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Eliminating Algorithmic Racial Bias in Clinical Decision Support Algorithms: Use Cases from the Veterans Health Administration
Journal article   Open access   Peer reviewed

Eliminating Algorithmic Racial Bias in Clinical Decision Support Algorithms: Use Cases from the Veterans Health Administration

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, …
Health equity, Vol.7(1), pp.809-816
2023
DOI: 10.1089/heq.2023.0037
PMCID: PMC10698768
PMID: 38076213
url
https://doi.org/10.1089/heq.2023.0037View
Published (Version of record) Open Access

Abstract

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.
predictive models health equity Veterans Health Administration clinical equations racial bias

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