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September 2, 2026Statistics in MedicineOpen Access

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

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Authors

CNCédric NeumannAMAndrew Anthony MatasJGJeffrey Geppert

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Overview

Statistical evaluation demonstrates volume-independent performance assessment across 744 healthcare providers, suggesting standard quality metrics conflate annual execution variability with noise.

Key Points

  • To introduce a Longitudinal Beta-Binomial model that separates sampling noise from genuine within-provider execution variability for fair healthcare quality measurement.
  • Formulated a Longitudinal Beta-Binomial model to distinguish sampling noise, provider-level year-to-year execution variability, and persistent differences across healthcare providers.
  • Analyzed five program years (2021–2025) of data for N=744 healthcare providers evaluated on two CMS mental health quality measures: FUH-30 and READM-30-IPF.
  • Standard Beta-Binomial modeling initially indicated FUH-30 was more discriminating than READM-30-IPF (annual reliability 0.77–0.87 vs 0.51–0.63 per year).
  • Separating execution variability reversed the ranking, showing lower discriminability for FUH-30 than READM-30-IPF (posterior credible interval [0.598, 0.642] vs [0.730, 0.784]) due to FUH-30 displaying year-to-year provider instability three times larger than sampling noise.
  • The longitudinal model decoupled provider assessment from patient volume, removing systematic evaluation biases against small and rural providers while effectively separating consistently low performers from erratic ones.

Cite This Study

Neumann et al. (2026) studied this question.

synapsesocial.com/papers/6a97e28dc562ede874ec6bd2https://doi.org/10.1002/sim.70722
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