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Data-driven hospital selection: A mechanism to address racial disparities in surgery for colorectal cancer
Gao, Linwei
Gao, Linwei
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Thesis/Dissertation
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2025-08
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Statistics
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https://doi.org/10.34944/1bnf-q767
Abstract
Disparities in surgical outcomes across the United States are often driven by variations in hospital quality. Both pre-operative health status and the choice of care site have been identified as key factors influencing surgical outcomes. This dissertation introduces a Bayesian hierarchical model (BHM) to support hospital selection to improve individual patient outcomes. Leveraging patient medical history, the model identifies hospitals that maximize the probability of survival and freedom from serious morbidity. The methodology is applied to statewide hospital data from Florida, recommending alternative hospitals based on outcome optimization while accounting for geographic proximity. Notably, our BHM-guided hospital referral strategy demonstrates a significant capacity to reduce existing racial disparities in surgery outcomes. Results show that data-informed hospital selection can substantially enhance surgical outcomes and promote health equity, highlighting the potential of Bayesian approaches to address quality and racial disparities in healthcare delivery.
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