American College of Surgeons NSQIP Hospital Benchmarking Using Bayesian Variational Inference to Adjust for Many CPT Codes
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The authors aimed to evaluate the effectiveness of Bayesian automatic differentiation variational inference (ADVI) as a method for risk adjustment in the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP), particularly in comparison to the conventional CatBoost (CATB) approach. They found that while both methods showed similar discrimination for outcomes, ADVI provided superior calibration, significantly reduced computational time, and improved stability for sparse CPT code combinations, suggesting it as a more efficient alternative for hospital benchmarking in surgical outcomes.
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