Performance characteristics of profiling methods and the impact of inadequate case-mix adjustment

Abstract

Profiling or evaluation of health care providers involves the application of statistical models to compare each provider’s performance with respect to a patient outcome, such as unplanned 30-day hospital readmission, adjusted for patient case-mix characteristics. The nationally adopted method is based on random effects (RE) hierarchical logistic regression models. Although RE models are sensible for modeling hierarchical data, novel high dimensional fixed effects (FE) models have been proposed which may be well-suited for the objective of identifying sub-standard performance. However, there are limited comparative studies. Thus, we examine their relative performance, including the impact of inadequate case-mix adjustment.

Keywords:

• Fixed effects
• Random effects
• Hierarchical logistic regression
• Profiling analysis

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.