Impact of case-mix measurement error on estimation and inference in profiling of health care providers

Abstract

Profiling analysis aims to evaluate health care providers by modeling each provider’s performance with respect to a patient outcome, such as unplanned hospital readmission. High-dimensional regression models are used in profiling to risk-adjust for patient case-mix covariates. Case-mix covariates typically ascertained from administrative databases are inherently error-prone. We examine the impact of case-mix measurement error (ME) on profiling models. The results show that even though the models’ coefficient estimates are biased, this does not affect the estimation of standardized readmission ratio (SRR). However, ME leads to increased variation in SRR estimates and degrades the ability to identify under-performing providers.

Keywords:

• Fixed effects
• hierarchical logistic regression
• measurement error
• profiling analysis
• random effects

Acknowledgments

This study was supported by NIDDK grants R01 DK092232 and K23 DK102903. The interpretation/reporting of the data presented are the responsibility of the authors and in no way should be seen as an official policy or interpretation of the U.S. government. We are grateful for comments from anonymous reviewers.

Additional information

Funding

National Institute of Diabetes and Digestive and Kidney Diseases R01 DK092232 and K23 DK102903.