Novel Algorithmic Fairness Tools for Reducing Health Disparities in Primary Care

项目摘要：卫生保健系统中的差距很大，导致健康状况恶化 以及对边缘化群体的护理质量。这些差异反映了我们目前的卫生系统有一个 不公平的均衡。卫生保健数据中嵌入了社会偏见，包括种族主义和 获得照顾来自低社会经济背景和农村地区的个人。然而，许多人 算法方法不足以解决健康差异，因为算法不能 评估或优化这些团队的绩效。现有工具可改善以下各项的差异性能 现实卫生保健环境中的多个边缘化群体极其有限。我们的创新方法 健康差距中的数据和算法偏差问题是创造出第一个此类总体问题 针对多个边缘群体的算法公平框架。在初始阶段，我们将重点关注数据 转换-对数据进行干预，以便对其进行去偏向，以表示所需的平衡，而不是 加剧了不公平的均衡。第二阶段构建新的公平回归估计量以加强公平性 预测的约束条件。我们的目标是创建可重复使用的工具，以促进公平地提供卫生保健 关心。我们将通过开发遵循道德流水线的通用方法来实现这一点 由健康的社会决定因素框架指导的算法。我们的具体目标是：(1)开发和测试 新的数据转换方法，依赖于微模拟来消除医疗保健数据的偏差，(2)开发 并测试了新的针对多组优化的公平惩罚回归方法，(3)测试了该方法的性能 新的算法框架在慢性肾脏疾病优先排序中的高影响力初级保健应用 公平对待面临健康差距的多个种族和民族，以及(4)创建开源 计算工具，教程小插曲，以及可重复研究和合成数据资源 传播。拟议的研究将产生一种统计创新的可重复使用的算法公平性 统一数据转换和公平回归的框架，通过在 慢性肾脏疾病护理质量研究。该初级保健应用程序将利用丰富的注册表数据， 包括对健康的社会决定因素的测量，在通常的护理环境中从 多个支付者的地理、种族和民族多样化的人口。我们的方法中心 具有严格的方法学设计的稳健性，包括与其他现有估计器和 综合模拟研究和国家真实世界登记册数据的标准做法。解决健康问题 初级保健方面的差距--持续、协调的保健中心--有可能对以下方面产生重大影响 通过医疗保健系统改善公共健康。我们框架的广泛适用性和创建 可重复使用的计算工具将促进在许多实际环境中的部署。

PROJECT SUMMARY: Disparities in the health care system are substantial, leading to worse health outcomes and quality of care for marginalized groups. These disparities reflect that our current health system has an inequitable equilibrium. Imbedded within health care data are societal biases, including racism and barriers in access to care for individuals from low socioeconomic backgrounds and rural areas. However, many algorithmic approaches are inadequate for addressing health disparities because the algorithms do not evaluate or optimize performance in these groups. Existing tools to ameliorate differential performance for multiple marginalized groups in realistic health care settings are extremely limited. Our innovative approach to the data and algorithmic bias problems in health disparities is to create a first-of-its-kind overarching algorithmic fairness framework for multiple marginalized groups. In the initial phase, we will focus on data transformations—intervening on the data in order to ‘de-bias’ it to represent a desired equilibrium rather than reinforcing the unfair equilibrium. The second stage builds novel fair regression estimators to enforce fairness constraints for prediction. Our goal is to create reusable tools that advance the equitable provision of health care. We will accomplish this by developing generalizable methodology that follows an ethical pipeline for algorithms guided by a social determinants of health framework. Our specific aims are to: (1) develop and test novel data transformation methods that rely on microsimulations for de-biasing health care data, (2) develop and test new fair penalized regression approaches optimized for multiple groups, (3) test the performance of the new algorithmic framework for a high-impact primary care application in chronic kidney disease prioritizing fairness for multiple racial and ethnic groups facing health disparities, and (4) create open-source computational tools, tutorial vignettes, and a synthetic data resource for reproducible research and dissemination. The proposed research will yield a statistically innovative reusable algorithmic fairness framework unifying data transformations and fair regression to reduce health disparities with robust testing in a chronic kidney disease study of quality of care. This primary care application will leverage rich registry data, including measurements of social determinants of health, collected in usual care settings from a geographically, racially, and ethnically diverse population across multiple payers. Our approach centers robustness with rigorous methodological design, including comparisons to alternative existing estimators and standard practice in comprehensive simulation studies and national, real-world registry data. Addressing health disparities in primary care—a hub of continuous, coordinated care—has the potential for substantial impact on improving public health via the health care system. The broad applicability of our framework and creation of reusable computational tools will facilitate deployment in many practical settings.