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年老年人的第二大死亡原因，在145,600中被诊断出患者并造成51,020人死亡。在没有转移性疾病的情况下，手术是标准超过90％的CRC患者的护理。现有文献和我们的初步研究的见解表明 CRC中最重要的外科手术差异与在手术风险和强大医院中的种族影响有关 - 死亡率和发病率的相关差异。 CRC手术结局的显着差异存在医院（例如，死亡率为0.6％-14.7％），已知差异对黑人患者产生不利影响。黑色的患者的手术利用率较低，手术结局较差，而生存率较低白人患者。即使可以找到更高质量的选择。这些差距超出了种族到住宅环境（例如，农村）和其他患者特征。获得更高质量的医院是实现的关键障碍整个人群的手术平等。驱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％）的医院位于其近距离附近家庭以及通过数据驱动的转介将差异降低> 30％的潜力，同时改善了整个结果人群。现有的风险分层工具以协助医院选择过程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）注意复杂的患者培训相互作用，3）有关医院质量的信息，以及4）比较统计。我们的初步数据表明，可以执行符合这些标准的准确风险预测。在提议中研究，我们将完善个性化预测模型，将其扩展到国家一级，并开发工具使统计比较成为可能。由于差距不再是脆弱者的问题，我们证明使用反事实模拟的数据驱动转介的社会福利收益。此外，我们将使用方案测试来模拟数据驱动的转介对推荐提供商的意愿的影响以提高质量的便利和声誉。这些信息对于推动政策改革至关重要提前手术权益。我们的目标是通过将较老的黑色CRC患者推荐给更高的质量来减少差异医院按1）开发个性化风险模型，以区分整个医院（或外科医生），2）提供旨在激励数据驱动推荐的政策的证据，3）设定策略来促进数据 CRC驱动的推荐。这项开创性工作将提供1）新的风险分层方法，2）估算在设计新政策以最小化时，数据驱动的推荐人的社会福利益处手术差异和3）有关医师偏好的新知识，以告知干预措施以促进采用数据驱动的推荐。这项工作将作为随后努力扩展数据驱动推荐的模板在所有手术治疗的固体器官恶性肿瘤中。