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Analysis of hospital readmissions with competing risks
1Department of Biostatistics and Kidney Epidemiology and Cost Center, 51329University of Michigan School of Public Health, Ann Arbor, MI, USA.
Statistical Methods in Medical Research
|July 28, 2022
Summary
This study introduces a competing risk model to accurately assess hospital readmission rates, improving provider profiling by accounting for factors like death. This method offers a fairer quality measure for healthcare facilities.
Area of Science:
- Health Services Research
- Biostatistics
- Health Economics
Background:
- The 30-day hospital readmission rate is a key metric for evaluating healthcare provider performance, cost-effectiveness, and patient quality of life.
- Current logistic regression models for readmission rates are influenced by competing risks, such as mortality, potentially skewing provider comparisons.
- Facilities with higher competing risk rates may appear to have better readmission rates, creating an inaccurate quality assessment.
Purpose of the Study:
- To propose a novel discrete time competing risk model for more accurate assessment of provider-level 30-day hospital readmissions.
- To develop a quality measure, the standardized readmission ratio, unaffected by competing risks.
- To introduce efficient algorithms for model estimation and inference.
Main Methods:
- A discrete time competing risk model was developed, utilizing cause-specific readmission hazards.
- A standardized readmission ratio was proposed as a quality measure, analogous to the standardized mortality ratio.
- An efficient Blockwise Inversion Newton algorithm and a stabilized robust score test were developed for estimation and inference.
Main Results:
- The proposed competing risk model provides a more accurate assessment of provider-level effects by considering the timing of events.
- The standardized readmission ratio is not systematically biased by the rate of competing risks, offering a more equitable quality measure.
- The developed algorithms facilitate efficient estimation and inference for numerous provider effects.
- Application to dialysis patients showed improved profiling, model fitting, and outlier detection compared to existing methods.
Conclusions:
- The discrete time competing risk model offers a superior approach to analyzing 30-day hospital readmissions for provider profiling.
- The standardized readmission ratio provides a more reliable quality metric, mitigating bias from competing risks.
- The new computational methods enhance the practical application of these models in healthcare quality assessment.
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