Accuracy of site benchmarking in clinical quality registries of varying size.

ABSTRACT: Background: There is increasing interest in the public reporting of health provider benchmarking within clinical registries to identify underperforming sites (also known as outliers). As such, research into the optimal methods and ideal conditions for outlier detection is important. Objective: The aim of this study was to assess the accuracy of benchmarking and outlier classification methods for different values of clinical registry sizes and case volume minimums. Method: Clinical registry datasets were parametrically simulated varying the following parameters: number of sites, clinicians, patients and outcome events, case volume minimum and outcome prevalence. Two benchmarking models (unadjusted and risk-adjusted with logistic regression) and two outlier classificatio

SUBMITTER: Hansen J 

PROVIDER: S-EPMC12756518 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature