A comparison of a Bayesian vs. a frequentist method for profiling hospital performance.
Risk-adjustment and provider profiling have become common terms as the medical profession attempts to measure quality and assess value in health care. One of the areas of care most thoroughly developed in this regard is quality assessment for coronary artery bypass grafting (CABG). Because in-hospital mortality following CABG has been studied extensively, risk-adjustment mechanisms are already being used in this area for provider profiling. This study compares eight different risk-adjustment methods as applied to a CABG surgery population of 28 providers. Five of the methods use an external risk-adjustment algorithm developed in an independent population, while the other three rely on an internally developed logistic model. The purposes of this study are to: (i) create a common metric by which to display the results of these various risk-adjustment methodologies with regard to dichotomous outcomes such as in-hospital mortality, and (ii) to compare how these risk-adjustment methods quantify the 'outlier' standing of providers. Section 2 describes the data, the external and internal risk-adjustment algorithms, and eight approaches to provider profiling. Section 3 then demonstrates the results of applying these methods on a data set specifically collected for quality improvement.