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Control limits to identify outlying hospitals based on risk-stratification.

Valentin Rousson1, Marie-Annick Le Pogam2, Yves Eggli2

  • 11 Division of Biostatistics, Institute for Social and Preventive Medicine, University Hospital Lausanne, Switzerland.

Statistical Methods in Medical Research
|September 21, 2016
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Summary

This study introduces a unified framework for calculating hospital quality indicators. The method accurately detects hospitals performing statistically worse than average using risk stratification for both binary and continuous outcomes.

Keywords:
Adjusted expected valuescontrol limitsfunnel plotoutcome indicatorquality of carestratification

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Area of Science:

  • Health Services Research
  • Biostatistics
  • Quality Improvement

Background:

  • Hospital quality is assessed using outcome indicators comparing observed to expected values.
  • Risk adjustment for patient case mix is crucial for accurate hospital comparisons.
  • Existing methods for calculating control limits in risk-adjusted indicators can be complex.

Purpose of the Study:

  • To propose a simple, unified framework for calculating control limits for risk-adjusted hospital outcome indicators.
  • To enable the distinction between variability due to stratification and variability due to chance.
  • To account for uncertainty in estimating expected values and detect hospitals performing significantly worse.

Main Methods:

  • Developed a framework for calculating control limits based on patient risk stratification.
  • Applied the method to both binary (e.g., adverse events) and continuous (e.g., length of stay) outcome indicators.
  • Validated the approach using Swiss hospital discharge data.

Main Results:

  • The proposed framework successfully distinguishes variability sources and accounts for estimation uncertainty.
  • It allows for the detection of hospitals performing statistically worse than average.
  • The method is applicable to diverse hospital outcome indicators.

Conclusions:

  • The unified framework provides a reliable method for calculating control limits in risk-adjusted hospital quality indicators.
  • This approach enhances the accuracy and interpretability of hospital performance comparisons.
  • The method is versatile, supporting both binary and continuous outcomes in healthcare quality assessment.