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Personalized Provider Selection to Reduce Surgical Disparities

个性化的医疗服务提供者选择以减少手术差异

基本信息

批准号：
10624968
负责人：
Rachel Kelz
金额：
$ 64.33万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-05-19 至 2027-02-28
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10624968
关键词：
Address Adoption Affect American College of Surgeons Attention Black Populations Black race Cancer Patient Caring Cause of Death Cessation of life Characteristics Colorectal Cancer Communities Complex Data Decision Aid Decision Making Development Diagnosis Disadvantaged Disease Disparity Educational Status Elderly Equity Event Familiarity Goals Guidelines Home Hospitals Incentives Inferior Intervention Knowledge Linear Models Literature Low income Malignant Neoplasms Medicare Methods Minority Modeling Morbidity - disease rate Operative Surgical Procedures Organ Outcome Participant Patient risk Patient-Focused Outcomes Patients Pattern Perception Performance Physicians Pilot Projects Policies Policy Maker Population Predictive Analytics Process Provider Quality of Care Questionnaires Race Recommendation Research Resources Risk Running Rural Sampling Science Societies Solid Specific qualifier value Specificity Surgeon Survival Rate Testing Training Travel Treatment outcome Uncertainty United States National Institutes of Health Variant Vulnerable Populations Work adjudication adverse outcome black patient cancer care cancer health disparity cancer surgery cohort colon cancer patients colorectal cancer treatment comparative design disparity elimination disparity reduction experience experimental study health care delivery health equity improved improved outcome insight interest machine learning algorithm mortality multidisciplinary new technology novel outcome prediction personalized predictions personalized risk prediction predictive modeling preference response risk prediction risk prediction model risk stratification simulation standard of care statistics surgery outcome surgical disparities surgical risk tool treatment disparity uptake welfare willingness

项目摘要

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 race effects in 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. These disparities extend beyond race to residential setting (e.g. rural) and other patient characteristics. Access to higher quality hospitals is a critical barrier to achieving surgical equity across the population. 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 the potential to reduce disparities by >30% with data driven referrals while improving outcomes across populations. 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 are no longer a problem for the vulnerable alone, 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 equity. 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.

结直肠癌（CRC）是2019年老年人死亡的第二大原因，确诊人数为14.56万人