Personalized Provider Selection to Reduce Surgical Disparities

University Of Pennsylvania, Philadelphia PA

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Abstract

ABSTRACT Colorectal cancer (CRC), the second leading cause of death in older adults in 2019, was diagnosed in 145,600 patients and was responsible for 51,020 deaths. In the absence of metastatic disease, surgery is the standard of care for more than 90% of CRC patients. Insight from existing literature and our preliminary studies suggest that the most essential surgical disparities in CRC are related to surgical risk and strong hospital- associated differences in mortality and morbidity. Significant variation in CRC surgical outcomes exists across hospitals (e.g. mortality rates 0.6%-14.7%) with known disparities adversely affecting black patients. Black patients have lower surgical utilization rates, worse surgical outcomes, and lower survival rates compared to White patients. Black patients are more likely to use lower quality, lower volume hospitals for surgery, even when a higher quality choice can be found closer to home. Significantly worse outcomes are also observed in rural settings. Access to high quality hospitals is a critical barrier to optimal surgical care. Data to drive hospital selection is limited. Our preliminary studies demonstrate that most Black patients (86%) have a higher quality hospital located within close proximity of their home and data driven referrals have the potential to reduce disparities by >30% and improve outcomes. Existing risk stratification tools to assist in the hospital selection process lack the requisite combination of factors to facilitate rational decision-making including: 1) disease specificity, 2) attention to complex patient-provider interactions, 3) information on hospital quality, and 4) comparative statistics. Our preliminary data suggest that accurate risk prediction can be performed that meet these criteria. In the proposed study, we will refine the personalized prediction models, scale them to the national level, and develop the tools to make statistical comparisons possible. As disparities drive unnecessary health care expenditures, we demonstrate the gains in Societal Welfare of data driven referrals using counterfactual simulation. Further, we will use scenario testing to simulate the effects of data driven referrals on the willingness of referring providers to trade-off convenience and reputation for enhanced quality. This information is critical to drive policy reform to advance surgical care. Our goal is to reduce disparities by referring older, black CRC patients to higher quality hospitals by 1) developing personalized risk models to differentiate across hospitals (or surgeons), 2) providing evidence to inform policies designed to incentivize data driven referrals, and 3) setting strategies to promote data driven referrals for CRC. This pioneering work will provide 1) new methods of risk stratification, 2) an estimate of the Societal Welfare benefits of data driven referrals for policy makers when designing new policies to minimize surgical disparities and 3) new knowledge on physician preferences to inform interventions to promote adoption of data driven referrals. This work will serve as a template for subsequent efforts to extend data driven referrals across all surgically treated solid organ malignancies.