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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：卫生保健系统存在巨大差异，导致健康结果恶化