Policy Adoption by State Governments

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 acting 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 the state government is increasing in liberalism and when unified political party control of the governor and the governorship of both houses of the state legislature is increasing. 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 theory of policy innovation and diffusion to predict the factors influencing the spread of policies and the use of Berry & Berry's (1990, 1999) Unified Model of State Policy Innovation prosper as their applicability to numerous public policy areas, including health care, are continually demonstrated. Similarly, event history analysis and specifically the Cox regression method continue to gain support as their value as analytical methods and appropriateness for use in public policy studies is repeatedly demonstrated. 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.