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

• 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
• 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

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年老年人第二大死亡原因，在145,600人中被诊断出 并造成51,020人死亡。在没有转移性疾病的情况下，手术是标准的 为90%以上的结直肠癌患者提供医疗服务。对现有文献的洞察和我们的初步研究表明 结直肠癌最本质的手术差异与手术风险中的种族效应和强大的医院- 死亡率和发病率的相关差异。结直肠癌手术结果在不同地区存在显著差异 医院的死亡率(例如，死亡率为0.6%-14.7%)与已知的差异对黑人患者造成不利影响。黑色 患者的手术使用率较低，手术结果较差，存活率较低。 白人病人。黑人患者更有可能使用质量较低、流量较小的医院进行手术，即使在 更高质量的选择可以在离家更近的地方找到。这些差距从种族延伸到居住环境。 (例如农村)和其他患者特征。获得更高质量的医院是实现 外科手术在整个人口中的公平。推动医院选择的数据有限。我们的初步研究 证明大多数黑人患者(86%)在他们的医院附近有更高质量的医院 通过数据驱动的转诊将差距缩小30%的潜力，同时改善以下方面的结果 人口。现有的帮助医院选择过程的风险分层工具缺乏必要的 促进理性决策的因素组合包括：1)疾病特异性，2)注意 复杂的患者-提供者交互，3)关于医院质量的信息，以及4)比较统计。我们的 初步数据表明，只要满足这些标准，就可以进行准确的风险预测。在建议的 在研究中，我们将提炼个性化预测模型，将其扩展到国家层面，并开发工具 使统计比较成为可能。由于贫富差距不再只是弱势群体的问题，我们 使用反事实模拟演示数据驱动的转诊在社会福利方面的收益。此外，我们 我将使用场景测试来模拟数据驱动的推荐对推荐提供者意愿的影响 在便利性和声誉之间进行权衡，以提高质量。这些信息对于推动政策改革至关重要 预付手术费用。我们的目标是通过将年长的黑人结直肠癌患者转诊到更高质量的患者来减少差异 医院通过1)开发个性化风险模型以区分不同医院(或外科医生)，2)提供 为旨在激励数据驱动转诊的政策提供信息的证据，以及3)制定促进数据的战略 推动儿童权利委员会的转介。这项开创性的工作将提供1)新的风险分层方法，2)估计 在设计新政策时，为政策制定者提供数据驱动的转诊所带来的社会福利好处 外科差异和3)关于医生偏好的新知识，以指导促进采用的干预措施 数据驱动的推荐。这项工作将作为扩展数据驱动转诊的后续工作的模板 所有经过手术治疗的实体器官恶性肿瘤。