Berry, 1990, p.401). While Berry & Berry’s (1990) fiscal health hypothesis examined the adoption of a state lottery policy as a way to increase revenues, I posit the enactment of an inpatient health care transparency law as a way to decrease a state’s health care expenditures. Health care transparency advocates argue that increased transparency in the health care market will increase comparison shopping by consumers. Specifically, the advocates of health care transparency believe that accessible health care data will lead to patient empowerment; thereby, resulting in an increase in the demand for evidence- based medicine. With an increase in demand for evidence-based medicine, due to its foundation on the provision of effective health care, the quality of health care is theorized to improve, and improvement in the quality of health care would result in lower overall health care costs (Sipkoff, 2004b, p.1). Therefore, as a reasonable extension, the enactment of a health care transparency law would increase consumer awareness of health care costs, encourage comparison shopping, increase the demand for evidence-based medicine; thereby increasing the quality of health and lowering health care costs. If the overall cost of health care decreases, then a state’s expenditures on health care would decrease. A state is likely to enact an inpatient health care transparency law as a potential solution to address its poor fiscal health, which may be due to numerous high-dollar

27 health care items its budget, in order to provide consumers with health care information to comparison shop to help decelerate its rising health care costs. Therefore, similar to F.S. Berry and W.D. Berry’s (1990) short-term fiscal health hypothesis, I hypothesize that

H2: The worse the fiscal health of a state’s government—that is, the greater its expenditures relative to its revenues—the more likely the state is to enact an inpatient health care transparency law.

A Crisis in Health Care. Fear plays some role in policy adoption as documented by earlier research investigating the “…relationship between a crisis and policy innovation” (Carter & LaPlant, 1997; Gray, 1973; Gray & Lowery, 1990; Nice, 1994; Savage, 1985; Stream, 1997; Walker; 1969). Supporters often advocate for new policies to be enacted to address a “crisis.” In the early 1990s through the present day, states are focusing attention on the rising cost of health care and reform offered through health care transparency. Politicians are likely to adopt a policy to address a perceived crisis in health care that may be due to the lack of accessible, accurate, understandable, and comparable health care data. Therefore, an innovative health care reform, such as an inpatient health care transparency law, to resolve the issue would be aimed at informing the state citizens to use a state health care website to comparison shop and help with their health care decision making. To reiterate, the concept behind transparency is that informed citizens would choose to receive health care from high quality, lower cost health care providers which would reward and shift more services to those health care providers with the best health care outcomes at the least expensive cost. If a crisis is perceived as worsening, such as an alarming increase in health care costs, it would likely raise the saliency level for the lawmaker and increase his/her likelihood to develop a new policy. In fact, Nice (1994, p. 33) stated that “a crisis, a deteriorating situation, or a vague perception that current performance is not satisfactory can spur decision making into researching new approaches, assessing their merits, and adopting those innovations that offer some prospect for improving the situation.” If “deteriorating situations” are brought to the attention of lawmakers, then the health care transparency issue is likely to be brought to forefront of lawmakers’

28 discussions as a relevant issue (Barrilleaux & Brace, 2001). Then, if politicians determine the state’s health care cost level as problematic, they may be respond by adopting innovative polices to address the “deteriorating situation.” If a crisis can spur innovation, as Nice (1994) espoused, then it is reasonable to expect that an alarming increase in health care costs provides lawmakers with the opportunity to promote an innovative policy in their states to resolve this issue. In addition, the public would demand the politicians to adopt a new policy to resolve the problem. Thus, in states where the saliency of this issue increases, these states will be more receptive to enacting an inpatient health care transparency law. Therefore, I hypothesize that

H3: The probability that a state will enact an inpatient health care transparency law increases as its rate of health care costs increases.

Legislative Ideology. The possible impact of the lawmaker’s ideological principles is another possible motivation to innovate. Earlier policy studies have noted “…the influence that legislative ideology has on state policy adoption” (Carter & LaPlant, 1997; Ka & Teske, 2002; Skocpol, 1993; Starr, 1982.) Accordingly, Barrilleaux, Brace, & Dangremond (1994, p. 28) argued “…that ideology was the most persistent force underlying state health reform efforts.” Additionally, researchers noted that when a policy proposal threatens a lawmaker’s ideological principles that the likelihood to adopt a policy to protect those principles increases. The influence of ideology on health care policy adoption may exist due to political party differences. Specifically, the differences between conservative and liberal perspectives regarding health care policy. Balla (2001, p. 234) described the differences in the political parties: “…in general, conservatives are less favorably disposed than liberals toward economic regulation, and this pattern typically holds in the health sector.” This was also supported by the writings of Goldstein (1997) and Johnson and Broder (1996). Additionally, Weissert and Weissert (1996) noted that “…liberals tend to support government regulation as a solution to market failures,” while “…conservatives are likely to point to government intervention as being one of the causes of market failure and…” to see increased governmental intervention in the market as an ideological threat.

29 If legislative ideology is a factor which motivates a state to enact a health care transparency law, then liberal lawmakers will be more likely to enact an inpatient health care transparency policy than conservative lawmakers. Therefore, I hypothesize that

H4: The probability that a state will enact an inpatient health care transparency law increases as its rate of state government liberalism increases.

Institute of Medicine Reports. The final motivator to innovate factor discussed includes the possible impact of the Institute of Medicine’s (IOM) pioneering reports on health care transparency. As previously discussed in Chapter two, the Institute of Medicine (IOM) is generally credited for kick-starting the transparency movement. The IOM’s two pioneering reports, the To Err is Human: Building a Safer Health System (2000) and Crossing the Quality Chasm: A New Health System for the 21st Century (2001). The To Err is Human report highlighted “…how tens of thousands of Americans die each year from medical errors and effectively put the issue of patient safety and quality on the radar screen of public and private policymakers” (Institute of Medicine, 2005, p.1). The Quality Chasm report defined six goals to reform health care delivery: “…care should be safe, timely, effective, efficient, equitable, and patient-centered (STEEEP)” (Institute of Medicine, 2005, p.1). If the Institute of Medicine’s pioneering reports are important motivators for the enactment of health care transparency laws, then we should expect for there to be an increase in the number of inpatient health care transparency law enactments after the publishing dates of the Institute of Medicine reports, To Err is Human and Quality Chasm, in 2000 and 2001 respectively. Therefore, I hypothesize that

H5: For all states, an inpatient health care transparency law is most likely to be enacted after the Institute of Medicine’s To Err is Human (2000) and the Quality Chasm (2001) reports were published.

Obstacles to Innovation and the Resources to Overcome Them The second group of variables studied, using the F.S. Berry and W.D. Berry (1999) Unified Model of State Policy Innovation explaining state policy adoption, concerns the obstacles to state policy adoption and the agents that provide the

30 resources for overcoming them. For this study, the “obstacles to inpatient health care transparency law enactment” and the “agents that provide resources to overcome them” variables include: unified party control of state government and state professional associations. These factors are described in the following pages. Unified Party Control of State Government. An important political resource to help lawmakers overcome obstacles to enact an inpatient health care transparency law is having unified party control of state government. According to Hansen (1983, pp. 153-154), when the “…governorship and both houses of the legislature are controlled by the same party, the state is more likely to adopt a policy than when the governmental institutions are under divided party control, regardless of which party is in power” (F.S. Berry & W.D. Berry, 1992, p.719). Hansen explained that this is because unified governments are able to avoid some of the barriers created when compromise between the two parties is necessary for policy adoption. Additionally, she maintained that the need for a unified government may be greater when attempting to adopt an unpopular or controversial policy, as unified governments may be more capable of achieving consensus than divided governments. Therefore, similar to Berry & Berry’s (1990, 1992) unified party control of government, I hypothesize that

H6: When a single political party controls the governorship and both houses of the legislature, the probability that the state will enact an inpatient health care transparency law is greater than when the government is under divided party control.

Professional Associations. The enactment of state inpatient health care transparency laws are likely to be influenced by state professional organizations. In 1994, F.S. Berry suggested that state policy adoptions may be influenced lawmakers who are members of national associations. Scholars have also reported the importance of the role that professional association networks play in policy adoption (McNeal et al., 2003). In this study, professional organizations may be an obstacle to inpatient health care transparency enactments. Organized constituencies may oppose a mandate requiring the collection and public reporting of inpatient health care data such as hospitals, physicians, or health plan associations, etc., whose true costs or levels of

31 performance may be exposed. Additionally, state officials concerned about reelection may be sensitive to any strongly held beliefs towards or against the enactment of an inpatient health care transparency law among the electorate.

H7: A state’s likelihood of enacting an inpatient health care transparency law decreases as the strength of its hospital and nursing home professional associations increases.

External Influences: The Diffusion of State Policy Previous Adoptions by Nearby States. The final group of factors studies is the “external influences” variables. External influences, such as the previous enactment of inpatient health care transparency laws by nearby states, may increase the likelihood of enactment of the inpatient health care transparency laws. According to Walker (1969), neighboring states influence policy adoptions. Walker explains that as states adopt new policies, information is available regarding the policy adopted that did not exist before. It follows that “…as neighboring states adopt the same or similar policies their adoptions provide even more information” (F.S. Berry & W.D. Berry, 1990). The neighboring states’ adoption of the same or similar policies acts as a resource that overcomes the obstacle of lack of information (F.S. Berry & W.D. Berry, 1990). While there are a number of different ways to define regional influence and determine a state’s influence on another, for this study, I use the F.S. Berry and W.D. Berry’s (1990) method of counting “…the number of neighboring states that have previously enacted…” an inpatient health care transparency law. “A ‘neighboring state’ is defined as one that shares a border with the state” under study (F.S. Berry & W.D. Berry, 1990). The assumption is that all of a state’s neighbors that have enacted an inpatient health care transparency law are influential in promoting innovation, and “…that there is pressure on a state to enact…” an inpatient health care transparency law “…as the number of neighboring states that have previously enacted…” an inpatient health care transparency law increases (F.S. Berry & W.D. Berry, 1990). Additionally, the states with more neighbors are a greater risk of enacting than others because they experience more peer pressure to adopt the new policy (F.S. Berry & W.D. Berry, 1990). F.S. Berry and W.D. Berry (1990, 1992), Boehmke and Witmer (2004), Hays and Glick (1997), and

32 Satterthwaite (2002) found support for neighboring influence using this method of measuring the influence of neighboring states. Therefore, I hypothesize that

H8: The probability of a state enacting an inpatient health care transparency law increases as the number of adjacent states enacting a similar law increases.

To recap the previous hypotheses, Table 4 presents the Unified Model of Inpatient Health Care Transparency Law Enactments with the study hypotheses.

Table 4. Unified Model of Inpatient Health Care Transparency Law Enactments with Study Hypotheses

THE MOTIVATION TO INNOVATE

The Proximity of State Elections H1: For all states, an inpatient health care transparency law is most likely to be enacted in an election year. In states with more than two years between gubernatorial elections, enactment is least likely in the year immediately following an election.

Short-Term Fiscal Health of the State Government H2: The worse the fiscal health of a state’s government—that is, the greater its expenditures relative to its revenues—the more likely the state is to enact an inpatient health care transparency law.

A Crisis in Health Care H3: The probability that a state will enact an inpatient health care transparency law increases as its rate of health care costs increases.

Legislative Ideology H4: The probability that a state will enact an inpatient health care transparency law increases as its rate of state government liberalism increases.

Institute of Medicine Reports H5: For all states, an inpatient health care transparency law is most likely to be enacted after the Institute of Medicine’s To Err is Human (2000) and the Quality Chasm (2001) reports were published.

OBSTACLES TO INNOVATION AND THE RESOURCES TO OVERCOME THEM

Unified Party Control of State Government H6: When a single political party controls the governorship and both houses of the legislature, the probability that the state will enact an inpatient health care transparency law is greater than when the government is under divided party control.

33 Table 4. Continued

Professional Associations H7: A state’s likelihood of enacting an inpatient health care transparency law decreases as the strength of its hospital and nursing home professional associations increases.

EXTERNAL INFLUENCES: THE DIFFUSION OF STATE POLICY

Previous Adoptions by Nearby States H8: The probability of a state enacting an inpatient health care transparency law increases as the number of adjacent states enacting a similar law increases.

The Description of the Variables Used in the Model, the Data Sources, and the Dataset Dependent Variable

ADOPTi,t is the theoretical dependent variable (or hazard rate) which identifies whether or not an inpatient health care transparency law was adopted in a given year, provided that the state has not adopted an inpatient health care transparency law in a previous year. ADOPTi,t is operationalized as a dichotomous variable equaling one if state i adopts during the specified year t, and equals zero if state i does not adopt during the specified year t. If a state adopts, the following state-years after the adoption are dropped. The years of the inpatient health care transparency law enactments by states provides the data necessary to measure ADOPTi,t. This information is available online at LEXIS-NEXIS, WESTLAW, National Association of Health Data Organizations (NAHDO), National Conference of State Legislatures (NCSL), and the state statute websites. To build a dataset for the ADOPT variable, I conducted an extensive review of the LEXIS-NEXIS, WESTLAW, National Association of Health Data Organizations (NAHDO), National Conference of State Legislatures (NCSL), and state statute websites to determine the year of the inpatient health care transparency law enactment for each state, if any. The data collected is presented in Table 5 and Table 6. Table 5 lists alphabetically the states which enacted an inpatient health care transparency law prior to and including 2006. Table 6 lists the same data chronologically. Additional

34 presentations of the year of inpatient health care transparency law enactment data are provided in Chapter four.

Table 5. States and the “Year of Table 6. States and the “Year of Inpatient Health Care Transparency Inpatient Health Care Transparency Law Enactment” (1971-2006), Law Enactment” (1971-2006), presented Alphabetically presented Chronologically Year State Year State No Mandate. Alabama 1971 California No Mandate. Alaska 1982 Maryland 1984 Arizona 1983 Maine 1995 Arkansas 1983 West Virginia 1971 California 1984 Arizona No Mandate. Colorado 1984 Illinois 1989 Connecticut 1984 Washington 1990 Delaware 1985 New Hampshire 1992 Florida 1985 Tennessee 1988 Georgia 1986 Pennsylvania No Mandate. Hawaii 1987 Nevada No Mandate. Idaho 1988 Georgia 1984 Illinois 1988 North Dakota 1993 Indiana 1988 Wisconsin 1996 Iowa 1989 Connecticut 1993 Kansas 1989 New Mexico 1993 Kentucky 1989 Rhode Island 1995 Louisiana 1990 Delaware 1983 Maine 1990 Utah 1992 Missouri 1992 Vermont No Mandate. Montana 1993 Indiana No Mandate. Nebraska 1993 Kansas 1987 Nevada 1993 Kentucky 1985 New Hampshire 1993 South Carolina 1992 New Jersey 1993 Virginia 1989 New Mexico 1995 Arkansas 2001 New York 1995 Louisiana 1995 North Carolina 1995 North Carolina 1988 North Dakota 1995 Texas No Mandate. Ohio 1996 Iowa 1992 Oklahoma 2001 New York 1991 Oregon 2002 Mississippi 1986 Pennsylvania 2006 Massachusetts

35 Table 5. Continued Table 6. Continued 1989 Rhode Island No Mandate. Alabama 1993 South Carolina No Mandate. Alaska No Mandate. South Dakota No Mandate. Colorado 1985 Tennessee No Mandate. Hawaii 1995 Texas No Mandate. Idaho 1990 Utah No Mandate. Michigan 1992 Vermont No Mandate. Minnesota 1993 Virginia No Mandate. Montana 1984 Washington No Mandate. Nebraska 1983 West Virginia No Mandate. Ohio 1988 Wisconsin No Mandate. South Dakota No Mandate. Wyoming No Mandate. Wyoming

Independent Variables There are nine independent variables proposed to test the eight hypotheses; however, PROFASSN1, PROFASSN2, and IOMRPT are not used in the final model. The development of the final model is described in Chapter four.

The Proximity to Elections. ELECT1i,t and ELECT2i,t are dummy variables used to identify the gubernatorial election cycle. “ELECT1 is operationalized as a dichotomous variable which equals one in the year of a gubernatorial election or zero if otherwise. ELECT2 is also operationalized as a dichotomous variable which equals one if it is neither the year of the gubernatorial election nor the year after the gubernatorial election, or zero if otherwise” (F.S. Berry & W.D. Berry, 1990). The election cycle data is available via two websites: 1) The Council of State Governors, Book of the States and 2) The Green Papers. To build the dataset, ELECT1 data are used to calculate ELECT2. First, to account for “neither the year of the gubernatorial election,” the coding is as follows: if ELECT1=1 for the current election year, then ELECT2=0 for the current election year. Next, to account for “nor the year after gubernatorial election,” the coding is as follows: if ELECT1=1 for the state’s previous election year data (if the same state), then ELECT2=0 for the current election year (the year after the election). To calculate ELECT2, 1970 data is included to act as the previous election year data in the dataset in order to compute the data for 1971.

36 The number of years between election cycles varies between states, and it also varies within a single state’s election cycle history. This can be problematic for some studies; however, due to the way the ELECT1 and ELECT2 variables are defined, this is not a problem for this study.

Fiscal Health. FISCALi,t-1 is a continuous variable which represents the fiscal health of a state’s government in the previous year. To control for size differences among the states, fiscal health is measured by calculating the difference between the “total state revenue” and the “total state spending” divided by the “total state spending.” The measurement is defined in F.S. Berry & W.D. Berry’s (1990) state lottery adoption study, and the data for the total state revenue and total state spending is available online via the U.S. Census Bureau website. The state revenue and state expenditures data are presented in thousands. When the fiscal data is negative, it means that the state spent more money than they had available that year. “The independent variables FISCAL and HCCRISIS are measured in the ‘previous year’ because legislative sessions typically begin in January;” thus, “…legislators must often make policy based on the prior year’s fiscal and economic data” (F.S. Berry & W.D. Berry, 1990).

A Crisis in Health Care. HCCRISISi,t-1 is a continuous variable which represents the percentage of the state budget dedicated to Medicaid, lagged for one year. In other words, it is the state’s Medicaid budget divided by the state’s total budget (both lagged by one year) and multiplied by 100 to convert to a percentage. The total state budget and state Medicaid budget data is available online at CMS within HHS and the Assistant Secretary for Planning and Evaluation within HHS. Upon review of the dataset, it became clear that this dataset was missing 1977 data for all states. I considered doing a mean substitution, but decided against it because the mean is well above the probable amount based on the data for the surrounding years. Therefore, I decided to average the figures for 1976 and 1978 for each state and enter the result for the missing data for 1977 for each state. The state expenditures data are presented in thousands, while the Medicaid expenditures data are in the millions. I multiplied the Medicaid expenditures by 1000 to

37 get the figures to be in similar units prior to computing the data for the HCCRISIS variable. Like FISCAL, “…HCCRISIS is measured in the “previous year” because legislative sessions typically begin in January.” Thus, the “…legislators must often make policy based on the prior year’s fiscal and economic data” (F.S. Berry & W.D. Berry, 1990).

Legislative Ideology. IDEOLOGYi,t is a continuous variable which measures the level of liberalism in the gubernatorial and legislative branches of a state’s government. Most measures of state ideology are measures of liberalism with decreasing levels of state policy acting as a measure of increasing conservatism. The state government liberalism data is available from the State Politics and Policy Quarterly (SPPQ) website’s database, which contains the data from the Berry, et al. (1998) “Measuring Citizen and Government Ideology in the American States” study. Of the three elite ideology datasets present, I used the one that cites the Berry, et al. (1998) study’s elite ideology measure. The IDEOLOGY data is presented using a 0-100 scale with the higher numbers representing increasing liberalism. Upon review of the dataset, I discovered that the IDEOLOGY dataset did not cover the entire time period of this study. The data is only available through 2004. Therefore, there was missing data for 2005 and 2006 for all of the states. This should not present a large problem for this study. The missing years are the concluding years of the study when most states have already adopted the inpatient health care transparency law and very few adoptions of the inpatient health care transparency law occurred. In fact, no states included in this study adopted in 2005 and only one state, Massachusetts, adopted in 2006.

Institute of Medicine (IOM) Reports. IOMRPT is a dichotomous variable which measures the influence of the Institute of Medicines’ reports, To Err is Human and Quality Chasm, after the second report’s publishing date of 2001, on inpatient health care transparency law enactments. To build the dataset, IOMRPT is coded with a one for year 2002 through and including 2006. The IOMRPT is coded as a zero in the years preceding 2002. In this

38 study, this variable does not have a lot of time to demonstrate its influence since 2006 is the last year included in this study.

Unified Party Control of State Government. “UNIFIEDPARTYi,t is a dichotomous variable which measures the degree to which a single political party controls the institutions of state government. UNIFIEDPARTY is operationalized as a dichotomous variable which equals one if both the governor and the two legislative houses are controlled by the same party (unified) and zero if there is split control” (Hansen, 1983, pp.153-54; F.S. Berry & W.D. Berry, 1990, p.403). The data is available online at the State Politics and Policy Quarterly (SPPQ) website’s database. Political party information for the governor and for the two legislative houses per state per year is available in the form of 0=Republican, .5=Split, and 1=Democrat. I decided to sum the three data elements (governor’s party, upper legislative house’s party, lower legislative house’s party). I decided that if the sum equaled 0 or 3, then UNIFIEDPARTY=1; thus, the governor and the two legislative houses were unified. If the sum of the three elements equaled anything between the values of 0 and 3, then UNIFIEDPARTY=0 indicating that there was split control of the governor and the two legislative houses. Upon review of the dataset, I found the same problem that I found with the IDEOLOGY dataset. The UNIFIEDPARTY dataset did not cover the entire time period for this study. The data is only available through 2004. Therefore, there is missing data for 2005 and 2006 for all of the states. “Additionally, it is important to note that the degree to which a single party controls the institutions of government cannot be measured for Minnesota and Nebraska in years with nonpartisan legislatures so these state-years were deleted from analyses using the UNIFIEDPARTY variable” (F.S. Berry & W.D. Berry, 1990).

Professional Associations. PROFASSN1i,t and PROFASSN2i,t are continuous variables which measure the hospital and nursing home professional associations’ influence on inpatient health care transparency law enactments. The Thomas and Hrebenar’ interest group rankings, located in the C.S. Thomas & R.J. Hrebenar chapters, “Interest Groups in the States” within Politics in the American States: A Comparative Analysis (5th, 6th, 7th, 8th, and 9th editions), are used to measure the

39 hospital and nursing home professional associations’ influence on inpatient health care transparency law enactments in states. Upon further examination of the data presented in the Thomas and Hrebenar chapters, I realized that the information did not include state-specific results. Therefore, I emailed Dr. Thomas and Dr. Hrebenar, and Dr. Thomas emailed me the state-specific data for each of the five years of interest group data collection. The data I received was state-specific, but not exclusive to hospital associations. Therefore, for each of the five sets of data (1989, 1995, 1998, 2002, 2006), I coded the data representing hospital and nursing associations’ influence on states. Then, I reconciled the data with the summary level data presented in the Thomas and Hrebenar chapters so that I could ensure consistency in the interpretation and application of the methods of categorizing the data. Next, I used the coded data and presented it in a table by state, effectiveness category, and year. Then, I removed Alaska and Hawaii from the risk pool. Finally, I used the point system that Thomas and Hrebenar used to rank the interest groups’ overall influence and I created rankings (“effectiveness per state per year”) of the influence of hospital and nursing home professional associations. The rankings were calculated by allocating 2 points for each “most effective” ranking, 1 point for each “second level of effectiveness” placement, and adding the totals for each state. Note that specific groups within an interest category sometimes appear within both the “most effective” and the “second level of effectiveness” category in a particular state and/or more than once in an effectiveness category. Therefore, it is possible to score six points per year per state. This process resulted in rankings of the influence of hospital and nursing home professional associations for each of the five years that Thomas and Hrebenar collected interest group data. It is important to note that Thomas and Hrebenar collected data during five distinct years: 1989, 1995, 1998, 2002, and 2006. To have data for the time period of 1989-2006 to analyze, I used the 1989 data for the years 1990-1994; 1995 data for years 1996-1997; 1998 data for 1999-2001; and 2002 data for 2003-2005. Additionally, there is no data to analyze for 1971-1988 because Thomas and Hrebenar did not begin to collect interest group ranking data until 1989 and this study begins in 1971. Therefore, I created two variables to analyze the influence of hospitals

40 and the nursing home professional associations: PROFASSN1 and PROFASSN2. The PROFASSN1 variable does not have data for the years 1971-1988; whereas, for the PROFASSN2 variable, I used the 1989 data for the years 1971-1988. To determine which professional association variable to use in the final model of this study, one model was created using PROFASSN1 and one model was created using PROFASSN2. They were assessed. The assessment of the models is described in Chapter four. To summarize, PROFASSN1 is a continuous variable that measures the influence of hospital and nursing home professional associations for the years 1989- 2006, inclusive; and PROFASSN2 is a continuous variable that measures the influence of hospital and nursing home professional associations for the years 1971-2006, inclusive.

Previous Enactments by Nearby States. NEIGHBORSi,t is a continuous variable that represents regional influence and is operationalized as the number of previously adopting neighbor states sharing a border with state i that had enacted an inpatient health care transparency law prior to year t (the year of measurement). “For this study, states are assumed to be neighbors of all states that share a border. Additionally, New Jersey and Maryland are treated as neighbors, and Massachusetts and Maine are treated as neighbors” (F.S. Berry & W.D. Berry, 1990). Finally, this study is examines only the contiguous states; therefore, Alaska and Hawaii are not included in this study. The first step in building the dataset for NEIGHBORS was to borrow directly from Berry & Berry’s (1990) state lottery adoption study where they define the neighboring states for each state, using the same assumptions as described above. Table 7 titled, “The 48 Continental U.S. States and Their Neighbors” provides a listing of the states and the neighbors used in this study.

Table 7. The 48 Continental U.S. States and Their “Neighbors” State The State’s Neighbors Alabama MS, TN, GA, FL Arizona CA, NV, UT, CO, NM Arkansas LA, TX, OK, MO, KY, TN, MS California OR, NV, AZ Colorado NM, AZ, UT, WY, NE, KS, OK

41 Table 7. Continued Connecticut NY, MA, RI Delaware MD, PA, NJ Florida AL, GA Georgia FL, AL, TN, NC, SC Idaho WA, OR, NV, UT, WY, MT Illinois WI, IA, MO, KY, IN, MI Indiana KY, IL, MI, OH Iowa MO, NE, SD, MN, WI, IL Kansas OK, CO, NE, MO Kentucky TN, AR, MO, IL, IN, OH, WV, VA Louisiana TX, AR, MS Maine NH, MA Maryland VA, WV, PA, DE, NJ Massachusetts RI, CT, NY, VT, NH, ME Michigan WI, IL, IN, OH Minnesota ND, SD, IA, WI, MI Mississippi LA, AR, TN, AL Missouri AR, OK, KS, NE, IA, IL, KY, TN Montana ID, WY, SD, ND Nebraska KS, CO, WY, SD, IA, MO Nevada CA, OR, ID, UT, AZ New Hampshire MA, VT, ME New Jersey DE, PA, NY, MD New Mexico AZ, UT, CO, OK, TX New York PA, NJ, CT, MA, VT North Carolina SC, GA, TN,VA North Dakota SD, MT, MN Ohio KY, IN, MI, PA, WV Oklahoma TX, NM, CO, KS, MO, AR Oregon CA, NV, ID, WA Pennsylvania DE, MD, WV, OH, NY, NJ Rhode Island CT, MA South Carolina GA, NC South Dakota ND, NE, WY, MT, MN, IA Tennessee NC, GA, AL, MS, AR, MO, KY, VA Texas NM, OK, AR, LA Utah AZ, NV, ID, WY, CO, NM Vermont NH, MA, NY Virginia NC, TN, KY, WV, MD Washington OR, ID West Virginia VA, KY, OH, PA, MD Wisconsin MN, IA, IL, MI Wyoming CO, UT, ID, MT, SD, NE SOURCE: Berry, F.S. & Berry, W.D. (1990, June). State Lottery Adoptions as Policy Innovations: An Event History Analysis. American Political Science Review, 84 (2), 395-415.

42 The final step in building the NEIGHBORS dataset was to calculate the total number of neighboring states that enacted an inpatient health care transparency law prior to the year of measurement per year from 1971 to 2006 for each state. To summarize, Table 8 displays the variable name, variable description, the corresponding hypothesis number, and data sources used in this dissertation. In the next section, I provide an overview of event history analysis (EHA), Cox regression, and a description of the statistical model.

Event History Analysis Survival analysis is the study of time-to-event data. The term survival analysis traces back to biomedical research. The methods originate from biomedical interests in studying morbidity and mortality; specifically: patients’ survival times between the time of diagnosis of certain disease and death. In different disciplines, survival analysis has different names. In the social sciences, it is called event history analysis. Economists call it duration analysis or transition analysis, and engineers call it lifetime or failure-time analysis (Guo, 2010, p.5). Event history analysis is a term for procedures analyzing duration-to-event data, where events are discrete occurrences (Garson, 2012, Parametric Survival Analysis (Event History Analysis), p.7). Additionally, an event history analysis has been described as “…an analysis of events occurring over time using a set of explanatory variables” (Agresti, A. & Finlay, B., 1997); an analytical method which uses longitudinal data to study the occurrence and timing of events (DesJardins, 2003); and an analytical technique whose goal is to explain an event (i.e., a change or transition from one state to another) “…that occurs in the behavior of an individual at a particular point in time” (Berry, F.S. & Berry, W.D., 1990, p.398). Coleman (1981) described an event history analysis as a method which evidences three fundamental characteristics. First, the data units (e.g., individuals, organizations, states) move along a finite series of states (i.e., statuses). Second, changes known as “…‘events’ may occur at any point in time” (Coleman, J., 1981, p.1). Third, the factors that influence events are either time- constant or time-dependent (Coleman, J., 1981, p.1). For this study, an event is defined

43 Table 8. Variable Name, Variable Description, Hypothesis Number, and Data Sources Variable Name and Variable Description Hyp. Data Source(s) Nos. ADOPT-The dichotomous dependent variable used to identify states that enacted an inpatient All LEXIS-NEXIS, WESTLAW, National health care transparency law during 1971 through and including 2006. Association of Health Data Organizations (NAHDO) website, National Conference of State Legislatures (NCSL) website, state statute websites.*

ELECT1-A dichotomous independent variable used to identify the gubernatorial election cycle. H1 The Council of State Governors, Book of the It equals one in the year of a gubernatorial election or zero if otherwise. States; The Green Papers.*

ELECT2-A dichotomous independent variable used to identify the gubernatorial election cycle. H1 The Council of State Governors, Book of the It equals one if it is neither the year of the gubernatorial election nor the year after the States; The Green Papers.* gubernatorial election, or zero if otherwise.

FISCAL-A continuous independent variable used to measure the fiscal health of a state’s H2 U.S. Census Bureau.* government in the previous year. It is measured by calculating the difference between the “total state revenue” and the “total state spending” divided by the “total state spending.” HCCRISIS-A continuous independent variable used to measure the percentage of the state’s H3 Office of the Actuary, CMS, HHS; Asst. Sec. budget dedicated to Medicaid, lagged for one year. In other words, it is the state’s Medicaid for Planning and Evaluation, HHS.* budget divided by the state’s total budget (both lagged for one year) and multiplied by 100 to convert to a percentage.

IDEOLOGY-A continuous independent variable used to measure a state government’s H4 State Politics and Policy Quarterly (SPPQ); liberalism in the gubernatorial and legislative branches. The IDEOLOGY data is presented Berry, et al. (1998).* using a 0-100 scale with the higher numbers representing increasing liberalism in the state.

IOMRPT-A dichotomous independent variable used to measure the influence of the IOM H5 The IOM reports, To Err is Human (2000) and reports, To Err is Human and Quality Chasm, after the second report’s publishing date of 2001. Quality Chasm (2001). UNIFIEDPARTY-A dichotomous independent variable used to measure the degree to which a H6 State Politics and Policy Quarterly (SPPQ).* single political party controls the institutions of state government. PROFASSN1-A continuous independent variable used to measure the hospital and nursing H7 Thomas, C.S., & Hrebenar, R.J.’s chapter, home professional associations’ influence for years 1989-2006, inclusive. “Interest Groups in the States” within Politics in the American States: A Comparative Analysis (5th, 6th, 7th, 8th, and 9th editions).* PROFASSN2-A continuous independent variable used to measure the hospital and nursing H7 Thomas, C.S., & Hrebenar, R.J.’s chapter, home professional associations’ influence for years 1971-2006, inclusive. “Interest Groups in the States” within Politics in the American States: A Comparative Analysis (5th, 6th, 7th, 8th, and 9th editions).*

NEIGHBORS-A continuous independent variable used to measure the number of states H8 F.S. Berry & W.D. Berry (1990). adjacent to a state that have previously enacted an inpatient health care transparency law. Selected years* = “Selected years” refer to the specific years each state enacted an inpatient health care transparency law during 1971 through and including 2006.

44 as an inpatient health care transparency law enactment by a state and the event occurs in the year that the state enacts the inpatient health care transparency law. Social science researchers, like biostatisticians, apply event history analysis to their research because “survival,” “failure,” and “risk” concepts are related to social science as well (Box-Steffensmeier, J. & Jones, B., 2004). In event history analysis, “survival time” is defined as the length of time until the event occurs (e.g., policy adoption). Next, “event history models focus on the ‘hazard function,’ which reflects the instantaneous probability that the event of interest will occur at a given time, provided that the unit of analysis has not experienced the event up to that time” (Garson, 2012, Parametric Survival Analysis (Event History Analysis), pp.7-8). While classic examples of survival analysis include duration to death or failure studies, event history analysis studies define their “hazards” with a more positive outlook and study duration to event “…adoption of an innovation in diffusion research…” or other subjects such as the longevity of events (Garson, 2012, Parametric Survival Analysis (Event History Analysis), p.8). The dependent variable in an event history analysis (“hazard rate”) represents the probability that a state will enact an inpatient health care transparency law during the period of observation (1971 through 2006), given the state is in the risk set at the time. A dichotomous observable variable is used to indicate whether a state adopts the law in a given year or not (coded 1 if the enactment occurred in a year, and 0 if the enactment did not occur). Event history allows for testing whether a state enacted an inpatient health care transparency law using pooled cross-sectional time series data. In this study, the 48 continental U.S. states are the units of analysis. Event history analysis requires a starting date to demark the beginning of a possible determinant’s influence on the enactment of a state law. To determine the start date for this study, I reviewed the states’ year of enactment for inpatient health care transparency laws. California was the first state to enact an inpatient health care transparency law in 1971. Therefore, I confined the analysis to inpatient health care transparency law enactments from 1971 and later, because it is reasonable to assume that no state is “at risk” of enacting a law prior to the year of enactment by the first state.

45 The “risk set” identifies the set of data units (e.g., states) in the sample that are “at risk” for experiencing the occurrence of the event. Therefore, since this study examines the 48 continental U.S. states, the risk set would exclude Alaska and Hawaii. Once a state enacts an inpatient health care transparency law, the state is no longer at risk of enacting the law. States who do not enact an inpatient health care transparency laws prior to the last year in my dataset (2006) are considered to remain at risk of enacting a law. As such, the dependent variable for California denotes a “1” in 1971. “The dependent variable for each of the remaining states consists of a series of “0”s beginning in 1971 and ending in the year before the state enacted an inpatient health care transparency law, followed by a “1” in the year of enactment” (F.S. Berry & W.D. Berry, 1990). After a state enacts a law that particular state’s cases (state-years) are dropped (Allison, 1984, p.16). States that do not enact an inpatient health care transparency law during the time period of 1971 through and including 2006 are treated in a consistent manner. The non-adopting states have a series of “0”s starting in 1971 and ending in 2006. There were ten states that did not enact an inpatient health care transparency law during the time period of this study (1971-2006, inclusive). The “survivor” states were Alabama, Colorado, Idaho, Michigan, Minnesota, Montana, Nebraska, Ohio, South Dakota, and Wyoming. The two states excluded from this analysis, Alaska and Hawaii, did not enact inpatient health care transparency laws within the timeframe of the study either. Event history analysis has numerous advantages over other standard methodologies used in innovation research. First, the EHA method allows for policy adoption to be explained by internal determinants and regional diffusion influences within a single model (F.S. Berry & W.D. Berry, 1990). Second, EHA allows the use of data that are simultaneously cross-sectional and longitudinal. Thus, the method analyzes both cross-sectional and temporal variation. Third, EHA examines both whether and when, in this study, an inpatient health care transparency law enactment occurred. Fourth, EHA allows the study of how the units of analysis transition from one status to another and how the possible determinants influence that change (McLendon, Hearn, & Deaton, 2005). Finally, EHA is a valuable technique for studying rare and

46 non-repeatable events occurring at a specific point in time, such as lottery adoptions (1990), a new tax (1992), or inpatient health care transparency law enactments. The event history analysis technique also has disadvantages. Agresti and Finlay (1997) describe two disadvantages that do not occur in ordinary regression modeling. The first disadvantage associated with using EHA is that the enactment of an inpatient health care transparency law will not have occurred yet for some states at the time of study. For this study, at the end of the study period in 2006, there were 38 states that have enacted an inpatient health care transparency law and ten states that have not enacted an inpatient health care transparency law (excluding Alaska and Hawaii). Again, the two states (Alaska and Hawaii) that were excluded from the analysis because they are not part of the 48 contiguous United States did not enact inpatient health care transparency laws either. These observations are referred to as being right- censored. The second complication factor associated with using EHA includes time-varying explanatory variables. Agresti and Finlay (1997) describe if “…the values for some of the variables change over the observed period of time,” it can be problematic. However, this should not cause any complications in this research because the variables that are being used are annual measures such as annual state budget figures, election cycle data, and neighboring states that enact an inpatient health care transparency law. These variables are generally measured once a year because they do not vary much during the span of a year. The event history analysis technique has another disadvantage. EHA has rigorous data requirements; therefore, it is possible that a researcher will not have all the data for all observations or over the entire time period. Additionally, the work involved to collect such information, when it is available, can present a very time- consuming task. Over the last decade and a half, event history analysis (EHA) has gained traction as an analytic method to study state policy adoption (F.S. Berry & W.D. Berry, 1990, 1992; Mintrom, 1997; Mooney, 2001; Wong & Shen, 2002; McLendon, Hearn, & Deaton, 2005).

47 The Statistical Model While this study is similar to Berry and Berry’s studies using event history analysis to predict state policy adoption in a given year and this study employs the use of Berry and Berry’s Unified Model of State Policy Innovation (1990, 1999) as the framework for the study hypotheses, this study differs from the Berry and Berry studies in the method selected to conduct the event history analysis. For policy adoption studies, a researcher may use a discrete-time or a continuous-time formulation of an event history analysis. Berry and Berry (1990, 1992, 1994), Mintrom (1994), and others who have used discrete-time models for an otherwise continuous-time process are correctly and accurately modeling the time path of policy adoption. Other political scientists have chosen a continuous-time model to study policy adoption, because it is accurate to presume that a legislature could adopt a policy anytime within a legislative session, have chosen an appropriate model as well (Steffensmeier & Jones, 1997, pp.1423-24; Box-Steffensmeier & Jones, 2004). For this study, the Cox proportional hazard model (Cox regression), a continuous-time model, was selected as the method for conducting the event history analysis. Cox regression was selected for three main reasons. First, this study lacks a “…strong, clear theoretical reason for positing a baseline hazard ratio…” for inpatient health care transparency law enactments by states (Box-Steffensmeier & Jones, 2004). Second, time-varying covariates or a combination of time-constant and time-varying covariates are not problematic in a Cox regression model. Third, Box-Steffensmeier & Jones (2004, p.85) repeatedly espoused that in most social science settings, the Cox regression model should generally be preferred over its alternatives, such as the parametric models or some of the discrete models. The combination of these reasons led to the selection of the Cox regression model for the method of conducting the event history analysis for this study. The basic Cox regression model is as follows: hi(t) = exp(β1x1i + β2x2i + …………+ βkxki,) h0(t)

Where hi(t) is the hazard rate for the ith state; h0(t) is the baseline hazard function; and β’x are the covariates and regression parameters.

48 Therefore, for this study, the Cox model for the adoption of inpatient health care transparency laws is as follows:

ADOPTi,t = exp(β1ELECT1i,t + β2ELECT2i,t + β3FISCALi,t-1 + β4HCCRISISi,t-1 + β5IDEOLOGYi,t + β6IOMRPTi,t + β7UNIFIEDPARTYi,t + β8PROFASSN2i,t + β9NEIGHBORSi,t)

Where the theoretical dependent variable (ADOPTi,t) is the probability that state i will adopt an inpatient health care transparency law in year t, given the state has not adopted an inpatient health care transparency law prior to year t. In the next chapter, Chapter four, the results of the statistical analysis and an explanation of the results as they pertain to of each of the hypotheses are provided.

49 CHAPTER FOUR

DATA ANALYSIS AND RESULTS

Introduction This chapter details the descriptive information regarding the enactment of inpatient health care transparency laws by states from 1971 through and including 2006. Then, the models are reviewed for multicollinearity, goodness-of-fit, and stability, and a final model is selected. Next, the hypotheses are examined using event history analysis, and the statistical analysis results are displayed in Tables 24-28. Finally, the hypotheses are interpreted and discussed.

Descriptive Information about the Enactment of Inpatient Health Care Transparency Laws by States As shown in Figure 1 and Table 9 below, the first inpatient health care transparency law enactment occurred in California in 1971 and then did not occur again until over ten years later, in 1982, by Maryland. In the early 1980s, a period of steady adoption began with more frequent adoption occurring by states in the late 1980s and early 1990s. The peak adoption years were in 1992 and 1993. The year 1996 marks the beginning of the decline in the number of adoptions of inpatient health care transparency law by states. The years 1971-1981, 1994, 1997-2000, and 2003-2005 were years where a state did not enact an inpatient health care transparency law during the study. Policy diffusion innovation theorists such as Rogers (1983) and others often refer to the “S” curve of innovation diffusion. The S-shaped curve depicts the innovation adopters graphed over time by the number of the total population adopting. It is referred to as a “S” curve because there is typically a few adopters at the beginning of the innovation time period followed by most of the adopters. Then, the number of adopters slows to only a few laggard innovators continuing to adopt. Thus, the curve on

50 the graph looks like the letter “S.” In Figure 1, the occurrence of adoption of inpatient health care transparency laws by states resembles the “S” curve. There is an early adopter in 1971, followed by a few more adopters in the early eighties. Then, the bulk of the adoptions occur in the late eighties and early nineties, followed by the adoptions lessening after the mid-nineties. At the conclusion of the study in 2006, ten states of 48 states had not adopted an inpatient health care transparency law. Table 10 identifies the ten non-adopting states included in this study, as well as, two non-adopting states that were excluded from the study (Alaska and Hawaii).

Cumulative Number of State Adopters Stateof Number Cumulative 10

1 1970 1975 1980 1985 1990 1995 2000 2005 Year State Adopted Inpatient Health Care Transparency Law

Figure 1. Line Graph of the Cumulative State Adoption of Inpatient Health Care Transparency Law during the Study (1971-2006)

51 Table 9. Year State Enacted an Inpatient Health Care Transparency Law by Decade during the Study (1971-2006), accompanied by the Number of Previous Enactments by Neighbors of Inpatient Health Care Transparency Laws and the States’ Total Number of Neighbors

1971 California (0 of 3)

1982 1983 1984 1985 1986 1987 1988 1989 Maryland (0 of 5) West Virginia (1 of 5) Arizona (1 of 5) Tennessee (0 of 8) Pennsylvania (2 of 6) Nevada (2 of 5) North Dakota (0 of 3) New Mexico (1 of 5) Maine (0 of 2) Washington (0 of 2) New Hampshire (1 of 3) Wisconsin (1 of 4) Rhode Island (0 of 3) Illinois (0 of 6) Georgia (1 of 5) Connecticut (0 of 3)

1990 1991 1992 1993 1995 1996 Delaware (2 of 3) Oregon (3 of 4) Vermont (1 of 3) Kansas (2 of 4) North Carolina (4 of 4) Iowa (3 of 6) Utah (3 of 6) Missouri (2 of 8) South Carolina (1 of 2) Texas (2 of 4) New Jersey (3 of 4) Indiana (1 of 4) Arkansas (4 of 7) Oklahoma (1 of 6) Kentucky (4 of 8) Louisiana (0 of 3) Florida (1 of 2) Virginia (3 of 5)

2001 2002 2006 New York (4 of 5) Mississippi (3 of 4) Massachusetts (6 of 6)

52 Table 10. States that Did Not Enact Inpatient Health Care Transparency Laws during the Study (1971-2006), accompanied by the States’ Total Number of Neighbors No Mandate. Alabama (4) No Mandate. Alaska (0)* No Mandate. Colorado (7) No Mandate. Hawaii (0)* No Mandate. Idaho (6) No Mandate. Michigan (4) No Mandate. Minnesota (5) No Mandate. Montana (4) No Mandate. Nebraska (6) No Mandate. Ohio (5) No Mandate. South Dakota (6) No Mandate. Wyoming (6) * = Not included in the study.

Descriptive Statistics Prior to reviewing the inferential statistics, I present Table 11 which displays the descriptive statistics for the complete dataset that was used in the event history analysis. Most variables have 1126 state-years (cases). This is calculated by multiplying the 48 states by the number of years until adoption of an inpatient health care transparency law per state, including the adoption year (not 48 states multiplied by 36 years in the study). Thus, the study dataset is right-censored. Three of the variables are missing data; therefore, there are not 1126 state-years for those variables. For IDEOLOGY, there are only 1104 state-years. This is because the dataset did not contain data for the years 2005 and 2006. Therefore, there are 22 missing state-years. Ten states did not adopt, and one state, Massachusetts adopted in 2006, but does not drop out of the dataset until 2007.

Table 11. Descriptive Statistics for Dataset Used in the Event History Analysis Std. Variable Name n Min. Max. Mean Dev. Skewness Kurtosis YEAR 1126 1971 2006 ADOPT 1126 0 1 .03 .18 ELECT1 1126 0 1 .26 .44

53 Table 11. Continued ELECT2 1126 0 1 .47 .50 FISCAL 1126 -.28 1.25 .08 .09 2.1 2.8 HCCRISIS 1126 0 15.60 3.53 2.26 1.55 4.1 IDEOLOGY 1104 0 95.04 46.87 21.98 .04 -.8 IOMRPT 1126 0 1 .05 .22 UNIFIEDPARTY 1075 0 1 .49 .50 PROFASSN1 316 0 3 .68 .84 .76 -.86 PROFASSN2 1126 0 3 .48 .74 1.2 .05 NEIGHBORS 1126 0 6 .84 1.33 1.6 1.9

For UNIFIEDPARTY, there are only 1075 state-years. This is because Nebraska was coded as missing data for all years and Minnesota was coded as missing data for 1971-1975 because it had a nonpartisan legislature during these years. Also, the dataset did not contain data for 2006. Eleven states did not adopt, and one state, Massachusetts adopted in 2006, but does not drop out of the dataset until 2007. Therefore, the calculation is 1126 state-years less 36 state-years, less six state-years, less nine state-years equals 1075 state-years. The explanation for the calculation is the following: thirty-six state-years were subtracted because Nebraska was a non-adopter during the thirty-six years of the study. Six state-years were subtracted because Minnesota was nonpartisan during 1971-1975 (5 state-years) plus one more state-year for 2006 because Minnesota was a non-adopter during the study. Nine state-years were subtracted because there were eleven non-adopting states in 2006 (except Massachusetts which adopted in 2006 but does not drop out of the dataset until 2007) less the two states (Nebraska and Minnesota) because they were already counted as not adopting for 2006 previously in the calculation. For PROFASSN1, there are only 316 state-years. The dataset includes states with data from years 1989 – 2006, inclusive. The dataset excludes states that have adopted from 1971-1988, inclusive. Fourteen states adopted an inpatient health care transparency law prior to 1989. Ten states did not adopt an inpatient health care transparency law during the study years (1971-2006, inclusive), and one state, Massachusetts adopted in 2006, but does not drop out of the dataset until 2007. Therefore, the PROFASSN1 dataset only describes 23 states, less than half of the

54 states included in this 48-state study. Additionally, this data only describes 18 years (1989-2006, inclusive) of this 36 year study. Therefore, the PROFASSN1 dataset only describes half of the years of the study period.

Multicollinearity Assessment of the Data It is important to assess the integrity of the analytical model through analysis of how the independent variables correlate with one another. Variables that “overlap” considerably are said to correlate with one another (Agresti & Finlay, 1997). In a multiple regression model, if two or more independent variables are highly correlated to each other, then one can choose which of the highly correlated variables to include and exclude the other(s) from the model. This situation, referred to as multicollinearity, can cause inflated standard errors for estimates of regression parameters (Agresti & Finaly, 1997). Multicollinearity can be problematic because it may cause results which result in an inaccurate description of the influence of the independent variables on the dependent variable. Testing for multicollinearity involves multiple steps. These steps were performed on the EHA data for years 1971-2006 inclusive. The following narrative and tables describe the multicollinearity test results.

Correlation Matrix A correlation matrix, presented in Table 12, provides the first look at the data used in this study and is the first step to assess for multicollinearity. This information is reviewed for variables that exhibit a high level of correlation (e.g., >.8). As shown in Table 12, only one statistically significant high correlation between two variables is present. PROFASSN1 is highly correlated with PROFASSN2. In fact, they are perfectly correlated. This was expected because the PROFASSN1 dataset is a subset of the PROFASSN2 dataset. Therefore, only one of the two variables may be used in the final model. PROFASSN2 was initially selected because it contained more cases. Thus, only PROFASSN2 is used in the remaining tests for multicollinearity.

55 Table 12. Correlation Matrix of the Data (1971-2006) UNIFIEDPARTY UNIFIEDPARTY PROFASSN1 PROFASSN1 PROFASSN2 NEIGHBORS NEIGHBORS IDEOLOGY IDEOLOGY HCCRISIS HCCRISIS IOMRPT IOMRPT ELECT2 ELECT2 FISCAL

ELECT1 -.56** .03 .02 -.00 .06* -.00 -.05 -.04 .01 ELECT2 .01 .03 -.024 -.02 -.02 .06 .06 .04

FISCAL .03 -.13** -.08** -.08** -.08 -.02 .18**

HCCRISIS .24** .28** -.12** .34** .34** .57**

IDEOLOGY -.14** -.03 .04 .12** -.05

IOMRPT .01 .04 .08** .41**

UNIFIEDPARTY .09 .11** -.09**

PROFASSN1 1.00** .41**

PROFASSN2 .27**

** = Correlation is significant at the 0.01 level (2-tailed). * = Correlation is significant at the 0.05 level (2-tailed).

Tolerance and Variance Inflation Factor (VIF) Statistics While the correlation matrix is the first step to provide information about potential problems, more tests are conducted to check for problems with multicollinearity. To perform more multicollinearity diagnostics for this study, a linear regression analysis is conducted. This is an acceptable method because the predictors are the only concern. Therefore, it does not matter what the dependent variable is for this analysis because the other output information generated is ignored. Thus, it is acceptable to perform any form of regression analysis for the multicollinearity diagnostics. Accordingly, a linear regression analysis is performed to look at the tolerance and variance inflation factor (VIF) statistics for the model. To interpret tolerance, if the tolerance value for any variable is less than 0.10, there may be a problem with multicollinearity. As shown in Table 13, none of the variables have tolerance values less than 0.10; therefore, no variable appears to signify a problem with multicollinearity. Variance inflation factor (VIF) statistics have a direct inverse relationship to tolerance statistics. If the VIF for any variable is greater than ten, there may be a problem with multicollinearity. As shown in Table 13, none of the variables have VIF

56 values that are greater than ten; therefore, no variable appears to indicate a problem with multicollinearity.

Table 13. Tolerance and Variance Inflation Factor (VIF) Variable Name Tolerance VIF ELECT1 .70 1.44 ELECT2 .70 1.44 FISCAL .85 1.17 HCCRISIS .59 1.70 IDEOLOGY .85 1.18 IOMRPT .78 1.28 UNIFIEDPARTY .95 1.05 PROFASSN2 .83 1.21 NEIGHBORS .58 1.73 Dependent Variable = Adopt

Condition Index and Variance Proportions The calculation of the data’s condition index and the variance proportions is the final test used to assess for problems with multicollinearity. To interpret condition index, a condition index over 30 may indicate a problem with multicollinearity and warrant further review. If further review is warranted, the variance proportions are examine to determine if any two variables have a variance proportion greater than 0.75 which would show a problem with multicollinearity. As displayed in Table 14, none of the variables have a condition index of over 30; therefore, none of the variables have multicollinearity problems. After reviewing the multicollinearity diagnostic findings cumulatively, none of the variables exhibited multicollinearity problems in any of the tests that were conducted; therefore, there are no multicollinearity problems present in this study.

Model Assessment An event history analysis was conducted using Cox regression in SPSS. The analysis was performed multiple times prior to determining the final model for this study.

57 In the first set of models tested, an analysis was conducted to determine which professional association variable to use in the final model. The analysis was conducted using Model 1 which includes PROFASSN1, along with the other variables (ELECT1,

Table 14. Condition Index and Variance Proportions Variance Proportions

UNIFIEDPARTY PROFASSN2 PROFASSN2 NEIGHBORS NEIGHBORS IDEOLOGY IDEOLOGY HCCRISIS HCCRISIS IOMRPT IOMRPT ELECT1 ELECT2 FISCAL FISCAL

Condition Index 1.00 .00 .01 .01 .01 .00 .00 .01 .01 .01 2.12 .00 .00 .03 .00 .00 .42 .01 .01 .06 2.28 .34 .12 .00 .00 .00 .00 .00 .01 .00 2.72 .00 .01 .12 .00 .00 .10 .23 .01 .16 2.87 .00 .03 .11 .00 .00 .11 .01 .64 .00 3.45 .04 .05 .26 .03 .05 .01 .43 .02 .02 3.89 .01 .03 .28 .02 .00 .28 .19 .26 .37 4.56 .47 .56 .05 .06 .12 .04 .00 .01 .08 6.55 .00 .01 .01 .85 .34 .00 .03 .02 .30 9.42 .12 .18 .14 .03 .48 .04 .10 .01 .00 Dependent Variable = Adopt

ELECT2, FISCAL, HCCRISIS, IDEOLOGY, UNIFIEDPARTY, NEIGHBORS), and IOMRPT. Then, the analysis was conducted using Model 2 which includes PROFASSN2, along with the other variables, and IOMRPT. After testing, it was determined that the IOMRPT variable needed to be dropped from future models due to convergence problems and a determination about which professional association variable to use in the final model could not be made yet. In the second set of models tested, an analysis was conducted using models that did not contain the IOMRPT variable. The analysis was conducted using Model 3 which includes PROFASSN1 with the other variables, but excludes IOMRPT and PROFASSN2. Then, the analysis was conducted using Model 4 which includes PROFASSN2 with the other variables, but excludes IOMRPT and PROFASSN1. In this

58 analysis, a determination was made about which professional association variable to use in the final model. However, due to concerns about potential measurement errors with the use of the PROFASSN1 and PROFASSN2 variables, a final model was developed and tested that excludes the professional association variables. Model 5 tests the other variables (ELECT1, ELECT2, FISCAL, HCCRISIS, IDEOLOGY, UNIFIEDPARTY, NEIGHBORS), but excludes PROFASSN1, PROFASSN2 and IOMRPT. At the conclusion of the analysis of Model 5, it was determined that Model 5 would serve as the final model for this study.

Assessment of Model 1: PROFASSN1, all variables, and IOMRPT An event history analysis was conducted using Cox regression for Model 1, which includes PROFASSN1, the other variables (ELECT1, ELECT2, FISCAL, HCCRISIS, IDEOLOGY, UNIFIEDPARTY, NEIGHBORS) including IOMRPT, but excluding PROFASSN2. Model 1:

ADOPTi,t = exp(β1ELECT1i,t + β2ELECT2i,t + β3FISCALi,t-1 + β4HCCRISISi,t-1 + β5IDEOLOGYi,t + β6IOMRPTi,t + β7UNIFIEDPARTYi,t + β8PROFASSN1i,t + β9NEIGHBORSi,t)

As you may recall, PROFASSN1 is a continuous variable that measures the influence of hospital and nursing home professional associations for the years 1989- 2006, inclusive; and PROFASSN2 is a continuous variable that measures the influence of hospital and nursing home professional associations for the years 1971-2006, inclusive. Immediately upon running the analysis, one can see that the IOMRPT variable is problematic. First, there is a warning/error that states “since coefficients did not converge, no further models will be fitted.” Upon reviewing the coefficients, the IOMRPT is easily identified as being too large. The IOMRPT variable has a coefficient of -12.22 with a corresponding odds ratio of zero. Additionally, the 95% confidence for the odds ratio is 0 to 1.76 x 10163 (i.e., infinity). Again, the value associated with IOMRPT is too large. This is evidence of a convergence problem.

59 Assessment of Model 2: PROFASSN2, all variables, and IOMRPT An event history analysis was conducted using Cox regression for Model 2, which includes PROFASSN2, the other variables (ELECT1, ELECT2, FISCAL, HCCRISIS, IDEOLOGY, UNIFIEDPARTY, NEIGHBORS) including IOMRPT, but excluding PROFASSN1. Model 2:

ADOPTi,t = exp(β1ELECT1i,t + β2ELECT2i,t + β3FISCALi,t-1 + β4HCCRISISi,t-1 + β5IDEOLOGYi,t + β6IOMRPTi,t + β7UNIFIEDPARTYi,t + β8PROFASSN2i,t + β9NEIGHBORSi,t)

Again, immediately upon running the analysis, one can see that the IOMRPT variable is problematic. The convergence error appears again. Upon reviewing the coefficients, the IOMRPT is easily identified again as being too large. The IOMRPT variable has a coefficient of -13.39 with a corresponding odds ratio of zero. Additionally, the 95% confidence for the odds ratio is 0 to 4.339 x 10231 (i.e., infinity). Again, the value associated with IOMRPT is too large. This is evidence of a convergence problem.

Cross Tab of ADOPT and IOMRPT To further confirm that the IOMRPT variable is problematic and should be dropped from the model, a cross tabulation of ADOPT and IOMRPT was run. Thirty- eight states enacted the inpatient health care transparency law during the study period of 1971–2006, inclusive. Of those 38 states, only two states that enacted the inpatient health care transparency law may be attributed to the effect of the IOMRPT variable (2002-2006, inclusive). Additionally, only two state-years out of 1126 state-years may have been enacted due to the effect of the IOMRPT variable. This very small number of state-years causes convergence problems. The model cannot converge. Therefore, it is necessary to drop the IOMRPT variable from the model.

Assessment of Model 3: PROFASSN1, all variables, without IOMRPT An event history analysis was conducted using Cox regression for Model 3, which includes PROFASSN1, the other variables (ELECT1, ELECT2, FISCAL,

60 HCCRISIS, IDEOLOGY, UNIFIEDPARTY, NEIGHBORS), but excluding IOMRPT and PROFASSN2. Model 3:

ADOPTi,t = exp(β1ELECT1i,t + β2ELECT2i,t + β3FISCALi,t-1 + β4HCCRISISi,t-1 + β5IDEOLOGYi,t + β6UNIFIEDPARTYi,t + β7PROFASSN1i,t + β8NEIGHBORSi,t)

The following tables report the Cox regression results of the factors influencing a state to enact an inpatient health care transparency law. In Table 15, a summary of the data is provided for Model 3. The “event” is the number of states that enacted an inpatient health care transparency law during the study period of 1971-2006, inclusive. Therefore, in this model, 23 states enacted an inpatient health care transparency law during the study period of 1971-2006, inclusive. There were 1126 cases (a.k.a. state- years) when a state was eligible to enact an inpatient health care transparency law during the years 1971 through and including 2006. A total of 278 state-years, representing 24.7% of the dataset, were used in this model’s event history analysis. For the remaining cases, 75.3% (848 missing state-years) were dropped from the analysis due to missing values within the dataset for this study. The missing values are described in Chapter three.

Table 15. Case Summary for Model 3: Using PROFASSN1 without IOMRPT n Percent Cases available Event 23 2% in analysis Censored 255 22.6% Total 278 24.7% Cases dropped Cases with missing values 848 75.3% Cases with negative time 0 0% Censored cases before the earliest event 0 0% in a stratum Total 848 75.3% Total 1126 100% Dependent Variable = YEAR -2 Log Likelihood = 187.51

61 In a statistical model, the goodness-of-fit describes how well the model fits a set of observations. Specifically, it depicts the difference between the observed values and the expected values under the proposed model. “The Likelihood ratio test of the overall model is called the omnibus test” (Garson, 2012, Cox Regression, p.37). The -2 log likelihood (a.k.a. -2LL) statistic appears in the “Omnibus Tests” tables in SPSS. A smaller -2LL value is better because it indicates a better fit of model. “If -2LL is significant, then the model as a whole is significant. This means that at least one of the covariates contributes significantly to the explanation of the duration to event. It also means the model is significantly better than the null model, which is the model when all covariates are 0” (Garson, 2012, Cox Regression, p.37).

Table 16. Omnibus Tests of Model Coefficients, Model 3, Block 0 -2 Log Likelihood 231.62 a Beginning Block 0

Table 17. Omnibus Tests of Model Coefficients, Model 3, Block 1 Change From Change From -2 Log Likelihood Overall (score) Previous Step Previous Block Chi- Chi- Chi- square df Sig. square df Sig. square df Sig. 187.51 40.81 8 .00 44.11 8 .00 44.11 8 .00 a Beginning Block 0, initial Log Likelihood function: -2 log likelihood: 231.62 b Beginning Block 1. Method=Enter

As shown in Table 17, the -2 log likelihood value=187.51 for Model 3. While this number is greater than zero, it is difficult to interpret the meaning of the score and the goodness-of-fit for this statistical model because there is no upper limit for the -2 log likelihood statistic. In this analysis, the -2 log likelihood provides some information about the model’s fit, but since it includes the entire universe of state-years, it is not affected like a sample would, but rather, only as independent variables are included or excluded from the model. Adding or excluding independent variables to the model changes the -2 log likelihood value and changes the fit of the model. A decrease in the -2 log likelihood

62 signifies a better fit of the model because at least one of the changes in the variables improved the predictive power of the model (Agresti & Finlay, 1997). Model 3’s Cox regression results are presented in Table 18. In Model 3, the variables UNIFIEDPARTY, NEIGHBORS, and IDEOLOGY are statistically significant at the p<.05 level, p<.01, and p<.001 level respectively. The PROFASSN1 variable is not statistically significant, nor are the remaining variables: ELECT1, ELECT2, FISCAL, and HCCRISIS.

Table 18. Output for Model 3: Using PROFASSN1 without IOMRPT Explanation Variables B SE Wald df Sig. Exp(B) Motivation to ELECT1 -.42 .69 .37 1 .544 .66 Innovate ELECT2 .35 .53 .44 1 .509 1.42 FISCAL 2.33 1.42 2.69 1 .101 10.31 HCCRISIS -.17 .11 2.57 1 .109 .84 IDEOLOGY .07 .02 21.52 1 .000*** 1.07 Obstacles to UNIFIEDPARTY -1.08 .52 4.34 1 .037* .34 Innovation and the PROFASSN1 .35 .30 1.32 1 .251 1.42 Resources to Overcome Them External Influences: NEIGHBORS -.68 .20 12.01 1 .001** .51 The Diffusion of State Policy * = p<.05 level, ** p<.01 level,*** = p<.001 level

Assessment of Model 4: PROFASSN2, all variables, without IOMRPT An event history analysis was conducted using Cox regression for Model 4, which includes PROFASSN2, the other variables (ELECT1, ELECT2, FISCAL, HCCRISIS, IDEOLOGY, UNIFIEDPARTY, NEIGHBORS), but excluding IOMRPT and PROFASSN1. Model 4: ADOPTi,t = exp(β1ELECT1i,t + β2ELECT2i,t + β3FISCALi,t-1 + β4HCCRISISi,t-1 + β5IDEOLOGYi,t + β6UNIFIEDPARTYi,t + β7PROFASSN2i,t + β8NEIGHBORSi,t)

The following tables report the Cox regression results of the factors influencing a state to enact an inpatient health care transparency law. In Table 19, a summary of the

63 data is provided for Model 4. There were 1126 cases (a.k.a. state-years) when a state was eligible to enact an inpatient health care transparency law during the years 1971 through and including 2006. A total of 1065 state-years, representing 94.6% of the data, were used in this model’s event history analysis. For the remaining cases, 5.4% (61 missing state-years) were dropped from the analysis due to missing values within the dataset for this study. It is important to note that the model using PROFASSN1, described in the section above, gives a less than accurate representation of the dataset due to the large percentage of missing values (75.3%). For example, the model using PROFASSN1 depicts only 23 states as adopters of an inpatient health care transparency law during the study period of 1971-2006 inclusive; whereas, the model using PROFASSN2, which uses a much more complete dataset, shows 37 states as adopters of an inpatient health care transparency law for the years. The model using PROFASSN2 provides a more accurate representation of the dataset due to the small percentage of missing values (5.4%).

Table 19. Case Summary for Model 4: Using PROFASSN2 without IOMRPT N Percent Cases available Event 37 3.3% in analysis Censored 1028 91.3% Total 1065 94.6% Cases dropped Cases with missing values 61 5.4% Cases with negative time 0 0% Censored cases before the earliest event 0 0% in a stratum Total 61 5.4% Total 1126 100% Dependent Variable = YEAR -2 Log Likelihood = 350.39

As aforementioned, the goodness-of-fit describes how well the model fits a set of observations and -2 log likelihood is a measure of goodness-of-fit. “If -2LL is significant, then the model as a whole is significant. This means that at least one of the covariates contributes significantly to the explanation of the duration to event” (Garson, 2012, Cox

64 Regression, p.37). As presented in Table 21, the -2 log likelihood value=350.39 for Model 4. Again, while this number is greater than zero, it is difficult to interpret the meaning of the score and the goodness-of-fit for this statistical model because there is no upper limit for the -2 log likelihood statistic.

Table 20. Omnibus Tests of Model Coefficients, Model 4, Block 0 -2 Log Likelihood 402.55 a Beginning Block 0

Table 21. Omnibus Tests of Model Coefficients, Model 4, Block 1 Change From Change From -2 Log Likelihood Overall (score) Previous Step Previous Block Chi- Chi- Chi- square df Sig. square df Sig. square df Sig. 350.39 43.60 8 .00 52.17 8 .00 52.17 8 .00 a Beginning Block 0, initial Log Likelihood function: -2 log likelihood: 402.55 b Beginning Block 1. Method=Enter

Model 4’s Cox regression results are presented in Table 22. In Model 4, the variables HCCRISIS, NEIGHBORS, and IDEOLOGY are statistically significant at the p<.05 level, p<.01, and p<.001 level respectively. The variable PROFASSN2 is not statistically significant, nor are the remaining variables: ELECT1, ELECT2, FISCAL, and UNIFIEDPARTY. Table 22. Output for Model 4: Using PROFASSN2 without IOMRPT Explanation Variables B SE Wald df Sig. Exp(B) Motivation to ELECT1 -.17 .49 .12 1 .725 .84 Innovate ELECT2 .18 .41 .21 1 .651 1.20 FISCAL .89 1.44 .38 1 .536 2.43 HCCRISIS -.19 .10 3.95 1 .047* .83 IDEOLOGY .05 .01 26.67 1 .000*** 1.05 Obstacles to UNIFIEDPARTY -.70 .37 3.63 1 .057 .50 Innovation and the PROFASSN2 -.14 .25 .30 1 .582 .87 Resources to Overcome Them

65 Table 22. Continued External Influences: NEIGHBORS -.53 .15 12.03 1 .001** .59 The Diffusion of State Policy * = p<.05 level, ** p<.01 level,*** = p<.001 level

Comparison of Model 3 and Model 4 Model 3 and Model 4 were reviewed to determine which model to use as the final model for this study. As previously mentioned, Model 3 tests the variable PROFASSN1, the other variables (ELECT1, ELECT2, FISCAL, HCCRISIS, IDEOLOGY, UNIFIEDPARTY, NEIGHBORS), but excludes IOMRPT and PROFASSN2. Model 4 tests the variable PROFASSN2, the other variables (ELECT1, ELECT2, FISCAL, HCCRISIS, IDEOLOGY, UNIFIEDPARTY, NEIGHBORS), but excludes IOMRPT and PROFASSN1. Model 3 and Model 4 were reviewed for multicollinearity issues. During the testing of Model 1 and Model 2 for muliticollinearity issues, the IOMRPT variable exhibited problems with convergence. Therefore, the IOMRPT variable was excluded from all future models. Once the IOMRPT variable was removed, the models no longer had convergence issues. Thus, Model 3 and Model 4 were determined to be without muliticollinearity issues. Model 3 and Model 4 were reviewed for goodness-of-fit. The -2 log likelihood value=187.51 for Model 3, and the -2 log likelihood value=350.39 for Model 4. While both -2LL values were greater than zero, both models had similar difficulties assessing the score’s meaning and the goodness-of-fit for the statistical model because there it lacked an upper boundary for the -2 log likelihood statistic. Finally, the results from the two models were reviewed. First, all of the coefficient signs of the variables in Model 3 were compared to the coefficients in Model 4, and the coefficient signs were consistent for all of the variables. If the variable in Model 3 exhibited a positive coefficient, then the same variable in Model 4 exhibited a positive coefficient. Second, PROFASSN1 and PROFASSN2 variables were reviewed for statistical significance, and the professional association variable in each model was not statistically significant. Third, in Model 3, UNIFIEDPARTY, NEIGHBORS, and IDEOLOGY are statistically significant at the p<.05 level, p<.01, and p<.001 level

66 respectively. In Model 4, the variables HCCRISIS, NEIGHBORS, and IDEOLOGY are statistically significant at the p<.05 level, p<.01, and p<.001 level respectively. Therefore, both Model 3 and Model 4 have three variables that are statistically significant, with NEIGHBORS and IDEOLOGY as statistically significant in both models at the same p-value levels. Since the Omnibus Tests of the Model Coefficients were stable for both models and the PROFASSN1 and PROFASSN2 variables were not statistically significant for either model, the deciding factor to choose the Model 4 over Model 3 is that Model 4 had more data in its dataset. Remember, the PROFASSN1 data used in Model 3 is a subset of the PROFASSN2 data used in Model 4. When more data is available to analyze, it increases the power of the statistical tests. Furthermore, if Model 3 was chosen, 14 states would have been excluded from the analysis and only 32 of the 1126 state-years (28%) would have been analyzed; whereas, Model 4 includes all of the states and all 1126 state-years in the analysis. Therefore, Model 4 was selected as the final model. That is, Model 4 was selected as the final model until the possibility of measurement errors associated with using either of the professional association variables was raised as a potential problem. The selection of a final model for this study was put on hold until further analysis was conducted.

Introduction and Assessment of Model 5: All variables, without PROFASSN1, PROFASSN2, and IOMRPT To address possible measurement errors with using the variables PROFASSN1 and PROFASSN2 and their respective datasets, Model 5 was developed and additional analyses were conducted. There may be possible measurement errors due to the use of only five years of professional association data to populate the years with missing data for the PROFASSN1 and PROFASSN2 variables for the 36 year study. To determine whether strong correlation and stability exists among the professional association data collection years, which if present would help justify the use of data from adjacent data collection years to populate years with missing data for the professional association variables, a correlation analysis was conducted for the five distinct years of professional association data. As shown in Table 23, the correlations

67 were not consistent. There were medium to medium-strong correlation among each of the adjacent data collection years, such as 1989 and 1995, 1995 and 1998, 1998 and 2002, and 2002 and 2006. However, non-adjacent years, such as 1989 and 2006, had very little correlation.

Table 23. Correlations Output for 5 Distinct Years of Professional Association Data (1989, 1995, 1998, 2002, 2006)

YEAR 1995 1995 YEAR 1998 YEAR 2002 YEAR 2006 YEAR

YEAR 1989 .48** .40** .19 .24

.62** .37** .32* YEAR 1995

.67** .35* YEAR 1998

.54** YEAR 2002

YEAR 2006 ** = Correlation is significant at the 0.01 level (2-tailed). * = Correlation is significant at the 0.05 level (2-tailed).

Due to the results of the correlation analysis and neither of the professional association variables having significant Cox regression results, another model was developed which excludes the professional association variables. Consequently, an event history analysis was conducted using Cox regression for Model 5, which includes the other variables (ELECT1, ELECT2, FISCAL, HCCRISIS, IDEOLOGY, UNIFIEDPARTY, NEIGHBORS), but excludes IOMRPT, PROFASSN1, and PROFASSN2. Model 5: ADOPTi,t = exp(β1ELECT1i,t + β2ELECT2i,t + β3FISCALi,t-1 + β4HCCRISISi,t-1 + β5IDEOLOGYi,t + β6UNIFIEDPARTYi,t + β7NEIGHBORSi,t)

The following tables report the Cox regression results of the factors influencing a state to enact an inpatient health care transparency law. In Table 24, a summary of the data is provided for Model 5. There were 1126 cases (a.k.a. state-years) when a state

68 was eligible to enact an inpatient health care transparency law during the years 1971 through and including 2006. A total of 1065 state-years, representing 94.6% of the data, were used in this model’s event history analysis. For the remaining cases, 5.4% (61 missing state-years) were dropped from the analysis due to missing values within the dataset for this study.

Table 24. Case Summary for Model 5: Without PROFASSN1, PROFASSN2, and IOMRPT N Percent Cases available Event 37 3.3% in analysis Censored 1028 91.3% Total 1065 94.6% Cases dropped Cases with missing values 61 5.4% Cases with negative time 0 0% Censored cases before the earliest event 0 0% in a stratum Total 61 5.4% Total 1126 100% Dependent Variable = YEAR -2 Log Likelihood = 350.7

As presented in Table 26, the -2 log likelihood value=350.7 for Model 5. While this number is greater than zero, as previously mentioned, it is difficult to assess the score’s meaning and the goodness-of-fit for the statistical model because there it lacked an upper limit for the -2 log likelihood statistic.

Table 25. Omnibus Tests of Model Coefficients, Model 5, Block 0 -2 Log Likelihood 402.55 a Beginning Block 0

69 Table 26. Omnibus Tests of Model Coefficients, Model 5, Block 1 Change From Change From -2 Log Likelihood Overall (score) Previous Step Previous Block

Chi- Chi- Chi- square df Sig. square df Sig. square df Sig. 350.7 43.58 7 .00 51.85 7 .00 51.85 7 .00 a Beginning Block 0, initial Log Likelihood function: -2 log likelihood: 402.55 b Beginning Block 1. Method=Enter

Model 5’s Cox regression results are presented in Table 27. In Model 5, the variables HCCRISIS and UNIFIEDPARTY are statistically significant at the p<.05 level and IDEOLOGY and NEIGHBORS are statistically significant at the p<.001 level. The remaining variables, ELECT1, ELECT2, and FISCAL, are not statistically significant.

Table 27. Output for Model 5: Without PROFASSN1, PROFASSN2, and IOMRPT Explanation Variables B SE Wald df Sig. Exp(B) Motivation to ELECT1 .18 .49 .13 1 .723 1.19 Innovate ELECT2 -.17 .41 .17 1 .685 .85 FISCAL .92 1.44 .41 1 .524 2.51 HCCRISIS -.20 .09 4.37 1 .037* .82 IDEOLOGY .05 .01 26.43 1 .000*** 1.05 Obstacles to UNIFIEDPARTY .74 .36 4.18 1 .041* 2.09 Innovation and the Resources to Overcome Them External Influences: NEIGHBORS -.56 .15 13.90 1 .000*** .57 The Diffusion of State Policy * = p<.05 level, ** p<.01 level,*** = p<.001 level

Table 28. Covariate Means for Model 5: Without PROFASSN1, PROFASSN2, and IOMRPT Covariate Variable Means ELECT1 .74 ELECT2 .53 FISCAL .08

70 Table 28.Continued HCCRISIS 3.44 IDEOLOGY 47.18 UNIFIEDPARTY .51 NEIGHBORS .80

Comparison of Model 3, Model 4, and Model 5 Model 3, Model 4, and Model 5 were reviewed to determine which model to use as the final model for this study. As previously mentioned, Model 5 tests the other variables (ELECT1, ELECT2, FISCAL, HCCRISIS, IDEOLOGY, UNIFIEDPARTY, NEIGHBORS), but excluding IOMRPT, PROFASSN1, and PROFASSN2. Model 5 was reviewed for multicollinearity issues. Since Model 3 and Model 4 were determined to be without muliticollinearity issues, the removal of the professional association variables does not change the determination. Therefore, Model 5 was determined to be without multicollinearity issues. Model 5 was reviewed for goodness-of-fit. The -2 log likelihood value=350.7 for Model 5. The results were similar for Model 3, Model 4, and Model 5. While the numbers were greater than zero, all three models had similar difficulties assessing the score’s meaning and the goodness-of-fit for the statistical model because there it lacked an upper limit for the -2 log likelihood statistic. Finally, the results from the Model 3, Model 4, and Model 5 were reviewed. First, with the professional association variables removed for Model 5, when the analysis was conducted, some of the coefficient signs of the variables changed. For example, where ELECT1 and UNIFIEDPARTY exhibited negative coefficients in Model 3 and Model 4, the same variables exhibited positive coefficients in Model 5. Additionally, where ELECT2 exhibited a positive coefficient in Model 3 and Model 4, the same variable exhibited a negative coefficient in Model 5. This means that the presence of the professional variable was making the model unstable and/or that there might have been some measurement issues with the professional association variables. Second, PROFASSN1 and PROFASSN2 variables were reviewed for statistical significance, and the professional association variables were not statistically significant

71 in Model 3 and Model 4. However, it is possible that the professional association variables did not have significant results due to a measurement error resulting from the use of data from five distinct data collection years to populate the years with missing data. Third, Model 3, Model 4, and Model 5 were reviewed for statistical significance of their variables as shown in Table 29. In Model 3, UNIFIEDPARTY, NEIGHBORS, and IDEOLOGY are statistically significant at the p<.05 level, p<.01, and p<.001 level respectively. In Model 4, the variables HCCRISIS, NEIGHBORS, and IDEOLOGY are statistically significant at the p<.05 level, p<.01, and p<.001 level respectively. In Model 5, the variables HCCRISIS and UNIFIEDPARTY are statistically significant at the p<.05 level and IDEOLOGY and NEIGHBORS are statistically significant at the p<.001 level. Therefore, when Model 5 is compared with Model 3 and Model 4, the number of statistically significant variables increases from three to four, and the p-value levels either are the same or improve in Model 5.

Table 29. Comparison of Output from Model 3, Model 4, and Model 5 Model 3 Model 4 Model 5 Explanation Variables B Sig. B Sig. B Sig. Motivation to ELECT1 -.42 .544 -.17 .725 .18 .723 Innovate ELECT2 .35 .509 .18 .651 -.17 .685 FISCAL 2.33 .101 .89 .536 .92 .524 HCCRISIS -.17 .109 -.19 .047* -.20 .037* IDEOLOGY .07 .000*** .05 .000*** .05 .000*** Obstacles to UNIFIEDPARTY -1.08 .037* -.70 .057 .74 .041* Innovation and the PROFASSN1 .35 .251 N/A N/A N/A N/A Resources to Overcome Them PROFASSN2 N/A N/A -.14 .582 N/A N/A External Influences: NEIGHBORS -.68 .001** -.53 .001** -.56 .000*** The Diffusion of State Policy * = p<.05 level, ** p<.01 level,*** = p<.001 level

Upon review of the three models, there is more stability in the coefficients than one might think possible given the results of the professional association correlation

72 analysis reported in Table 23. The HCCRISIS values range from just missing the statistically significant threshold in Model 3 to significant in Model 4 and Model 5, and this occurs within a fairly narrow range of values. This also occurs for the UNIFIEDPARTY variable, where the values range from narrowly missing the statistically significant threshold in Model 4 to significant in Model 3 and Model 5. Additionally, the IDEOLOGY and NEIGHBORS variables report remarkably similar values for Model 3, Model 4, and Model 5. Therefore, the models are fairly similar. Yet, the results of the professional association correlation analysis confirm some possible measurement problems which may have created instability in Model 3 and Model 4; thus, justifying the omission the PROFASSN1 and PROFASSN2 variables from the final model. This information coupled with the increase in the number of statistically significant variables from three to four and the p-value levels remaining the same or improving when Model 3 and Model 4 when compared to Model 5, the selection of Model 5 as the final model to be used for this study is well justified. Therefore, the final model for this study includes the variables ELECT1, ELECT2, FISCAL, HCCRISIS, IDEOLOGY, UNIFIEDPARTY, and NEIGHBORS, but excludes IOMRPT, PROFASSN1, and PROFASSN2.

Model 5 (Final Model): ADOPTi,t = exp(β1ELECT1i,t + β2ELECT2i,t + β3FISCALi,t-1 + β4HCCRISISi,t-1 + β5IDEOLOGYi,t + β6UNIFIEDPARTYi,t + β7NEIGHBORSi,t)

Results of the Statistical Analysis and Explanation by Hypothesis In this section, the hypotheses proposed in this research are interpreted and discussed with regards to the results of the event history analysis for the final model of this study. As previously mentioned, the final model used in this study includes the variables ELECT1, ELECT2, FISCAL, HCCRISIS, IDEOLOGY, UNIFIEDPARTY, and NEIGHBORS, but excludes IOMRPT, PROFASSN1, and PROFASSN2. Final Model: ADOPTi,t = exp(β1ELECT1i,t + β2ELECT2i,t + β3FISCALi,t-1 + β4HCCRISISi,t-1 + β5IDEOLOGYi,t + β6UNIFIEDPARTYi,t + β7NEIGHBORSi,t)

73 To review, the Cox regression tests the theoretical factors of policy adoption and diffusion based on the following explanations: 1) the motivation to innovate (Hypotheses 1-5), 2) obstacles to innovation and resources to overcome them (Hypotheses 6-7), and 3) external influences: the diffusion of state policy (Hypothesis 8). The results for the final model are presented in Tables 24-28. The following is a description of the findings for each hypothesis, followed by a discussion.

The Motivation to Innovate The following is a description of the findings for the five hypotheses that test “motivation to innovate” variables. Then, a discussion follows addressing all five hypotheses simultaneously. The Proximity of State Elections. Hypothesis one, first part, an inpatient health care transparency law is most likely to be enacted by states in a gubernatorial election year, is not supported because ELECT1 is not statistically significant (p=.72). Therefore, the likelihood of a state enacting an inpatient health care transparency law is not affected by whether the state is in a gubernatorial election year. Hypothesis one, second part, an inpatient health care transparency law is least likely to be enacted by states in the year following a gubernatorial election by states with more than two years between gubernatorial elections, is not supported because ELECT2 is not statistically significant (p=.69). Therefore, the likelihood of a state enacting an inpatient health care transparency law is not affected by whether, for states with more than two years between gubernatorial elections, the state is in the year following the gubernatorial election year. Thus, the likelihood of a state enacting an inpatient health care transparency law is not affected by the proximity of the state elections. Short-Term Fiscal Health of the State’s Government. Hypothesis two, an inpatient health care transparency law is more likely to be enacted in states the worse the fiscal health of a state’s government (i.e., the greater its expenditures relative to its revenues), is not supported because FISCAL is not statistically significant (p=.52).

74 Therefore, the likelihood of a state enacting an inpatient health care transparency law is not affected by the short-term fiscal health of the state’s government. A Crisis in Health Care. Hypothesis three, an inpatient health care transparency law is more likely to be enacted in states as the rate of health care costs increase, is not supported because the variable HCCRISIS is statistically significant at p<.05 level (HCCRISIS: p=.04), but is statistically significant in the opposite direction than predicted. The hypothesis is not supported because the variable has a Beta of -.2 indicating a negative relationship between the rate of health care costs and a state enacting an inpatient health care transparency law. Thus, the likelihood of a state enacting an inpatient health care transparency law decreases as the rate of health care costs increase. Legislative Ideology. Hypothesis four, an inpatient health care transparency law is more likely to be enacted in a state as its rate of state government liberalism increases, is supported because the variable IDEOLOGY is statistically significant. The variable IDEOLOGY is statistically significant at p<.001 level (IDEOLOGY: p=.000) and has a Beta of .05 indicating a positive relationship between the rate of state government liberalism and a state enacting an inpatient health care transparency law. As indicated by the odds ratio, for every one unit increase in the IDEOLOGY variable (the score on a 0-100 scale of state government liberalism), the state is 1.05 times more likely to enact an inpatient health care transparency law. In other words, for every one unit increase in the IDEOLOGY variable, the likelihood of a state enacting an inpatient health care transparency law increases by 5.0%. Thus, the hypothesis is supported and the likelihood of a state enacting an inpatient health care transparency law increases as its rate of state government liberalism increases. Institute of Medicine Reports. The result for hypothesis five, an inpatient health care transparency law is more likely to be enacted after the Institute of Medicine’s To Err is Human (2000) and the Quality Chasm (2001) reports were published, is unknown because the variable IOMRPT had to be removed from the model due to convergence problems. Discussion of “Motivation to Innovate” Results. Of the five hypotheses that test “motivation to innovate” variables, two hypotheses had statistically significant

75 variables: HCCRISIS and IDEOLOGY. However, only the IDEOLOGY hypothesis is supported. The health care crisis hypothesis (HCCRISIS) is statistically significant, but in the opposite direction than predicted. As posited by Nice (1994), if a crisis exists and is perceived as worsening, instead of improving, the motivation to innovate is likely to increase. Applying this theory to the worsening health care financial and quality crisis, it was hypothesized that the probability that a state will enact an inpatient health care transparency law increases as its rate of health care costs increases. However, as mentioned, the HCCRISIS variable is statistically significant, but in an opposite direction than predicted. The Cox regression results indicated a statistically significant, negative relationship between the rate of health care costs and a state enacting an inpatient health care transparency law. Therefore, for every 1% increase in the HCCRISIS variable (the percent of the state’s budget dedicated to Medicaid), the likelihood of a state enacting an inpatient health care transparency law decreases by 17.9%. Perhaps instead of a crisis encouraging the adoption of innovation as several researchers advocated (Carter & LaPlant, 1997; Gray, 1973; Gray & Lowery, 1990; Nice, 1994; Savage, 1985; Stream, 1997; Walker; 1969), when the state is in the midst of a crisis, the specific solution being proposed matters. Perhaps when the enactment of an inpatient health care transparency law is proposed as the solution to the health care crisis, a solution associated with high-dollar, web-based, database technologies, it is possible that policymakers and their constituents believe that they cannot afford the solution. With a health care crisis measured as the percent of the state’s budget dedicated to Medicaid, it may be that the states with a high percent of their state budget dedicated to Medicaid are the states that can least afford a solution to the health care crisis which requires a large upfront and ongoing monetary commitment. Additionally, it could be that states with a high percent of their state budget dedicated to Medicaid are states that must focus their efforts on resolving their ailing Medicaid programs rather than expend their efforts implementing health care transparency policies which address more than just Medicaid programs. The second “motivation to innovate” variable that is statistically significant is the IDEOLOGY variable. The legislative ideology hypothesis proposes that an inpatient

76 health care transparency law is more likely to be enacted in a state as its rate of state government liberalism increases is supported. The variable IDEOLOGY is statically significant at p<.001 level (IDEOLOGY: p=.000) and has a Beta of .05 indicating a positive relationship between the rate of state government liberalism and a state enacting an inpatient health care transparency law. This result lends support to Barrilleaux, Brace, & Dangremond’s (1994, p.28) statement “…that ideology was the most persistent force underlying state health reform efforts.” Surprisingly, the traditional “motivation to innovate” variables for political and economic factors such as ELECT1, ELECT2, and FISCAL were not statistically significant for inpatient health care transparency law enactment. Interestingly, these variables are often statistically significant for policy adoptions for other health care policies and in other public policy areas. While inpatient health care transparency bills were likely to be endorsed by lawmakers during an election year to increase their chances for reelection because providing the consumer-taxpayer with health care information, lowering health care costs, and improving health care quality were perceived as being desired among constituents, ELECT1 and ELECT2 were not statistically significant. Low saliency of the health care transparency policy with the voters is the reason why ELECT1and ELECT2 were not statistically significant, although there is some evidence that health care transparency policies are popular. As previously mentioned, the “motivation to innovate” variable IOMRPT, measuring the influence of two groundbreaking Institute of Medicine reports, was removed from the study due to convergence issues. This was not a surprise since the IOMRPT dataset included only four years of data for this 36 year study and those years were the later years of the study period. Due to the nature of event history analysis, since most of the states had already adopted an inpatient health care transparency law and were censored from the study by the time the IOMRPT data started its iterations, it is understandable that there were convergence problems when the variable IOMRPT was included in the model. Obstacles to Innovation and the Resources to Overcome Them

77 The following is a description of the findings and a discussion for the hypotheses that test the “obstacles to innovation and the resources to overcome them” variables. Unified Party Control of State Government. Hypothesis six, an inpatient health care transparency law is more likely to be enacted in a state when a single political party controls the governorship and both houses of the legislature, is supported because the variable UNIFIEDPARTY is statistically significant. The variable UNIFIEDPARTY is statistically significant at the p<.05 level (UNIFIEDPARTY: p=.04) and has a Beta of .74 indicating a positive relationship between unified party control of state government and a state enacting an inpatient health care transparency law. Additionally, UNIFIEDPARTY has an odds ratio of 2.09, which means that an inpatient health care transparency law is 2.09 times more likely to be enacted in a state when a single political party controls both the governor and the two legislative houses of the state legislature. Thus, the hypothesis is supported and the likelihood of a state enacting an inpatient health care transparency law increases when a single political party controls the governorship and both houses of the legislature. Professional Associations. The result for hypothesis seven, an inpatient health care transparency law is less likely to be enacted as the strength of its hospital and nursing home professional associations increases, is unknown because the variables PROFASSN1 and PROFASSN2 had to be removed from the study due to possible measurement problems which may have created instability issues within the models. Discussion of “Obstacles to Innovation and Resources to Overcome Them” Results. As expected for the traditional variable of public policy innovation and diffusion studies, the “obstacles to innovation and the resources to overcome them” variable, UNIFIEDPARTY, is statistically significant. The unified party hypothesis that an inpatient health care transparency law is more likely to be enacted in a state when a single political party controls the governorship and both houses of the legislature is supported. The variable UNIFIEDPARTY is statistically significant at the p<.05 level (UNIFIEDPARTY: p=.04) and has a Beta of .74 indicating a positive relationship between unified party control of state government and a state enacting an inpatient health care transparency law. Thus, the hypothesis is supported. This result provides support for Hansen’s viewpoint that having unified party control of state government is

78 an important political resource for state officials to overcome obstacles to innovation (Hansen, 1983, pp. 153-154; F.S. Berry & W.D. Berry, 1992, p.719). As previously mentioned, the “obstacles to innovation and resources to overcome them” variables PROFASSN1 and PROFASSN2, measuring the influence of the hospital and nursing home professional associations, were removed from the study due to possible measurement problems which may have created instability issues within the models. In retrospect, this was not a complete surprise because a mere five years of professional association data were used to populate years with missing data for the PROFASSN1 and PROFASSN2 variables for this 36 year study.

External Influences: The Diffusion of State Policy The following is a description of the findings and a discussion for the hypothesis that tests the external influence variable: the previous adoptions by nearby states (NEIGHBORS). Previous Adoptions by Nearby States. Hypothesis eight, an inpatient health care transparency law is more likely to be enacted in a state as the number of adjacent states enacting similar laws increases, is not supported. While the variable NEIGHBORS is statistically significant at p<.001 level (NEIGHBORS: p=.000), it has a Beta of -.56 indicating a negative relationship between the number of neighbors previously adopting an inpatient health care transparency law and a state enacting an inpatient health care transparency law. As indicated by the odds ratio, for every one unit increase in the NEIGHBORS variable (the number of states adjacent to the state that have previously enacted an inpatient health care transparency law), the likelihood of a state enacting an inpatient health care transparency law decreases by 42.6%. Interestingly, like the HCCRISIS variable, the NEIGHBORS variable is significant, but in the opposite direction than predicted. Discussion of “External Influences” Results. The results for the variable NEIGHBORS testing external influence are quite interesting. While the NEIGHBORS hypothesis is not supported, like the HCCRISIS variable, the NEIGHBORS variable is significantly significant but it is statistically significant in the opposite direction than predicted. The NEIGHBORS variable is statically significant with a negative coefficient.

79 This is contrary to what is predicted in current diffusion literature. A possible explanation for the reverse finding is that neighboring states witnessed the stakeholder arguments and the enormous state financial commitment associated with the policy and decided not to enact the inpatient health care laws. As shown in Table 9 and Table 10, it is clear that initially states follow a diffusion pattern other than the neighbor diffusion model. In fact, the states with more neighbors who have previously adopted an inpatient health care transparency law either enacted an inpatient health care transparency law later than their neighbors or they never enact an inpatient health care transparency law. An example of this behavior is Massachusetts which finally enacted an inpatient health care transparency law in 2006 after all six of its neighbors previously enacted an inpatient health care transparency law by 2002. This result has interesting implications for innovation and diffusion theory. In the next chapter, Chapter five, a case study of the National Association of Health Data Organizations (NAHDO) is presented. I provide a focused review of literature about diffusion strategies and the practical politics of professional associations to determine their role and importance in state inpatient health care transparency law enactments. Then, I conduct a qualitative analysis of professional associations using three techniques. Finally, I summarize the qualitative analysis results and compare them to the review of the professional association diffusion literature.

80 CHAPTER FIVE

NAHDO: A CASE STUDY

Introduction During the earlier chapters of this study, I conducted a quantitative analysis of professional associations to analyze their influence on state inpatient health care transparency law enactments. However, I was unable to determine their influence because the professional association variables had to be dropped from the study due to measurement issues. Therefore, the purpose of this chapter is to conduct a qualitative analysis of a professional association, specifically the National Association of Health Data Organizations (NAHDO), to determine the influence of professional associations on state inpatient health care transparency law enactments. During the quantitative analysis, the professional association variables represented the influence of hospital and nursing home interest groups; whereas, during the qualitative analysis, NAHDO is the professional association whose influence on state policy adoption was examined. In this chapter, I provide a focused review of literature about diffusion strategies and the practical politics of professional associations to determine their role and importance in state inpatient health care transparency law enactments. Then, I conduct a qualitative analysis of professional associations using three techniques to determine what their role was and whether it was consistent with the literature. The three techniques that I use to conduct the qualitative analysis include: 1) a descriptive history of NAHDO where I examine its role as a diffusion agent and the diffusion process strategies it employs; 2) a qualitative content analysis of primary data from NAHDO’s reports and newsletters that capture strategies and practices it has used to educate its members about health care transparency laws; and 3) telephone interviews of three of NAHDO’s key stakeholders to gather first-hand knowledge of

81 diffusion strategies, practical politics, and other activities NAHDO has implemented to support states in their pursuit to enact inpatient health care transparency laws. Finally, I summarize and discuss the qualitative analysis results and compare them to the review of the professional association diffusion literature.

Diffusion Strategies and Practical Politics of Professional Associations: A Focused Review of Literature In 1969, Walker first introduced the concept of professional associations as a method of diffusing policy ideas. He “…described the increased prominence of professional associations and the emergence of interstate and federal agencies devoted to facilitating communication between state officials” (Karch, 2003, p. 159). Walker (1969) argued that professional associations were beginning to serve as modes of communication to facilitate the diffusion of policy innovations, replacing “…outdated modes of communication based on regional and cultural ties.” Walker stated, “Decision makers in the states seem to be adopting a broader, national focus based on new lines of communication which extend beyond regional boundaries.” Additionally, scholars have observed that some professional associations specialize in facilitating communication between officials who are responsible for formulating and implementing policy. Several scholars, including Gray (1994), Grupp & Richards (1975), Savage (1975), and Walker (1969, 1971) have posited that these organizations are in part responsible for increasing the speed of state policy diffusion, as well as, obscuring the regional diffusion patterns. To illustrate, Gray (1996) observed that a large number of health care policy professional associations “…facilitate the diffusion of policy innovations and replace the impact of conventional factors such as state innovativeness, regional ties, and federal incentives.” While most have not been able to demonstrate empirically, numerous scholars have asserted that professional associations affect policy diffusion. In 1994, Gray stated that “…there have been almost no empirical examinations of the impact of professional associations.” During the same year, F.S. Berry (1994) urged for this omission to be addressed and described three reasons why it is important to study professional associations. First, F.S. Berry stated that if professional association

82 participation affects policy diffusion, then a study that does not take this participation into account would be incomplete. Second, examinations of professional associations would add information to policy innovation and diffusion literature. The third reason to study professional associations is that professional associations are important institutions that warrant study. It is important to study professional associations because there are many professional associations that exert influence on professional practices and policy in their professional areas. Thus, they warrant study which focuses on how they affect the diffusion of innovation. Scholars have posited that professional association participation facilitates communication beyond neighboring and regional states. Kile (2005) contends that there are two different types of professional associations or interest group networks which influence state policy diffusion: the policy assistance interest groups and stakeholder interest groups. Policy assistance interest groups act as state policy information clearinghouses and provide resources to state policymakers. Some of the resources which they use to influence state policies include legislative tool kits, “best practices” and “lessons learned” policy briefs, and state policy strategy committees. Policy assistance interest groups exist to serve the policymakers. Therefore, the main purpose of these groups is to connect policymakers to one another in order to exchange policy-related ideas. The National Conference of State Legislatures (NCSL) is an example of a policy assistance group. The National Governors Association (NGA) is another prominent policy assistance group. It provides serves the governors and their policy advisors with information such as “best practices” and “lessons learned” from other governors (National Governors Association, 2004). As with the NCSL, the main purpose of the group is to provide networking opportunities to encourage the spread of policies among states. The interactions among the policymakers can be unstructured such as informal conversations or they can be structured such as formal policies. The amount of participation in these state groups seems to matter, as well (F.S. Berry, 1994). F.S. Berry (1994) evidenced “…that the greater the participation of specific state officials in these associations, the more likely the state is to adopt a policy” (p. 453).

83 The second type of professional association is the organized stakeholder interest groups. These stakeholder groups are able to influence policy diffusion with their resources within each of the states and via a coordinated national strategy for state- level policy to protect their constituencies. For the current study, I focus on policy assistance interest groups. Most professional associations view their main purpose as providing policy information to the state officials who are interested in a specific program, as evidenced by their organizations’ mission statements. To accomplish their mission, professional associations serve as information clearinghouses. They provide information about new state program and provide updates through the use of websites, state liaisons, task forces, and policy briefs. Generally, they sponsor regular meetings and host conferences that bring state policymakers together to disseminate information about recent developments in their states. At the meetings and conferences, lawmakers frequently discuss new programs with each other in order to learn about policy outcomes and political feasibility. They make connections and form bonds with other attendees, and they further disseminate policy-relevant information. Often, lawmakers use these new relationships to develop new legislation. For example, lawmakers may borrow language from the statutes of other states to use in the development of legislative proposals for their own states. Additionally, professional associations publish documents that help to diffuse policy information describing how policy innovations and providing examples through newsletters, policy briefs, position papers, and reports. Lawmakers rely on these publications as they develop legislation and attempt to enact policies. Additionally, professional associations use recent technological and institutional changes, such as the use of email, the Internet, and other technological advances to improve the timeliness and the capacity of the dissemination of policy information to their interested parties. As a result of this review of literature, I expect to find evidence of professional associations serving as policy diffusion agents during the qualitative analysis. The review of literature provided numerous references to professional associations disseminating policy innovation information through their advocacy, education, and

84 networking activities. Additionally, there were examples of professional associations hosting conferences; producing publications such as “best practices” and “lessons learned” policy briefs, legislative tool kits, and model state laws; and providing online resources to disseminate policy information beyond regional boundaries.

National Association of Health Data Organizations (NAHDO): A Descriptive History Introduction A descriptive history of the National Association of Health Data Organizations (NAHDO) enriches this study by providing a narrative account of a professional association to supplement the quantitative information provided via the event history analysis. NAHDO is the focus of the descriptive history for the current study because “NAHDO is in the unique position of being the only organization of its kind, bringing together members of the public and private sectors of the health information industry” (NAHDO website, 2/2/2012). In other words, NAHDO is the only national professional association dedicated to health care data issues. As such, a case study of this professional association provides a relevant descriptive history for the present study and can help answer my research question regarding policy diffusion.

NAHDO: Role as a Diffusion Agent & Diffusion Strategies “In the spring of 1986, the National Association of Health Data Organizations (NAHDO) was established as a result of a consensus recommendation developed at a meeting sponsored by the Washington Business Group on Health (WBGH) and the Intergovernmental Health Policy Project (IHPP) at George Washington University. The consensus recommendation established an association of state health data organizations (SDOs) for the purpose of supporting activities of state-level agencies that are mandated to collect, analyze, disseminate, and use hospital discharge datasets” (NAHDO website, 2/2/2012). Additionally, the association was directed to discuss and promote change in the following areas: 1) To increase uniformity of the data being collected, its coding, and accessibility; 2) To broaden the data collection to include outpatient and other non-hospital-based utilization; 3) To discuss the merits and the

85 methodology of physician-specific data collection; and 4) To provide technical assistance, “best practices,” and “lessons learned” to new and current state data organizations (NAHDO News, Spring 2005). Two years later, in 1988, “…NAHDO became a private, not-for-profit, national educational and charitable membership organization (501-c-3)” (NAHDO website, 2/2/2012). The following year, “in 1989, the NAHDO Board of Directors expanded their membership to include organizations and individuals from the private for-profit and not-for-profit sectors” (NAHDO website, 2/2/2012). In October 2001, the NAHDO Board adopted its first vision and mission statements for the association. The vision stated: “Improved health through a health- information rich society.” The original mission adopted stated: In order to accomplish our vision, NAHDO recognizes that state health data agencies are an essential component of our nationwide information infrastructure. NAHDO works to strengthen state health data agencies by:  Developing close working relationships with public and private state health agencies in order to be in a position when opportunity arises to help them increase capacity;  Working with private and federal partners to nurture and strengthen state health data agency infrastructure;  Bringing groups together to learn from each other and/or to work together;  Sponsoring and supporting work to increase the consistency and predictability of statewide health data;  Promoting standardization in data elements and methods of collecting, analyzing, and dissemination of data;  Identifying the gaps in publicly available data and promoting the closure of these gaps; and  Advocating for the state health data agency role in state and national health policy development (NAHDO Annual Report 2009–2010).

It is readily apparent. NAHDO has stayed true to its original purpose. “The current mission statement and objectives are remarkably similar to the original vision and mission which were developed over twenty-five years ago” (NAHDO website, 2/2/2012). The current mission statement for NAHDO reads as follows: The National Association of Health Data Organizations (NAHDO) is a national, not-for-profit membership organization dedicated to

86 improving health care through the collection, analysis, dissemination, public availability, and use of health data.

NADHO provides leadership in health care information management and analysis, promotes the availability of and access to health data, and encourages the use of these data to make informed decisions and guide the development of health policy. NAHDO provides information on current issues and strategies to develop a nationwide, comprehensive, integrated health information system, sponsors educational programs, provides assistance, and serves as a forum to foster collaboration and the exchange of ideas and experiences among collectors and users of health data. By doing so, NAHDO works to increase the state of knowledge (NAHDO website, 2/2/2012).

The current objectives for NAHDO state: NAHDO and its members work to:  Support the development of public domain health data organizations and the use of their data to address national, state, and local issues and patient-level concerns;  Promote uniformity and standardization of health data collection and dissemination among public, private, and voluntary data collectors and users;  Promote cooperation among public and private entities that collect, analyze, and disseminate health data;  Promote the development of uniform privacy regulations and strict penalties for misuse of patient identifiable data; and  Promote timely access to the latest developments, trends, and expertise in health care information (NAHDO website, 2/2/2012).

Additionally, NAHDO created and abides by a “Statement of Principles on Information for Health System Reform.” The National Association of Health Data Organizations (NAHDO) sets forth the following principles to guide the collection, analysis, and dissemination of data/information to support health system reform. We support the development of a nationwide, publicly controlled health information infrastructure built on the strength and expertise of existing public and private health information systems.

NAHDO believes the U.S. health care system requires:  Publicly available data as a foundation for effective decision making at all levels; and

87  Timely and meaningful performance indicators that are independently validated, responsibly used, and comparable across providers, communities, sectors, and states.

NAHDO believes that to accomplish its mission:  Policies that protect the privacy and confidentiality of the patient are essential to a viable, robust health information system;  Data must be uniform and comparable;  Data must be transformed into meaningful information for multiple users with diverse needs;  A nationwide health information infrastructure should provide comprehensive data on health status, health system capacity, use, costs, charges, expenditures and payments, measures of quality of care, and threats to health;  A public-private partnership is key to a successful nationwide health information infrastructure;  Federal guidance is needed to help to ensure the quality, completeness, comparability, timeliness, accuracy, and accessibility to individual-level, longitudinal data; and  Sufficient resources should be invested in database development, analysis, interpretation, dissemination initiatives, information technology, and in efforts to enhance current information systems (NAHDO website, 2/2/2012).

Initially, NAHDO membership only included state data organizations; however, today NAHDO membership includes “…a diverse, national “community of practice” around the collection, analysis, and dissemination of health care data” (NAHDO website, 2/2/2012). “NAHDO members include federal, state, academic, and private organizations that maintain or use large-scale health databases for health services research, market applications, public health and policy, quality improvement, and consumer information” (NAHDO website, 2/2/2012). Additionally, NAHDO “…membership includes state data organizations, federal agencies, peer review organizations, software and hardware vendors, consultative groups, and universities” (NAHDO website, 2/2/2012). “NAHDO membership also includes representatives from state and regional hospital associations, managed care organizations, health research organizations, and the media” (NAHDO website, 2/2/2012). Finally, NAHDO membership includes “…state and private health care organizations that maintain statewide health care databases, stakeholders of these databases in the public and

88 private sectors, and the leaders in hospital discharge data reporting systems and the emerging All-Payer Claims Databases (APCDs)” (NAHDO website, 2/2/2012). NAHDO headquarters staff and its membership provide technical assistance, communication of best practices, guidance, and advocacy for uniformity in data standards across states through national workshops, teleconferences, webinars, forums, and listservs. It hosts experts such as “…state, federal, and private sector leaders in health care data collection, analysis, and dissemination…” at its annual conference for the past 25 years (NAHDO website, 2/2/2012). NAHDO actively encourages “…states to implement inpatient, ambulatory surgery, emergency department data,” and All-Patient Claims Data Bases (APCD) reporting programs, and continues to be “…a leader in the development and implementation of national standards for hospital discharge data and collaborates with the Public Health Data Standards Consortium, the APCD Council, and American’s Health Insurance Plans (AHIP) to develop core APCD reporting standards for state-based claims data systems” (NAHDO website, 2/2/2012). Additionally, NAHDO provides educational programs and forums to encourage cooperation and transfer of ideas and practices among health care collectors and users. NAHDO is well-known for its extensive network and its capacity to maintain and enhance existing large-scale health care databases, as well as, its ability to improve its members’ analytic workforce. “NAHDO’s corporate members provide technical and analytical products and services to improve the quality and use of health care data” (NAHDO website, 2/2/2012). Some of the products available include tools and toolkit products. For example, in 2005, the Overall Care Index was one of the first toolkit products NAHDO made available online. The index is a composite score of five selected AHRQ Patient Safety Indicators (PSIs), which was developed while under contract with the Wisconsin Employer Health Care Quality Alliance. The quality report tools, funded by the Robert Wood Johnson Foundation, are another example. Through the use of its membership and extensive network, NAHDO provides at their national conferences and meetings. Additionally, its members testify as national and state policymakers, and they are responsible for the successful implementation of grants and projects. NAHDO has numerous “…working relationships with health

89 information professionals in all 50 states, including private sector and local health agencies” (NAHDO website, 2/2/2012). Many of these relationships have been established through projects such as the Healthcare Cost and Utilization Project (HCUP), NAHDO-CDC Cooperative Agreement, and AHRQ’s Building Research Infrastructure and Capacity Program. NAHDO staff and members serve as experts on national panels, steering committees, and at hearings. They are frequently asked to speak to state and national groups. NAHDO is regularly contacted by state and national media to comment on health information issues. They also provide oral and written testimony of technical and analytic products produced by NAHDO. In addition, NAHDO members draft comments for submission either proactively or in response to federal policy modifications, published reports, and presentations. NAHDO prepares poster presentations to promote dialogue at conferences. NAHDO drafts issue briefs and position statements such as the “Position Statement: Promoting Safety in the Health Care System: Comments on the Institute of Medicine’s Focus on Evaluation of Medical Errors to Encourage Safety in the Health Care System.” Additionally, NAHDO authors and co-authors reports, including “The State Experience in Health Quality Data Collection” for the Consumer-Purchaser Disclosure Group. Finally, NAHDO and its staff actively engage in grants and contracts that support NAHDO’s organizational mission and strengthen its capacity in state health data agencies and infrastructure. In sum, NAHDO provides educational programs and forums to encourage cooperation and transfer of ideas and practices among health care data collectors and users. It “…provides leadership in health care information management and analysis, promotes the availability of and the access to health care data, and the use of these data to make informed decisions and guide the development of health care policy. NAHDO provides technical assistance and information on current issues and strategies to develop a comprehensive, integrated health information system” (NAHDO website, 2/2/2012). Its “…members are experts in health care data collection and use, and represent the only community of practice dedicated to population-based health care data issues” (NAHDO website, 2/2/2012).

90 Qualitative Content Analysis To complement the descriptive history of the National Association of Health Data Organizations (NAHDO) presented in the previous section, I conducted a qualitative content analysis of primary data from NAHDO’s reports and newsletters that capture strategies and practices it has used to educate its members about health care transparency laws. Education and information is at the core of NAHDO’s efforts to diffuse knowledge and practices to its members and the public. I examined all of NAHDO’s reports, newsletters, and other publications that were available on NAHDO’s website. The documents reviewed covered events from 1998 through 2006. The qualitative content analysis revealed that most of NAHDO’s activities related to health care transparency laws occurred both before and after the state had enacted a health care transparency law or the state had voluntary decided to implement the main components of a health care transparency law without a mandate. From the information reviewed, as shown in Table 30, NAHDO offers support to states who desire to enact a health care transparency law by conducting state surveys, publishing reports, writing position statements, and facilitating teleconferences. It assists states with resolving issues related to health care data collection, health care measurement, health care data systems, and the display of health care data. Additionally, NAHDO hosts annual conferences to educate its members and to provide networking opportunities to disseminate information related to health care data. Also, it develops proposals and secures funding to conduct research to develop health care data measurement and management standards; publicizes the necessity of health care information to make health care decisions; and provides state and federal funding justification strategies. Finally, NAHDO presents the national perspective on health information at national and state meetings.

Table 30. Timeline of NAHDO’s Primary Data used to Educate its Members about Health Care Transparency Laws (1998 through 2006)

1999: NAHDO 1998 Annual Report (NAHDO’s activities during FY 1998)  1998 Project: Survey of State

91 Table 30. Continued

Jan. 2000: NAHDO 1999 Annual Report (NAHDO’s activities during FY 1999)  Position Statement: Promoting Safety in the Health Care System: Comments on the Institute of Medicine’s Focus on Evaluation of Medical Errors to Encourage Safety in the Health Care System (December 7, 1999)  Agenda 2000: Accomplishments: 1) Strengthened and Increased Visibility of State Systems o Robert Wood Johnson Foundation and Alpha Center Invited Paper: “Data Sharing and Dissemination Strategies for Measuring and Fostering Competition in Health Care” o NAHDO presented the national perspective on health information in national and state venues

Oct. 2002: NAHDO Annual Report 2001-2002 NAHDO Membership Workshop Series:  Membership workshop teleconferences were conducted in 2002 and “Lessons Learned” were drafted and distributed afterwards, including: o “Communicating the Value of Discharge Data to Legislators and other Stakeholders: State and federal funding justification strategies” (February 2002)

Oct. 2003: NAHDO Annual Report 2002-2003 National Opinion Research Center: “Value of Hospital Administrative Data”:  AHRQ awarded a contract to the National Opinion Research Center (NORC) and NAHDO to conduct a study to document the value of hospital administrative data. The study is expected to help build a broad constituency of support for the continuation and enhancement of hospital discharge databases.  The purpose of this study is to evaluate the value and impact or utility of administrative data and to improve existing data systems by identifying new data elements for use in reporting and research. Ultimately, this study can be used by NAHDO and its members to educate and advocate for statewide health care data systems.

December 2-3, 2002: NAHDO’s 17th Annual Meeting A State Health Data panel highlighted the challenges facing state agencies.

Case Study Telephone Interviews Purpose To supplement my quantitative research and to generate new insights into the enactment of health care transparency laws, I conducted a case study where I examined a professional association: the National Association for Health Data Organizations (NAHDO). As part of the case study, I interviewed, via telephone, three of NAHDO’s key stakeholders to gather first-hand knowledge of diffusion strategies, practical politics, and other activities NAHDO implemented to support states in their pursuit to enact inpatient health care transparency laws. Specifically, I interviewed

92 three of NAHDO’s professional staff and long-term members of NAHDO’s Board of Directors and obtained information regarding the interviewees’ knowledge of inpatient health care transparency law enactments from 1971 through and including 2006.

Method Case study telephone interviews were determined to be the method to collect the information because the subjects live and work in different states. Face-to-face communication is not necessary to obtain the information for this study, as well as, in- person interviews are not a cost-effective means of obtaining this data. Upon receiving approval from the Office of Human Subjects (Appendix A), I contacted the subjects with an introduction email (Appendix B) with an Informed Consent Form attached (Appendix C) and requested the subject to participate in the study. In the introduction email, I requested the subject to respond to the email and indicate whether s/he agreed to participate in the study. If the subject responded that s/he agreed to participate, I telephoned the subject to fully discuss the Informed Consent Form and related concepts, answered questions about the study and his/her rights, and scheduled a telephone interview. I obtained a signed Informed Consent Form from each subject prior to conducting a telephone interview. The Discussion Guide for the Case Study Telephone Interviews (Appendix D) was used to facilitate the interviews. The Discussion Guide contains questions about inpatient health care transparency law enactments and NAHDO activities. The telephone interviews were scheduled to last about 20 minutes. The subject’s participation was completely voluntary. All of the information obtained remains confidential, to the extent allowed by law. No individual responses are reported. Only summary data is reported. Next, I discuss the results of the case study telephone interviews.

Results of the Case Study Telephone Interviews A major resource for the case study was the telephone interviews conducted of NAHDO’s key stakeholders. On May 14, 2012, an introduction email (Appendix B) with an Informed Consent Form (Appendix C) attached were emailed to five people who

93 were either part of NAHDO’s professional staff or were long-term members of NAHDO’s Board of Directors. Of the five people selected to participate, three people were interviewed. Fortunately, there were representatives from both NAHDO and NAHDO’s Board of Directors. The first telephone interview was scheduled and conducted immediately after the introduction email was sent out. It was conducted on May 15, 2012. A follow-up request for participation was sent out on May 30, 2012 which included an endorsement to participate by NAHDO’s Executive Director, who has been the Executive Director since 1998. After which, two more people of the five people requested to participate agreed to participate. These interviews were conducted on June 8, 2012 and June 12, 2012. After receiving signed Informed Consent Forms from the participants, the Informed Consent Form was discussed. Questions were encouraged and answered. Then, the telephone interviews were conducted using the Discussion Guide for the Case Study Telephone Interviews (Appendix D). As you may recall, the Discussion Guide contains questions about inpatient health care transparency law enactments and NAHDO activities. The telephone interviews were scheduled to last about 20 minutes. While one interview lasted approximately 20 minutes. The other two interviews lasted much longer than the scheduled amount of time. One of the interviews lasted more than double the scheduled time (45 minutes). The interviewees were cordial and cooperative. They seemed at ease with the questions and very knowledgeable about the subject matter. They freely provided information and welcomed follow-up questions and additional contact. In conclusion, the interviewees were enthusiastic about the topic and are interested in the results of this study. Next, the responses of three key stakeholders from NAHDO and the NAHDO’s Board of Directors are summarized for each interview question. To review the case study telephone interviewees’ responses that have not been summarized, please refer to Appendix E. In Question one, the interviewees were asked, “During the mid-1980s to mid- 1990s, there was a heavy period of state inpatient health care transparency law enactments. What factors do you think influenced states to enact inpatient health

94 care transparency laws?” All three interviewees listed the rising costs of health care as one of the factors. Interviewees stated that large purchasers, employers, business coalitions, health plans, consumers, state agencies, and health data agencies like NAHDO were looking for a solution to the rising cost of health care and were confronted with the lack of accessible, standardized data. Large purchasers and employers, who wanted health care data to conduct negotiations, are credited for being the driving force for getting the inpatient health care transparency laws passed during this time period. Additional factors which influenced states to enact inpatient health care transparency laws were the states’ health planning departments and President Bush’s Blue Ribbon Panel on Quality. These government groups wanted accessible, standardized health care data to make state planning decisions and assessments about the quality of health care and utilization patterns. Additionally, they desired accurate measurement of health care quality. Specifically, standards needed to be developed and used, and the standards that existed needed to be improved. A third factor that influenced on the enactment of inpatient health care transparency laws by states was the growing interest in consumer quality. People wanted to know information about health care. They were interested in health care cost and outcome information for procedures, physicians, hospitals, etc. Finally, a policy champion was listed as an influential factor for the enactment of inpatient health care transparency laws by states. In fact, one interviewee stated that a policy champion was the most important factor for getting a health care transparency law passed by a state. The need for a policy champion is repeated in an interviewee’s response for Question three of the telephone interview, as the most important thing that is needed to get a health care transparency law passed. In Question two, the interviewees were asked, “What obstacles did states encounter in their efforts to get inpatient health care transparency laws enacted?” One interviewee stated that the answer depends on the era. There were different obstacles the states encountered in different eras. In the early days of trying to get an inpatient health care transparency law passed, the hospitals and hospital associations were the biggest obstacles.

95 All of the interviewees reported that there was opposition and fear by the providers. The providers were concerned about having health care information available to non-providers. They were concerned about having the health care information adversely affecting them and/or their proprietary concerns. Some of the concerns included: 1) “How would the providers look?”; 2) “How would the health care data be utilized?”; 3) “Would providers have an opportunity to review the health care data prior to it being publicly reported?”; and 4) “Would providers have an opportunity to influence health care policy?” Additionally, there were a lot of debates regarding issues of 1) “Who will “own” the data (or be the data aggregator)?”; 2) “How will the information be reported?” (as opposed to the initial debates of whether to collect and report health care data); 3) “Who pays for the data collection?”; 4) “Who decides what data will be collected?”; and 5) “Who decides what data will be released to the public?” Two of the interviewees listed funding as a big obstacle that states encountered in their efforts to enact inpatient health care transparency laws. It was reported that the legislators often pass a health care transparency law that fund the data collection, but neglect or choose not to fund the analysis of the health care data. Some additional obstacles listed by the interviewees that the states encountered in their efforts to enact inpatient health care transparency laws included questions about the value of health care data collection and reporting; the problems associated with non- electronic data collection; and legislators who supported the enactment of an inpatient health care transparency law but were noncommittal because they did not want to harm anyone. Despite the obstacles that were present, the interviewees reported that the businesses and health care data organizations helped to make the health care data publicly available. Ironically, the hospital associations, who were the biggest opponents of the movement, are now the biggest users of the health care data. In Question three, the interviewees were asked, “What diffusion strategies, practical politics, and other activities did NAHDO implement to support states in their pursuit of getting inpatient health care transparency laws enacted?” All of the interviewees espoused the same message regarding NAHDO playing a key role in getting state inpatient health care transparency laws passed. All three interviewees

96 listed NAHDO as providing the following services: sharing strategies to get health care transparency legislation passed; sharing successful legislation among states; sharing “talking points;” providing an inventory of data processes, submissions, datasets, tools, etc., for states to use; providing expertise regarding laws, data collected, and use of the data; providing consultation services; and providing testimony. Additionally, NAHDO acts as the central, coordinating health care data organization. NAHDO shows the commonality among states which helps with the standardization of methodologies, data collection, and reporting; they bring people together; they tackle issues together; and they share “lessons learned” and “best practices.” One interviewee summarized NAHDO as offering “a roadmap for the states to follow to get a health care transparency law passed. It includes how to convert raw data to reporting; “best practices,” regulations, oversight, and data standards documentation; conferences to attend; software tools; and a network of folks to help. NAHDO is a cheap resource that provides advocacy, education, technical consultation, and the minimum dataset they need.” In Question four, the interviewees were asked, “The states who were NAHDO members that were successful in getting their inpatient health care transparency laws enacted, were they the most active participants in NAHDO activities? Please explain.” One interviewee explained that some states already had health care transparency laws, referred to as data collection and reporting laws, prior to the formation of NAHDO. These states are known as “first generation” states. Florida is a “first generation” state. Whereas, the states that enacted health care transparency laws after the establishment of NAHDO are known as “next generation” states. Utah is an example of a “next generation” state. NAHDO and the “first generation” states were significant influences in assisting other states to enact inpatient health care transparency laws. The “first generation” states were and continue to be the most active members of NAHDO, both as mentors and as leaders. The states who were NAHDO members that were successful in getting their inpatient health care transparency laws enacted were the most active participants in NAHDO activities because they understood the value of participating in the NAHDO- sponsored activities. They were actively engaged as they were getting their health care

97 transparency laws enacted. They continued to be engaged as they became more experienced in using the data. The more they used the data, the more questions they had. They remained active participants to take advantage of the information sharing of “best practices” and “lessons learned.” They wanted to learn how to improve in the areas of health care data collection and reporting. They were willing to share their “best practices” and “lessons learned” with others, such as consumers, providers, etc. Additionally, they remained active in NAHDO activities because they wanted to use their experience to influence the development of standards for health care data collection and reporting.

Discussion After reviewing the descriptive history of NAHDO, the qualitative content analysis, and the case study telephone interviews, I determined that the qualitative analysis results were consistent with the professional association diffusion literature. They both evidence support for professional associations as agents affecting policy diffusion. Throughout the qualitative information gathered and analyzed, there were numerous examples of NAHDO, a professional association, sharing information with different organizations and individuals, regardless of where they were physically located and conducting activities to assist states to enact their health care transparency laws. The following is a listing of some of the qualitative information supporting professional associations as diffusion agents in this study. First, from the focused review of literature of the diffusion strategies and practical politics of professional associations, several scholars asserted that professional associations can affect policy diffusion by facilitating communication among those public officials who are responsible for developing and implementing policy. Second, from the descriptive history, NAHDO is described as providing testimony to national and state policymakers; serving as experts on national panels, steering committees, and hearings; drafting issue briefs and policy statements; and hosting educational programs, forums, and conferences to encourage the transfer of ideas and practices among health care data collectors and users. Third, from the qualitative content analysis of the primary data from NAHDO’s

98 reports and newsletters, NAHDO is depicted as using education and information to diffuse knowledge and practices to its members. Finally, in the interviewee responses, especially in Question three and Question four, NAHDO is described as a policy diffusion agent through its sharing of strategies with states to get health care transparency laws passed and serving as a rich resource to its members and the states. Most of these examples supporting professional associations as diffusion agents can be found in more than one of the qualitative analyses conducted for this case study, which support their credibility. The results of the case study telephone interviews for Question four provide additional evidence of professional associations as diffusion agents. The interviewees reported that the states who were NAHDO members that were successful in getting their inpatient health care transparency laws passed were states that were the most active participants in the NAHDO activities; thus, providing support for F.S. Berry’s (1994) premise “…that the greater the participation of specific state officials in these [professional] associations, the more likely the state is to adopt a policy” (p. 453). To summarize, the qualitative analysis provided numerous occurrences which support the professional association literature’s claim of professional associations serving as policy diffusion agents. The next chapter, Chapter six, begins with a brief overview of this dissertation. Then, it describes the impact that this study has on innovation and diffusion theory. Next, the study limitations and suggestions for future research are explored. Finally, a conclusion is presented addressing the outlook for the field of innovation and diffusion as well as the health care transparency movement.

99 CHAPTER SIX

CONCLUSION

Introduction This chapter begins with a brief overview of this dissertation. Then, it describes the impact that this study has on innovation and diffusion theory. Next, the study limitations and suggestions for future research are explored. Finally, a conclusion is presented addressing the outlook for the field of innovation and diffusion as well as the health care transparency movement.

Overview This research was designed to answer the question of “What factors influence a state legislature to enact an inpatient health care transparency law?” F.S. Berry and W.D. Berry’s (1990, 1999) Unified Model of State Innovation provides the framework for my study hypotheses and the analysis of inpatient health care transparency law enactments by states between 1971 and 2006 inclusive. A Unified Model of Inpatient Health Care Transparency Law Enactments was developed to test the eight hypotheses. An event history analysis was conducted using Cox regression in SPSS. Next, the results of the quantitative analysis are reviewed. The final model includes the variables ELECT1, ELECT2, FISCAL, HCCRISIS, IDEOLOGY, UNIFIEDPARTY, and NEIGHBORS, but excludes IOMRPT, PROFASSN1, and PROFASSN2. Of the five hypotheses that test “motivation to innovate” variables, only the IDEOLOGY hypothesis was supported. For IDEOLOGY, it was hypothesized that an inpatient health care transparency law is more likely to be enacted in a state as its rate of state government liberalism increases. The IDEOLOGY variable was statistically significant, the Beta was positive, and the hypothesis was supported. This result lends

100 support to Barrilleaux, Brace, & Dangremond’s (1994, p.28) statement “…that ideology is the most persistent force underlying state health reform efforts.” Of the two hypotheses that test “obstacles to innovation and resources to overcome them,” only one hypothesis had a statistically significant variable: UNIFIEDPARTY. For UNIFIEDPARTY, it was hypothesized that a state will enact an inpatient health care transparency law increases when a single political party controls the governorship and both houses of the legislature. As expected for the traditional variable of public policy innovation and diffusion studies, the variable was statically significant and the hypothesis was supported. This result provides support for Hansen’s viewpoint that having unified party control of state government is an important political resource for state officials to overcome obstacles to innovation (Hansen, 1983, pp. 153- 154; F.S. Berry & W.D. Berry, 1992, p.719). To summarize, the quantitative analysis results provide support for legislative ideology and unified party control of state government as factors influencing inpatient health care transparency law enactments by a state. Additionally, the health care crisis and previous adoptions by nearby states hypotheses were not supported. The HCCRISIS and NEIGHBORS variables were statistically significant, but in the opposite direction than predicted. In addition to the quantitative analysis, a case study of a national health care data professional association, NAHDO, was developed. In this case study, I provided a focused review of literature about diffusion strategies and the practical politics of professional associations to determine their role and importance in state inpatient health care transparency law enactments. Then, I conducted a qualitative analysis of professional associations using three techniques: 1) a descriptive history of NAHDO focusing on its role as a diffusion agent and the diffusion process strategies it employs; 2) a qualitative content analysis of primary data from NAHDO’s reports and newsletters that capture strategies and practices it has used to educate its members about health care transparency laws; and 3) telephone interviews of three of NAHDO’s key stakeholders to gather first-hand knowledge of diffusion strategies, practical politics, and other activities NAHDO implemented to support states in their pursuit of getting inpatient health care transparency laws enacted. Finally, I summarized the qualitative analysis

101 results and compared them to the review of the professional association diffusion literature. The results of the qualitative analysis are as follows. The first finding of the qualitative analysis showed support for professional associations affecting policy diffusion. In the focused literature review of professional associations, several authors asserted that professional associations can affect policy by facilitating communication among officials charged with formulating and implementing policy. Throughout the qualitative analyses, those authors’ assertions were supported by numerous examples of NAHDO, a professional association, sharing information with different organizations and individuals and conducting activities to assist states to enact their health care transparency laws. The second finding of the qualitative analysis showed support for F.S. Berry’s (1994) premise “…that the greater the participation of specific state officials in these [professional] associations, the more likely the state is to adopt a policy” (p. 453). The interviewees’ responses to Question four of the telephone interview reported that the states who were NAHDO members that were successful in getting their inpatient health care transparency laws passed were states that were the most active participants in the NAHDO activities. To summarize the qualitative results, it provides support for professional associations and policy champions as diffusion agents for inpatient health care transparency law enactments by states.

Implications for Innovation and Diffusion Theory This study’s findings suggest that state adopters of an inpatient health care transparency law are more likely to enact an inpatient health care transparency law when liberalism is increasing in state government and when unified political party control is increasing in the state legislature. This study supports variables traditionally used in policy adoption research including political ideology and unified political party control in state government. However, this dissertation has produced some unexpected results. It was surprising that the neighbors and a health care crisis variables were statistically significant, but in

102 the opposite direction than predicted. These findings, especially the finding about neighbor diffusion, should prompt researchers to reexamine the current literature on policy innovation and diffusion. This dissertation’s qualitative analysis provides support for professional associations affecting policy diffusion. The NEIGHBORS variable was statically significant but not in the predicted direction. This result lends support that policies are diffused via an avenue other than through states which share boundaries. There are numerous examples supporting this possibility throughout the qualitative analyses. Additionally, there were numerous examples of NAHDO, a professional association, sharing information with different organizations and individuals, regardless of where they were physically located and conducting activities in an effort to assist states to enact their health care transparency laws. The results of the telephone interviews for Question four provide additional evidence of professional associations as diffusion agents. The interviewees reported that the states who were NAHDO members that were successful in getting their inpatient health care transparency laws passed were states that were the most active participants in the NAHDO activities; thus, providing support for F.S. Berry’s (1994) premise “…that the greater the participation of specific state officials in these [professional] associations, the more likely the state is to adopt a policy” (p. 453). Additionally, this dissertation contributes to the policy innovation and diffusion literature by providing an analysis of inpatient health care transparency laws and discussing the results as they relate to policy innovation and diffusion. At the time of this study, no study had focused solely on the enactment of state health care transparency laws. The findings of this study explain why states enact inpatient health care transparency laws. Furthermore, this research expands the use of the Unified Model of State Policy Innovation (F.S. Berry & W.D. Berry, 1990, 1992, 1999) as a powerful explanatory model for state policy innovation, and diffusion in the analysis of inpatient health care transparency issues. Finally, this dissertation contributes to the policy innovation and diffusion literature by demonstrating the usefulness of the event history analysis technique in public policy study; thus, increasing the number of public policy studies that support the

103 use of event history analysis and specifically the Cox regression method as valuable and appropriate methods to use in public policy studies.

Study Limitations and Future Research This study has a few limitations and has numerous opportunities for future research. Of the study limitations, most of them can be improved in future research. One study limitation is that these findings cannot be generalized to other populations. These findings are specific to inpatient health care transparency laws enacted by U.S. states. However, testing this model of inpatient health care transparency law enactments with similar health care transparency, transparency, or consumerism laws may prove quite valuable. This limitation may be easily addressed with some minor changes to the variables and provide bountiful opportunities for future research. For example, expanding this study to examine outpatient/ambulatory surgery, health plan, physician, and infection control health care transparency laws may yield interesting results. Similarly, expanding this study to examine laws related to public reporting such as pharmacy prices, gas prices, and insurance prices via consumer comparison websites may yield interesting results, as well. Another potential limitation of this research is the incomplete data for three of the variables. The variables IDEOLOGY and UNIFIEDPARTY had incomplete datasets. However, the amount of data missing and its effect is likely to be minimal. For IDEOLOGY, there are 1104 state-years of 1126 state-years. This is because the dataset did not contain data for the years 2005 and 2006. Therefore, there are 22 missing cases. Ten states did not adopt, and one state, Massachusetts adopted in 2006, but does not drop out of the dataset until 2007. For UNIFIEDPARTY, there are 1075 state-years of 1126 state-years. This is because Nebraska was coded as missing data for all years and Minnesota was coded as missing data for 1971-1975 inclusive because these states had a nonpartisan legislature during these years. Also, the dataset did not contain data for 2006. Eleven states did not adopt, and one state, Massachusetts adopted in 2006, but does not drop out of the dataset until 2007. Finally, the PROFASSN1/PROFASSN2 variable was removed from the study due to the use of merely five years of professional association data to populate the years with

104 missing data for the 36 year study. Overall, for the nine variables tested in the model for a span of 36 years, the dataset was remarkably complete. An idea for future study would be to study the implementation dates of the inpatient health care transparency laws and then compare them with the results from this study’s examination of the enactment dates. Does the date used change the results of the study, and in what ways? The reason the question is posed for future study is that some states were successful in getting an inpatient health care transparency law passed, but they were not successful in implementing the law; there was a lag in the number of years between the law’s enactment and its implementation; or the law was not implemented to the full extent mandated in the legislation. Generally, the lack of implementation by a state is due to lack of funding, lack of expertise, lack of a policy champion, industry opposition, and/or lawsuits. It would be interesting to study whether the factors that influence the adoption of an inpatient health care transparency law by states are the same factors that influence a successful implementation of the same law. Another possibility for future research would be to extend the time period examined in this study to include the years 2007 to the current year. While this study covers an extensive period of time (1971-2006, inclusive; 36 years), additional data for some of the variables may be available to study now and more states may have enacted inpatient health care transparency laws. Perhaps the IOMRPT variable would be an influential factor for inpatient health care transparency law adoption if more years after the publication dates of the Institute of Medicine reports were included in the study. Some additional topics for future study include further testing of the influence of a health care crisis, modifying the measurement or data source for the professional association variables, testing for fixed-regional diffusion, and testing for the influence of a policy champion. To further test the influence of a health care crisis, a closer examination of whether the cost of the solution offered (the policy innovation) affects whether the policy adopted since the HCCRISIS variable is defined in terms of a financial crisis (i.e., the state’s budget dedicated to Medicaid) is suggested. Perhaps in future studies, another source of data or a different way to measure the influence of professional associations can be used so that enough data is present to test the

105 influence of this variable. Another suggestion is to test for fixed-regional diffusion to compare the results to the NEIGHBORS variable (states that share borders). A final recommendation for future study is to incorporate a variable into the model to test the influence of a policy champion on the enactment of inpatient health care transparency laws by states. This recommendation is made due to the telephone interviewees stating repeatedly that a policy champion is an important factor influencing the enactment of inpatient health care transparency laws.

Conclusion The theory of policy innovation and diffusion to predict the factors influencing the spread of policies and the use of Berry & Berry’s (1999) Unified Model of State Policy Innovation prosper as their applicability to numerous public policy areas, including health care, are continually demonstrated. Similarly, the event history analysis approach and specifically the Cox regression method continue to gain support as their value as analytical methods and appropriateness to use in public policy studies is repeatedly demonstrated. This dissertation provides an analysis and evaluation of factors influencing states to enact inpatient health care transparency laws between 1971 and 2006 inclusive. Berry & Berry’s (1999) Unified Model of State Policy Innovation provides the framework for the study hypotheses and the analysis of inpatient health care transparency law enactments by states. This study tests eight hypotheses using event history analysis. The quantitative analysis provides support for legislative ideology and unified party control of state government as factors influencing inpatient health care transparency law enactments by a state. Additionally, the quantitative analysis provides support for a health care crisis and neighbors as factors influencing inpatient health care transparency law enactments by a state, but in an opposite direction than predicted. The study’s findings suggest that state adopters of an inpatient health care transparency law are more likely to enact an inpatient health care transparency law when liberalism is increasing in state government and when unified political party control is increasing in the state legislature.

106 Additionally, this dissertation presents a case study of a national health care data professional association using several techniques, including telephone interviews. The qualitative analysis provides support for professional associations and policy champions as diffusion agents for inpatient health care transparency law enactments by states. This study supports variables traditionally used in policy adoption research including legislative ideology and unified political party control in state government. However, it will be interesting to see whether influences such as professional associations gain traction over the traditional regional diffusion influences such as states sharing borders as factors influencing state policy adoption. Meanwhile, as evidenced in this study, there continues to be support for a model that is able to test both internal determinants and regional diffusion influences on policy adoption by states. The outlook for the future of the health care transparency movement looks promising. The health care transparency movement promotes improved access to information, patient empowerment, improved patient safety and quality of care, improved provider accountability, and lower health care costs. This movement is not a passing fad, but rather a permanent change being implemented in all health care settings across the United States. It is creating positive changes for both patients and purchasers of health care. Additionally, it is generating numerous opportunities for researchers to study the health care transparency movement, health care clinical and social policies, and, of course, the health care data itself. Improved health through reliable, accessible data and data-supported decisions is increasingly becoming the norm and less an idealistic scenario to be realized in the distant future.

107 APPENDIX A: HUMAN SUBJECTS COMMITTEE APPROVAL LETTER

108 APPENDIX B: INTRODUCTION EMAIL FOR CASE STUDY TELEPHONE INTERVIEWS

109 APPENDIX C: INFORMED CONSENT FORM FOR CASE STUDY TELEPHONE INTERVIEWS

110 APPENDIX D: DISCUSSION GUIDE FOR CASE STUDY TELEPHONE INTERVIEWS

1) During the mid-1980s to mid-1990s, there was a heavy period of state inpatient health care transparency law enactments. What factors do you think influenced states to enact inpatient health care transparency laws?

2) What obstacles did states encounter in their efforts to get inpatient health care transparency laws enacted?

3) What diffusion strategies, practical politics, and other activities did NAHDO implement to support states in their pursuit of getting inpatient health care transparency laws enacted?

4) The states who were NAHDO members that were successful in getting their inpatient health care transparency laws enacted, were they the most active participants in NAHDO activities? Please explain.

111 APPENDIX E: CASE STUDY TELEPHONE INTERVIEWEE RESPONSES

In this appendix, the four questions from the Discussion Guide for the Case Study Telephone Interviews (Appendix D) are accompanied by the results from the three interviews. The order of the interviewee responses have been changed and do not reflect the order the interviews were conducted, nor are the interviewee responses presented in a consistent order. This is to protect the confidentiality of the interviewee. Additionally, where necessary, identifying information has been removed to protect the confidentiality of the interviewee.

Interviewee Response:  People were struggling to get a handle on cost data due to the concern about the rising cost of health care. o Health data agencies like NAHDO o Business coalitions and health plans o Consumers o State agencies

 1990s – President’s Blue Ribbon Panel on Quality. o Looked at under/over utilization of health care services. o Looked at disparities with regard to access to services and preventable deaths.

 We want quality to be measured accurately. o We knew billing codes were used. o We wanted standards to be developed and used. o We wanted standards that existed to be improved.

Interviewee Response:  ***Large purchaser and employer activism. Their costs were going up. They were frustrated with the lack of data to conduct negotiations. They wanted to use the existing hospital discharge databases’ data. They found that the hospital data systems were non-standardized and contained data that was not uniform and/or accessible.

112  The second group to influence the enactment of health care transparency laws (public health care data collection and reporting laws) was the states’ health planning departments. State health departments’ wanted data to make decisions about planning for their state’s population’s health. For many states, the Certificate of Need (CON) laws or state health planning laws were the first laws that required states to collect and/or report health care data.

 A policy champion is the most important factor to get a health care transparency law passed in his/her state.

Interviewee Response:  The rising health care costs  The growing interest in consumer quality

2) What obstacles did states encounter in their efforts to get inpatient health care transparency laws enacted?

Interviewee Response:  Especially for health care transparency, the providers’ fear of having the health care information available to non-providers o How would the providers look? o How would the health care data would be utilized? o Would providers have an opportunity to review the health care data prior to it being publicly reported? o Providers wanted the opportunity to influence health care transparency policy.

 Non-electronic data collection (not automated); all paper data

 Legislators supported enactment of health care transparency law, but they didn’t want to harm anyone.

 Businesses and health care data organizations helped get health care data publicly available.

Interviewee Response:  Funding

 Political opposition o People who were asked to provide the information (data) had concerns: . That it would adversely affect them . Proprietary concerns

 Questions about the value of health care data collection and reporting.

113 Interviewee Response:  Depends on the era. There were different obstacles states encountered in different eras.

 In the early days, there was industry (hospital) push-back (i.e., the hospital associations). Now, the hospital associations are the biggest users of the data.

 There are a lot of debates around the issues of who will “own” the data/be the data aggregator, who will control the data system, and how will the information be reported, as opposed to the initial debates of whether or not to collect and report the health care data.

 There are a lot of discussions about who pays, who decides what data gets collected, and what data gets released. “Who decides” is central to these debates.

 Funding is another big obstacle. For example, who will fund doing the analytics? A state may pay for the data collection, but not the analysis of the data.

 Obstacle/Tactic: Sometimes certain people do not want to see public health care quality reporting conducted. They say to use an Electronic Health Record (EHR) instead, but an EHR does not contain the same information as the data that is collected for public health quality reports.

3) What diffusion strategies, practical politics, and other activities did NAHDO implement to support states in their pursuit of getting inpatient health care transparency laws enacted?

Interviewee Response: NAHDO:  Routinely provides expertise (regarding laws, data collected, use of the data)

 Involved with virtually all of the states who have had health care transparency laws passed.

 Provides “lessons learned” and “best practices” information.

 Provides consultation services. (“How to get it done” testimony)

 Acts as the central, coordinating organization.

114 Interviewee Response:  A state or policy champion will contact NAHDO and ask for their help in getting a health care transparency law passed.

 There isn’t a “one-size-fits-all” solution for states to get a health care transparency law passed. It takes individualized (per state) problem-solving to help get a law passed.

 The first and most important step is to identify the key players.

 NAHDO does its homework with the policy champions. You must have a policy champion to get a health care transparency law passed.

 Identify the strengths and weaknesses that the state faces in getting the health care transparency law passed, and help the policy champion overcome them.

 Identify the state’s wants. Identify the obstacle people. Address them and make it happen.

 NAHDO goes into the state with the understanding that if we complete all of the steps, we will get the health care transparency law passed. We will make it happen.

 NAHDO has a comprehensive inventory of data processes, submissions, datasets, tools, etc., for states to use.

 When talking to folks, NAHDO says “Here’s what states with a health care transparency law can do that you can’t.” It’s a compelling story. NAHDO lists the benefits of a health care transparency law, and reminds them that that it isn’t a radical idea. NAHDO will show basic metrics for a neighbor state and demonstrate to the state without a health care transparency law what your neighbor state can do and you can’t.

 NAHDO has a roadmap for the state to follow to get a health care transparency law passed. It includes how to convert raw data to reporting; “best practices,” regulations, oversight, and data standards documentation; conferences to attend; software tools; and a network of folks to help. NAHDO is a cheap resource that provides advocacy, education, technical consultation, and the minimum dataset they need. By advocacy, we mean that NAHDO is a positive messenger for the state data agency that is continuously under fire. The collection and public reporting of health care data is the right thing for a state to do. They need this data to manage the state population’s health.

115  Again, the most important thing you need to get a health care transparency law passed is a policy champion. S/he can be a politician, a state health officer, or anyone, but having a policy champion for the health care transparency law is essential.

Interviewee Response: NAHDO played a key role.  They shared successful legislation among different states.

 They shared strategies to get health care legislation passed.

 They shared “talking points.”

 They testified, upon request (e.g., The world would get better if we passed health care transparency laws).

 They showed commonality among the states. o Helps with the standardization of methodologies, data collection, reporting.

 They pulled people together.

 They tackled issues together.

 They shared “lessons learned” and “best practices.”

4) The states who were NAHDO members that were successful in getting their inpatient health care transparency laws enacted, were they the most active participants in NAHDO activities? Please explain.

Interviewee Response: NAHDO Member states that were successful in getting their inpatient health care transparency laws enacted, were they the most active participants in NAHDO activities?  Yes, because they understood the value of it. o They were heavily engaged as they tried to get legislation passed. o They were further along; therefore, they were more engaged with the data so they were more active. . To take advantage of the information sharing, “best practices,” “lessons learned,” and use their experience to influence the development of the standardization of methodologies, data collection, and reporting.

Interviewee Response:  Some states had health care transparency laws (called public health care data collection and reporting laws) prior to NAHDO’s existence. These are

116 called “first generation” states, such as Florida. These states were already progressive states.

 The “next generation” states, like Utah, had laws passed after the establishment of NAHDO.

 NAHDO Member States that were successful in getting their inpatient health care transparency laws enacted, were they the most active participants in NAHDO activities?

o NAHDO and the “first generation” states brought the other states along and were instrumental in helping other states get their health care transparency laws enacted.

o The “first generation” states are the most active members of NAHDO, both as mentors and as leaders.

Interviewee Response: NAHDO Member States that were successful in getting their inpatient health care transparency laws enacted, were they the most active participants in NAHDO activities?  Yes, they were!

 They were willing to share “lessons learned” and “best practices.”

 They wanted to gain more information about how to do better (e.g., health care data collection and reporting).

 They were willing to provide information to others (consumers, providers, or anyone) who wanted information.

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127 BIOGRAPHICAL SKETCH

Lisa Jean Eaton Lisa Jean Eaton earned a Bachelor of Arts degree in Criminal Justice from the University of Florida. She received a Master of Health Administration degree from the University of South Florida. As a full-time employee and a part-time student, Lisa earned her Doctor of Philosophy degree in Public Administration and Policy at the Reubin O’D. Askew School of Public Administration and Policy at Florida State University. Lisa has worked in health care management and social services since 1997. She is a Certified Professional Project Manager, a Florida Certified Contract Negotiator, and a Florida Certified Contract Manager. Lisa presently serves as an analyst for the Office of Program Accountability at the Florida Department of Juvenile Justice. Previously, Lisa has served as the Administrator of an assisted living facility; an internal auditor and a Program Administrator within the State Center of Health Statistics at the Florida Agency for Healthcare Administration; the Program Integrity Manager and the Health Services Manager at the Florida Healthy Kids Corporation; a Research Project Analyst for Tampa Bay Surgery Center; an analyst for the Bureau of Contracts at the Florida Department of Juvenile Justice; a Contract Manager and a Contract Monitor at the Florida Department of Children and Families; and a Healthcare Recruiter for Maxim Healthcare Services. Lisa is married to Chris Eaton. They have two daughters, Lindsay and Jocelyn. Lisa enjoys swimming, traveling, attending musicals, reading, and spending time with family and friends.

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