A Novel Method for the Fair Assessment of Healthcare Quality Measures and Providers Care Consistency

Vollständiger Abstract

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ABSTRACT The selection and use of quality measures in value‐based payment rely on single‐period methods that conflate sampling noise with genuine within‐provider year‐to‐year execution variability, producing assessments that are unstable across years and dependent on patient volume. We introduce a Longitudinal Beta‐Binomial model, which separates three sources of variability simultaneously and produces a year‐invariant measure‐level assessment and a volume‐independent provider‐specific assessment . Applied to five years of data (program years 2021–2025) for 744 providers on two CMS mental health quality measures, the analysis yields a striking reversal: under the standard Beta‐Binomial framework, FUH‐30 appears far more discriminating than READM‐30‐IPF (–0.87 vs 0.51–0.63 per year); separating execution variability from sampling noise reverses this conclusion ( [0.598, 0.642] for FUH‐30 vs [0.730, 0.784] for READM‐30‐IPF, 95% posterior credible intervals), because FUH‐30's year‐to‐year provider instability is three times larger than its sampling noise. The framework proposes three requirements simultaneously: discriminability ( can separate good from poor health‐care providers and is year‐invariant where swings substantially across years); fairness (both and are independent of patient volume, removing the structural disadvantage faced by small and rural providers); and actionability ( distinguishes consistently substandard from erratic providers, enabling targeted intervention and more honest measure adoption decisions).