Supplementary materials for this article are available online. To provide good control and correctly calibrated rates, we propose direct standardization instead. We find that indirect standardization, as currently used by Hospital Compare, fails to adequately control for differences in patient risk factors and systematically underestimates mortality rates at the low volume hospitals. For the ultimate purpose of comparisons, hospital mortality rates must be standardized to adjust for patient mix variation across hospitals. To make appropriately calibrated predictions, our revised models incorporate information about hospital volume, nursing staff, medical residents, and the hospital’s ability to perform cardiovascular procedures. This process of calibrating individualized predictions against general empirical advice leads to substantial revisions in the Hospital Compare model for AMI mortality. Here, we calibrate these Bayesian recommendation systems by checking, out of sample, whether their predictions aggregate to give correct general advice derived from another sample.
The HQA: Improving Care Through Information, a public-private collaboration, was created in December 2002 to promote reporting on hospital quality of care. Before individualized Bayesian recommendations, people derived general advice from empirical studies of many hospitals, for example, prefer hospitals of Type 1 to Type 2 because the risk is lower at Type 1 hospitals. Hospital Compare was created through the efforts of Medicare and the Hospital Quality Alliance (HQA). Except for the largest hospitals, these individual recommendations or predictions are not checkable against data, because data from smaller hospitals are too limited to provide a meaningful check. Hospital Compare’s current recommendations are based on a random-effects logit model with a random hospital indicator and patient risk factors. In particular, 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 code#
Simply type in your zip code or city and state to access a wealth of information, including data on 44 quality measures. Bayesian models are increasingly fit to large administrative datasets and then used to make individualized recommendations. It includes quality measures that show how often hospitals provide the recommended care for patients with specific. By visiting, you can quickly access the Hospital Compare tool which analyzes and compares data about the quality of care at more than 4,700 hospitals across the country.
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