• Shazia Mehmood Siddique, Kelley Tipton, Brian Leas, Christopher Jepson, Jaya Aysola, Jordana B Cohen, Emilia Flores, Michael O Harhay, Harald Schmidt, Gary E Weissman, Julie Fricke, Jonathan R Treadwell, and Nikhil K Mull.
• Division of Gastroenterology, University of Pennsylvania; Leonard Davis Institute of Health Economics, University of Pennsylvania; and Center for Evidence-Based Practice, Penn Medicine, Philadelphia, Pennsylvania (S.M.S.).
• Ann. Intern. Med. 2024 Apr 1; 177 (4): 484496484-496.

BackgroundThere is increasing concern for the potential impact of health care algorithms on racial and ethnic disparities.PurposeTo examine the evidence on how health care algorithms and associated mitigation strategies affect racial and ethnic disparities.Data SourcesSeveral databases were searched for relevant studies published from 1 January 2011 to 30 September 2023.Study SelectionUsing predefined criteria and dual review, studies were screened and selected to determine: 1) the effect of algorithms on racial and ethnic disparities in health and health care outcomes and 2) the effect of strategies or approaches to mitigate racial and ethnic bias in the development, validation, dissemination, and implementation of algorithms.Data ExtractionOutcomes of interest (that is, access to health care, quality of care, and health outcomes) were extracted with risk-of-bias assessment using the ROBINS-I (Risk Of Bias In Non-randomised Studies - of Interventions) tool and adapted CARE-CPM (Critical Appraisal for Racial and Ethnic Equity in Clinical Prediction Models) equity extension.Data SynthesisSixty-three studies (51 modeling, 4 retrospective, 2 prospective, 5 prepost studies, and 1 randomized controlled trial) were included. Heterogenous evidence on algorithms was found to: a) reduce disparities (for example, the revised kidney allocation system), b) perpetuate or exacerbate disparities (for example, severity-of-illness scores applied to critical care resource allocation), and/or c) have no statistically significant effect on select outcomes (for example, the HEART Pathway [history, electrocardiogram, age, risk factors, and troponin]). To mitigate disparities, 7 strategies were identified: removing an input variable, replacing a variable, adding race, adding a non-race-based variable, changing the racial and ethnic composition of the population used in model development, creating separate thresholds for subpopulations, and modifying algorithmic analytic techniques.LimitationResults are mostly based on modeling studies and may be highly context-specific.ConclusionAlgorithms can mitigate, perpetuate, and exacerbate racial and ethnic disparities, regardless of the explicit use of race and ethnicity, but evidence is heterogeneous. Intentionality and implementation of the algorithm can impact the effect on disparities, and there may be tradeoffs in outcomes.Primary Funding SourceAgency for Healthcare Quality and Research.

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