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

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 $\tau$ and a volume-independent provider-specific assessment ${\tau }_n$ . 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 ( ${\rho }^{\left(j\right)}=0.78$ -0.87 vs 0.51-0.63 per year); separating execution variability from sampling noise reverses this conclusion ( $\tau =0.621$ [0.598, 0.642] for FUH-30 vs $\tau =0.758$ [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 ( $\tau$ can separate good from poor health-care providers and is year-invariant where ${\rho }^{\left(j\right)}$ swings substantially across years); fairness (both $\tau$ and ${\tau }_n$ are independent of patient volume, removing the structural disadvantage faced by small and rural providers); and actionability ( ${\tau }_n$ distinguishes consistently substandard from erratic providers, enabling targeted intervention and more honest measure adoption decisions).