UC Irvine

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.