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Hospital profiling using Bayesian decision theory.

Johannes Hengelbrock1, Johannes Rauh1, Jona Cederbaum1

  • 1Federal Institute for Quality Assurance and Transparency in Healthcare, Berlin, Germany.

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Summary
This summary is machine-generated.

Identifying hospital performance outliers requires a Bayesian decision framework. This approach optimizes decision rules for quality assessment, ensuring transparent and justifiable classifications for healthcare quality reporting and care improvement.

Keywords:
Bayesian decision theoryhospital profilingquality assurancequality of care

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Area of Science:

  • Health Services Research
  • Bayesian Statistics
  • Medical Quality Management

Background:

  • Evaluating hospital quality necessitates identifying performance outliers.
  • Classifying healthcare providers as outliers involves uncertainty due to unknown true quality.
  • Observed quality indicator results are used to infer true hospital quality.

Purpose of the Study:

  • To embed healthcare provider classification into a Bayesian decision theoretical framework.
  • To derive optimal decision rules based on expected decision consequences for hospital profiling.
  • To propose utility functions for external quality reporting and initiating care delivery changes.

Main Methods:

  • Utilized a Bayesian decision theoretical framework for classifying healthcare providers.
  • Employed paradigmatic utility functions for external reporting and care improvement.
  • Applied funnel plots to visualize and compare optimal decision rules; analyzed sensitivity and specificity.

Main Results:

  • The classification of outliers is significantly influenced by underlying utility functions.
  • Demonstrated the application of the methodology to hip replacement surgeries in Germany (1,277 hospitals, >180,000 procedures).
  • Developed an R package (iqtigbdt) for classifying quality indicator results.

Conclusions:

  • Framing hospital outlier classification as a decision theoretic problem yields transparent and justifiable rules.
  • The proposed methodology provides a robust framework for evaluating healthcare provider performance.
  • The R package is available on GitHub for practical implementation.