Essays on health care quality measurement and evaluation /

Essays on health care quality measurement and evaluation /

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Bibliographic Details
Author / Creator:	Coca Perraillon, Marcelo, author.
Imprint:	2015. Ann Arbor : ProQuest Dissertations & Theses, 2015
Description:	1 electronic resource (106 pages)
Language:	English
Format:	E-Resource Dissertations
Local Note:	School code: 0330
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/10773051

Hidden Bibliographic Details
Other authors / contributors:	University of Chicago. degree granting institution.
ISBN:	9781321877656
Notes:	Advisors: Tamara Konetzka Committee members: Robert Gibbons; Tamara Konetzka; Ya-Chen Tina Shih; Ronald Thisted. This item is not available from ProQuest Dissertations & Theses. This item must not be sold to any third party vendors. This item must not be added to any third party search indexes. Dissertation Abstracts International, Volume: 76-11(E), Section: B. English
Summary:	Concerns about high costs and uneven quality of health care in the United States have prompted a call for interventions to both improve the quality of health care and reduce costs. Health care report cards are regarded as a powerful tool to improve quality of care by providing incentives for consumers to choose providers of better quality and for providers to compete on quality. In 2008, Nursing Home Compare, a web portal that publishes report cards for nursing homes, made significant changes to its reporting system. It went from publishing sets of clinical outcome measures, staffing measures, and deficiency citations to a composite rating in which nursing homes are assigned one to five stars. Chapter 2 presents findings of a study evaluating whether reporting simplified, composite ratings changed consumer behavior and whether the new ratings motivated nursing homes to improve their quality scores. We used a regression discontinuity design to estimate changes in the number of admissions six months after the launch of the new system and to estimate changes in quality scores on the first health inspection after the release of the five-star ratings. To identify causal effects, regression discontinuity design takes advantage of quasi-randomization close to cut-off points in the score used to assign stars. Comparison of observed nursing home and resident-level baseline characteristics shows that nursing homes close to a cut-off point are comparable and that the assumptions of the regression discontinuity design are satisfied. Our main results show that nursing homes that obtained an additional star gained more admissions after the composite ratings were implemented, with heterogeneous effects depending on baseline star level. On the provider side, our results indicate that nursing homes that gained an additional star made improvements in their health inspection scores but not on scores based on staffing and quality measures. Collectively, these findings show that the new five-star composite rating system affected consumer choice of nursing homes and induced changes in quality scores, even though quality reporting, in a more complex form, previously existed. Therefore, the form of quality reporting matters to consumers and providers. To improve response to health care report cards, policymakers should attempt to use simplified systems such as five-star ratings. Future research should also investigate whether the new ratings truly reflect the quality of care in nursing homes. Chapter 3 describes a new statistical model to predict quality of life, measured in terms of preferences or utility over health states, when only a measure of health functioning is available. Traditionally, when data on preferences are not available, analysts rely on condition-specific or generic measures of health status like the SF-12 for predicting or mapping preferences. Such prediction is challenging because of the characteristics of preference data, which are bounded, have multiple modes, and have a large proportion of observations clustered at values of one. We developed a finite mixture model for cross-sectional data that maps the SF-12 to the EQ-5D-3L preference index, an instrument commonly used in economic evaluations. Our model characterizes the observed EQ-5D-3L index as a mixture of three distributions: a degenerate distribution with mass at values indicating perfect health and two censored (Tobit) normal distributions. Using estimation and validation samples derived from the Medical Expenditure Panel Survey 2000 dataset, we compared the prediction performance of these mixture models to that of two previously proposed methods: ordinary least squares regression (OLS) and two-part models. Our results show that finite mixture models in which predictions are based on classification outperform two-part models and OLS regression based on mean absolute error, with substantial improvement for samples with fewer respondents in good health. The potential for misclassification is reflected on larger root mean square errors. Moreover, mixture models underperform around the center of the observed distribution. These results show that that finite mixtures offer a flexible modeling approach that can take into account the idiosyncratic characteristics of the distribution of preference over health states. The use of mixture models allows researchers to obtain estimates of health preferences when only summary scores from the SF-12 and a limited number of demographic characteristics are available. Mixture models are particularly useful when the target sample does not have a large proportion of individuals in good health.