Justin M List, Paul Palevsky, Suzanne Tamang, Susan Crowley, David Au, William C Yarbrough, Amol S Navathe, Craig Kreisler, Ravi B Parikh, Jessica Wang-Rodriguez, J Stacey Klutts, Paul Conlin, Leonard Pogach, Esther Meerwijk, Ernest Moy
{"title":"消除临床决策支持算法中的种族偏见:退伍军人健康管理局的使用案例。","authors":"Justin M List, Paul Palevsky, Suzanne Tamang, Susan Crowley, David Au, William C Yarbrough, Amol S Navathe, Craig Kreisler, Ravi B Parikh, Jessica Wang-Rodriguez, J Stacey Klutts, Paul Conlin, Leonard Pogach, Esther Meerwijk, Ernest Moy","doi":"10.1089/heq.2023.0037","DOIUrl":null,"url":null,"abstract":"<p><p>The Veterans Health Administration uses equity- and evidence-based principles to examine, correct, and eliminate use of potentially biased clinical equations and predictive models. We discuss the processes, successes, challenges, and next steps in four examples. We detail elimination of the race modifier for estimated kidney function and discuss steps to achieve more equitable pulmonary function testing measurement. We detail the use of equity lenses in two predictive clinical modeling tools: Stratification Tool for Opioid Risk Mitigation (STORM) and Care Assessment Need (CAN) predictive models. We conclude with consideration of ways to advance racial health equity in clinical decision support algorithms.</p>","PeriodicalId":36602,"journal":{"name":"Health Equity","volume":"7 1","pages":"809-816"},"PeriodicalIF":2.6000,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10698768/pdf/","citationCount":"0","resultStr":"{\"title\":\"Eliminating Algorithmic Racial Bias in Clinical Decision Support Algorithms: Use Cases from the Veterans Health Administration.\",\"authors\":\"Justin M List, Paul Palevsky, Suzanne Tamang, Susan Crowley, David Au, William C Yarbrough, Amol S Navathe, Craig Kreisler, Ravi B Parikh, Jessica Wang-Rodriguez, J Stacey Klutts, Paul Conlin, Leonard Pogach, Esther Meerwijk, Ernest Moy\",\"doi\":\"10.1089/heq.2023.0037\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The Veterans Health Administration uses equity- and evidence-based principles to examine, correct, and eliminate use of potentially biased clinical equations and predictive models. We discuss the processes, successes, challenges, and next steps in four examples. We detail elimination of the race modifier for estimated kidney function and discuss steps to achieve more equitable pulmonary function testing measurement. We detail the use of equity lenses in two predictive clinical modeling tools: Stratification Tool for Opioid Risk Mitigation (STORM) and Care Assessment Need (CAN) predictive models. We conclude with consideration of ways to advance racial health equity in clinical decision support algorithms.</p>\",\"PeriodicalId\":36602,\"journal\":{\"name\":\"Health Equity\",\"volume\":\"7 1\",\"pages\":\"809-816\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2023-11-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10698768/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Health Equity\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1089/heq.2023.0037\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Health Equity","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1089/heq.2023.0037","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
Eliminating Algorithmic Racial Bias in Clinical Decision Support Algorithms: Use Cases from the Veterans Health Administration.
The Veterans Health Administration uses equity- and evidence-based principles to examine, correct, and eliminate use of potentially biased clinical equations and predictive models. We discuss the processes, successes, challenges, and next steps in four examples. We detail elimination of the race modifier for estimated kidney function and discuss steps to achieve more equitable pulmonary function testing measurement. We detail the use of equity lenses in two predictive clinical modeling tools: Stratification Tool for Opioid Risk Mitigation (STORM) and Care Assessment Need (CAN) predictive models. We conclude with consideration of ways to advance racial health equity in clinical decision support algorithms.