Racial Bias in a VA Algorithm for High-Risk Veterans

针对高风险退伍军人的 VA 算法中的种族偏见

基本信息

批准号：
10625965
负责人：
Amol S Navathe
金额：
--
依托单位：
PHILADELPHIA VA MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-02-01 至 2025-01-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10625965
关键词：
Adopted Affect African American African American population Algorithms Care given by nurses Caring Cessation of life Characteristics Clinical Data Enrollment Equity Exposure to Face Funding Future Goals Health Home Hospitalization Individual Investigation Investments Laboratories Methods Mission Modeling Needs Assessment Outcome Palliative Care Patients Predictive Analytics Primary Care Psychosocial Stress Race Reduce health disparities Reporting Research Resource Allocation Resources Risk Services Structural Racism Subgroup Surveys Testing Veterans Veterans Health Administration adverse outcome analytical tool clinical application clinical care data warehouse experience health care disparity health care service health information technology high risk hospice environment housing instability improved inequitable distribution innovation mortality novel patient subsets prediction algorithm predictive modeling primary care clinician programs racial bias racial disparity risk prediction risk prediction model screening social health determinants telehealth transportation access

项目摘要

PROJECT SUMMARY African-American Veterans are at particular risk of adverse outcomes, including mortality and hospitalization, due to adverse social determinants of health (SDoH) including poor transportation access and housing instability. Identifying individuals at risk of adverse outcomes has been a priority at the Veterans Health Administration (VA), which has implemented novel predictive analytic tools in clinical care settings to target care resources efficiently and equitably. The VA has invested an average of 5% of total VA spending towards health information technology to support such algorithms. One predictive algorithm implemented nationwide and commonly used by VA clinicians is the Care Assessment Needs (CAN) score, which predicts risk of future hospitalization and/or death for over 5 million Veterans receiving primary care. The CAN score is currently used by patient-aligned care teams (PACTs) and nurse care navigators to direct clinical programs and resources, including telehealth, palliative care, and home-based primary care, to high-risk Veterans. The CAN score is primarily based on laboratory, demographic, utilization, and other administrative data. Recent studies have shown that similar algorithms used in non-VA settings may mischaracterize risk for vulnerable patient subgroups – including African-Americans – whose health is heavily influenced by disproportionate exposure to adverse SDoH. Importantly, race and SDoH are not routine inputs into the CAN score. There is a growing concern that algorithms like the CAN score could generate “algorithmically unfair” predictions that systematically mischaracterize risk for subgroups – particularly African-Americans – whose care is heavily influenced by SDoH. However, there has been no systematic investigation into unfairness of the CAN score between African-American and White Veterans. In this project, we will systematically examine algorithmic unfairness in the VA CAN algorithm and develop approaches to mitigate it, including testing the incorporation of SDoH metrics. Our preliminary investigations into the CAN score show that it underestimates risk for African-Americans compared to White Veterans, which may lead to fewer referrals of high risk African-American Veterans to clinical programs. In Aim 1, we will develop methods to mitigate algorithmic unfairness in the CAN score using its existing variables. In Aim 2, we will incorporate race and select metrics of SDoH that are available through VA screening efforts into the CAN score to improve algorithmic unfairness. In Aim 3, we will use the “Fair” CAN score generated in Aim 2 to investigate how mitigating unfairness would change the racial composition of Veterans enrolled in clinical programs targeted at high-risk Veterans.

项目概要非裔美国退伍军人尤其面临不良后果的风险，包括死亡和住院治疗，由于不利的健康社会决定因素（SDoH），包括交通便利和住房条件差识别存在不良后果风险的个人一直是退伍军人健康部门的首要任务。管理局（VA），在临床护理环境中实施了新颖的预测分析工具来瞄准 VA 平均将 VA 总支出的 5% 投入到医疗资源中。支持此类算法的健康信息技术在全国范围内实施。 VA 爱好者常用的是护理评估需求 (CAN) 评分，它可以预测未来的风险目前有超过 500 万接受初级护理的退伍军人住院和/或死亡。供患者护理团队 (PACT) 和护士护理导航员用来指导临床计划和向高危退伍军人提供远程医疗、姑息治疗和家庭初级保健等资源。 CAN 分数主要基于实验室、人口统计、利用率和其他管理数据。最近的研究表明，在非 VA 环境中使用的类似算法可能会错误地描述风险弱势患者亚群——包括非裔美国人——他们的健康受到以下因素的严重影响不成比例地暴露于不利的 SDoH 重要的是，种族和 SDoH 并不是 CAN 的常规输入。人们越来越担心 CAN 评分等算法可能会产生“算法不公平”。系统性地错误描述亚群体风险的预测 — — 特别是非裔美国人 — — 医疗服务受到 SDoH 的严重影响，但目前尚未对医疗服务的不公平性进行系统调查。非裔美国人和白人退伍军人之间的 CAN 得分。在这个项目中，我们将系统地检查VA CAN算法中的算法不公平性并开发缓解该问题的方法，包括测试 SDoH 指标的纳入。 CAN 评分显示，与白人退伍军人相比，它低估了非裔美国人的风险，这可能会导致更少的高风险非裔美国退伍军人转诊至临床计划在目标 1 中，我们将在目标 2 中，我们使用现有变量开发方法来减轻 CAN 评分中的算法不公平性。将通过 VA 筛选工作将 SDoH 的种族和选择指标纳入 CAN 在目标 3 中，我们将使用目标 2 中生成的“公平”CAN 分数来改善算法不公平性。调查减轻不公平现象将如何改变参加临床的退伍军人的种族构成针对高风险退伍军人的计划。