Gautam Gowrisankaran, Principal Investigator

Estimating Hospital Quality Using a Bayesian Selection Model

  In this project, the researchers estimate the quality of care for pneumonia patients being treated by hospitals in Southern California. Using Bayesian methods to control for patient selection based on severity of illness. The use of Bayesian methods has a number of advantages but is very computationally intensive. Because of the interactive nature of the Bayesian estimation algorithms, the researchers were able to parallelize the computation.

  This researcher sought to estimate the effects of competition for both Medicare and HMO patients on the quality decisions of hospitals in Southern California. It was found that increases in the degree of competition for HMO patients decrease risk-adjusted hospital mortality rates. Conversely, increases in competition for Medicare enrollees are associated with increases in risk-adjusted mortality rates for hospitals. In conjunction with previous research, estimates by the researchers indicated that increasing competition for HMO patients appears to reduce price and save lives, and hence appears to be welfare improving. However, increases in competition for Medicare appear to reduce quality and perhaps reduces welfare. The net effect of a given merger on hospital quality depends on the geographic distribution of different payer groups.