Health equity and the impacts of EHR data bias associated with social determinants

健康公平以及与社会决定因素相关的电子病历数据偏差的影响

• 批准号: 10584190
• 负责人: Nicole Gray Weiskopf
• 金额: $ 35.54万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-04 至 2027-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10584190
• 关键词: Algorithm Design; Algorithms; Assessment tool; Automobile Driving; COVID-19 pandemic; Cardiac health; Caring; Characteristics; Clinical; Data; Data Reporting; Data Set; Development; Disparity; Documentation; Electronic Health Record; Ensure; Equitable healthcare; Equity; Ethnic Origin; Failure; Future; Goals; Health; Health Care Costs; Health system; Healthcare; Healthcare Activity; Healthcare Systems; Individual; Inequity; Ingestion; Intervention; Lead; Learning; Measures; Mediator; Medical; Methodology; Methods; Modeling; Outcome; Patient Care; Patient risk; Patient-Focused Outcomes; Patients; Pattern; Performance; Provider; Race; Research; Resources; Risk; Risk Assessment; Risk Estimate; Security; Severity of illness; Structural Models; Techniques; Testing; Time; United States; Work; black patient; clinical decision-making; clinical predictors; clinical risk; community based practice; data quality; demographics; ethnic bias; health care availability; health care delivery; health care disparity; health care service utilization; health difference; health equity; health inequalities; improved; novel; patient health information; patient population; perceived discrimination; point of care; predict clinical outcome; prediction algorithm; predictive tools; racial bias; risk prediction; risk prediction model; social determinants; social health determinants; tool

Project Summary / Abstract Achieving optimal health in the United States is challenging, in part due to inequities in social determinants of health (SDoH) like financial security, experiences of discrimination, and healthcare access. These biases may manifest in the data collected during health care in electronic health records (EHRs) and, in turn, be propagated in research and healthcare activities that use those data. In other words, real-world data will reflect real-world biases and inequities. A biased healthcare system will produce biased data. Analyses performed with biased data will produce biased results. The end result is that without appropriate understanding and intervention, these biases will perpetuate themselves, ultimately furthering inequity in health and healthcare. Increasingly, healthcare delivery has become reliant on clinical risk prediction and risk assessment algorithms that use EHR data to help identify patients who are at-risk, allocate health system resources, and inform healthcare decisions. Even if these algorithms are designed to be equally valid for all patients, if they are applied to biased data the results will also be biased. In order to improve equity in health and healthcare, it is vital that we understand biases in EHR data that are associated with social determinants of health and develop methods that can ensure that risk prediction algorithms produce valid results for all patients. Therefore, the objectives of the proposed work are to: 1) Characterize the patterns of bias in EHR data 2) Identify latent and observed factors that drive mechanisms of poor data quality 3) Evaluate the impact of data bias on clinical tasks that rely on EHR data 4) Evaluate structural modeling and debiasing methods to improve analyses conducted with EHR-derived datasets that contain bias. We will be working with data from OCHIN, a large community-based practice network, which provided care for approximately 1.8 million unique patients between 2018 and 2020. First, we will identify associations between SDoH and EHR data quality. Second, we will evaluate the accuracy of a set of representative clinical risk prediction and risk assessment algorithms to characterize the relationship between EHR data quality, algorithm performance, and SDoH. Finally, using structural models and the relationships defined in the first two aims, we will model the performance of clinical risk prediction and assessment algorithms in the EHR, and we will examine strategies for incorporating SDoH information to improve their accuracy and support appropriate clinical decision-making at the point of care.

项目总结/摘要 在美国实现最佳健康是具有挑战性的，部分原因是社会决定因素的不平等。 健康（SDoH），如财务安全，歧视的经验，和医疗保健的访问。这些偏见可能 在电子健康记录（EHR）中的医疗保健期间收集的数据中显示， 在使用这些数据的研究和医疗保健活动中传播。换句话说，真实世界的数据将反映 现实世界的偏见和不平等。有偏见的医疗保健系统会产生有偏见的数据。执行的分析 有偏差的数据将产生有偏差的结果。最终的结果是，如果没有适当的理解和 如果不采取干预措施，这些偏见将继续存在，最终加剧健康和医疗保健方面的不平等。 医疗服务越来越依赖于临床风险预测和风险评估算法 使用EHR数据来帮助识别处于风险中的患者，分配卫生系统资源， 医疗决策。即使这些算法被设计为对所有患者都同样有效，如果它们是 应用于有偏数据，结果也将有偏。为了改善健康和医疗保健的公平性， 重要的是，我们要了解EHR数据中与健康的社会决定因素相关的偏见， 可以确保风险预测算法为所有患者产生有效结果的方法。因此 拟议工作的目标是： 1)描述EHR数据中的偏差模式 2)识别导致数据质量差的潜在和观察到的因素 3)评估数据偏差对依赖EHR数据的临床任务的影响 4)评估结构建模和去偏方法，以改进使用EHR导出的 包含偏差的数据集。 我们将使用来自OCHIN的数据，OCHIN是一个大型的社区实践网络，它提供了对 在2018年至2020年期间，约有180万名独特的患者。首先，我们将确定 SDoH和EHR数据质量。其次，我们将评估一组代表性临床风险的准确性， 预测和风险评估算法，以表征EHR数据质量之间的关系，算法 性能和SDoH。最后，使用结构模型和前两个目标中定义的关系，我们 将模拟EHR中临床风险预测和评估算法的性能，我们将 研究纳入SDoH信息的策略，以提高其准确性，并支持适当的 在护理点的临床决策。