Hospital Peer Groups, Reliability, and Stabilization: Shrinking to the Right Mean

• Activity Number: 27
• Type: Topic Contributed
• Date/Time: Sunday, August 4, 2013 : 2:00 PM to 3:50 PM
• Sponsor: Section for Statistical Programmers and Analysts
• Author(s): Alex Bohl*+ and David Jones and Dmitriy Poznyak and Jessica Ross and Eric Schone and Frank B Yoon and Joe Zickafoose and Sam Stalley
• Companies: Mathematica Policy Research and Mathematica Policy Research and Mathematica Policy Research and Mathematica Policy Research and Mathematica Policy Research and Mathematica Policy Research and Mathematica Policy Research and Mathematica Policy Research
• Keywords: Health Policy ; Hospital Quality Indicators

Abstract

The AHRQ Quality Indicators (QIs) are reliability-adjusted or "smoothed" to the national mean to deal with the unstable QIs estimates due to hospitals with small numbers of denominator cases and rare outcomes. Differences in hospital scope, size, and other characteristics allude to the possibility that smoothing to target means determined by hospital attributes, or "peer groups," may reduce bias when comparing hospitals on their estimated QIs. Current research suggests that incorporating peer-group targets into the risk-adjustment model through random effects is not feasible due to high-dimensional parameters and computational limits of MCMC estimation. Two alternative approaches are to: (1) smooth to a peer-group's risk-adjusted mean in the current framework (using an empirical estimate of reliability using the signal-to-noise ratio); or (2) add fixed effects for hospital characteristics to the risk-adjustment model. This study aims to compare the performance of these alternative peer-group smoothing methods and discuss their conceptual implications. We will judge model performance based on changes to model efficiency and reduction in comparison bias.