  Bayesian models are increasing fit to large administrative data sets and then
used to make individualized recommendations. For instance, Medicare's Hospital
Compare webpage provides information to patients about specific hospital
mortality rates for a heart attack or Acute Myocardial Infarction (AMI).
Hospital Compare's current recommendations are based on a random effects logit
model with a random hospital indicator and patient risk factors. By checking
the out of sample calibration of their individualized predictions against
general empirical advice, we are led to substantial revisions of the Hospital
Compare model for AMI mortality. As opposed to Hospital Compare, our revised
models incorporate information about hospital volume, nursing staff, medical
residents, and the hospital's ability to perform cardiovascular procedures,
information that is clearly needed if a model is to make appropriately
calibrated predictions. Additionally, we contrast several methods for
summarizing a model's predictions for use by the public. We find that indirect
standardization, as currently used by Hospital Compare, fails to adequately
control for differences in patient risk factors, whereas direct standardization
provides good control and is easy to interpret.
