Novel Algorithmic Fairness Tools for Reducing Health Disparities in Primary Care

用于减少初级保健健康差异的新颖算法公平工具

基本信息

批准号：
10676234
负责人：
Sherri Rose
金额：
$ 32.38万
依托单位：
STANFORD UNIVERSITY
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：
2022-08-03 至 2026-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10676234
关键词：
Address Algorithms Chronic Kidney Failure Communities Comprehensive Health Care Data Data Science Data Set Data Sources Development Disparity Equilibrium Equity Ethics Ethnic Population Geography Goals Grant Health Health Services Accessibility Health system Healthcare Healthcare Systems Individual Inequity Institute of Medicine (U.S.)Knowledge Literature Measurement Measures Methodology Methods Outcome Patient Care Performance Phase Population Heterogeneity Primary Care Process Public Health Quality of Care Reduce health disparities Reproducibility Research Saran Specific qualifier value Statistical Methods Techniques Testing United States United States National Library of Medicine Work adaptive learning algorithm development algorithmic bias biomedical informatics care coordination clinical care computerized tools data registry data resource design disparity reduction ethnic diversity ethnic health disparity ethnic minority experimental study flexibility health care delivery health care disparity health care settings health data health disparity health equity health outcome disparity improved innovation insight interest marginalization marginalized population novel open source open source tool performance tests poor health outcome process optimization racial disparity racial diversity racial minority racial population racism rural area simulation social health determinants socioeconomics statistical learning tool treatment as usual

项目摘要

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.

项目摘要：医疗保健系统的差异巨大，导致健康结果更差这些差异反映了我们当前的卫生系统存在的问题。医疗保健数据中隐藏着社会偏见，包括种族主义和障碍。来自低社会经济背景和农村地区的个人获得护理的机会然而，许多人都无法获得护理。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组中的现有工具以改善差异性能。在现实的医疗保健环境中，我们的创新方法极其有限。健康差异中的数据和算法偏差问题是要创建一个史无前例的总体针对多个边缘群体的算法公平框架在初始阶段，我们将关注数据。转换——对数据进行干预，以使其“去偏”以代表期望的平衡，而不是第二阶段构建新颖的公平回归估计器以加强公平性。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服务。我们将通过开发遵循道德管道的通用方法来实现这一目标。由健康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。依赖微观模拟来消除医疗保健数据偏差的新颖数据转换方法，(2) 开发并测试针对多个组优化的新公平惩罚回归方法，（3）测试性能用于慢性肾病优先的高影响力初级保健应用的新算法框架为面临健康差异的多个种族和族裔群体提供公平性，以及 (4) 创建开源计算工具、教程插图和用于可重复研究的合成数据资源所提出的研究将产生专业创新的可重用算法公平性。统一数据转换和公平回归的框架，通过可靠的测试来减少健康差异慢性肾病护理质量研究该初级保健应用程序将利用丰富的注册数据，包括对健康的社会决定因素的测量，这些测量是在常规护理环境中从我们的服务中心涵盖多个付款人的地理、种族和族裔背景。严格方法设计的稳健性，包括与替代现有估计器的比较和综合模拟研究和国家真实世界登记数据的标准做法。初级保健（持续、协调的护理中心）的差异可能会对通过医疗保健系统改善公共卫生。我们的框架和创建的广泛适用性。可重复使用的计算工具将有助于在许多实际环境中进行部署。