Volume 47 | Number 4 | August 2012

Abstract List

Andrew Ryan, James Burgess, Robert Strawderman, Justin Dimick


To test the accuracy of alternative estimators of hospital mortality quality using a onte arlo simulation experiment.

Data Sources

Data are simulated to create an admission‐level analytic dataset. The simulated data are validated by comparing distributional parameters (e.g., mean and standard deviation of 30‐day mortality rate, hospital sample size) with the same parameters observed in edicare data for acute myocardial infarction () inpatient admissions.

Study Design

We perform a onte arlo simulation experiment in which true quality is known to test the accuracy of the Observed‐over‐Expected estimator, the Risk Standardized Mortality Rate (), the imick and taiger () estimator, the Hierarchical oisson estimator, and the Moving Average estimator using hospital 30‐day mortality for as the outcome. Estimator accuracy is evaluated for all hospitals and for small, medium, and large hospitals.

Data Extraction Methods

Data are simulated.

Principal Findings

Significant and substantial variation is observed in the accuracy of the tested outcome estimators. The estimator is the most accurate for all hospitals and for small hospitals using both accuracy criteria (root mean squared error and proportion of hospitals correctly classified into quintiles).


The mortality estimator currently in use by edicare for public quality reporting, the , has been shown to be less accurate than the estimator, although the magnitude of the difference is not large. Pending testing and validation of our findings using current hospital data, should reconsider the decision to publicly report mortality rates using the .