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Inferring Hospital Quality from Patient Discharge Records Using a Bayesian Selection Model (1999)
BibTeX
@MISC{Departments99inferringhospital,
author = {John Geweke Departments and John Geweke and Gautam Gowrisankaran and Robert J. Town},
title = {Inferring Hospital Quality from Patient Discharge Records Using a Bayesian Selection Model},
year = {1999}
}
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Abstract
This paper develops new econometric methods to estimate hospital quality and other models with discrete dependent variables and non-random selection. Mortality rates in patient discharge records are widely used to infer hospital quality. However, hospital admission is not random and some hospitals may attract patients with greater unobserved severity of illness than others. In this situation the assumption of random admission leads to spurious inference about hospital quality. This study controls for hospital selection using a model in which distance between the patient's residence and alternative hospitals are key exogenous variables. Bayesian inference in this model is feasible using a Markov chain Monte Carlo posterior simulator, and attaches posterior probabilities to quality comparisons between individual hospitals and groups of hospitals. The study uses data on 77,937 Medicare patients admitted to 117 hospitals in Los Angeles County from 1989 through 1992 with a diagnosis of pneu...
Keyphrases
hospital quality patient discharge record bayesian selection model alternative hospital los angeles county discrete dependent variable mortality rate key exogenous variable new econometric method hospital selection hospital admission unobserved severity random admission spurious inference attache posterior probability bayesian inference medicare patient non-random selection individual hospital
