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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要/摘要在美国实现最佳健康状况具有挑战性，部分原因是社会决定因素的不平等健康 (SDoH)，例如财务安全、歧视经历和医疗保健获取。这些偏见可能体现在电子健康记录 (EHR) 中医疗保健期间收集的数据中，进而被在使用这些数据的研究和医疗保健活动中传播。换句话说，现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。进行的分析有偏见的数据会产生有偏见的结果。最终的结果是如果没有适当的理解和如果不进行干预，这些偏见就会长期存在，最终加剧健康和医疗保健方面的不平等。医疗保健服务越来越依赖于临床风险预测和风险评估算法使用 EHR 数据帮助识别有风险的患者、分配卫生系统资源并告知医疗保健决策。即使这些算法被设计为对所有患者同样有效，如果它们是应用于有偏差的数据，结果也会有偏差。为了提高健康和医疗保健的公平性，至关重要的是，我们了解与健康和发展的社会决定因素相关的电子病历数据中的偏差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机制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 衍生的分析包含偏差的数据集。我们将使用来自 OCHIN 的数据，OCHIN 是一个大型社区实践网络，为 2018 年至 2020 年间约有 180 万独特患者。首先，我们将确定之间的关联 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来表征 EHR 数据质量、算法之间的关系性能和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，我们将检查纳入 SDoH 信息的策略，以提高其准确性并支持适当的护理点的临床决策。