Evidence map›Paper›PMID 37782868›Full record

ArticleHealth affairs (Project Hope)2023

Mitigating Racial And Ethnic Bias And Advancing Health Equity In Clinical Algorithms: A Scoping Review.

Michael P Cary, Anna Zink, Sijia Wei, Andrew Olson, Mengying Yan, Rashaud Senior, Sophia Bessias, Kais Gadhoumi, Genevieve Jean-Pierre, Demy Wang and 4 more

Abstract readScoping Review
In one paragraph

Article in Health affairs (Project Hope), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

38 citing papers in PubMed.

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  17. Observational
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Michael P CaryMichael P. Cary Jr. (michael.cary@duke.edu), Duke University, Durham, North Carolina.
Anna ZinkAnna Zink, University of Chicago, Chicago, Illinois.
Sijia WeiSijia Wei, Northwestern University, Chicago, Illinois.
Andrew OlsonAndrew Olson, Duke University.
Mengying YanMengying Yan, Duke University.
Rashaud SeniorRashaud Senior, Duke University.
Sophia BessiasSophia Bessias, Duke University.
Kais GadhoumiKais Gadhoumi, Duke University.
Genevieve Jean-PierreGenevieve Jean-Pierre, Duke University.
Demy WangDemy Wang, Duke University.
Leila S LedbetterLeila S. Ledbetter, Duke University.
Nicoleta J Economou-ZavlanosNicoleta J. Economou-Zavlanos, Duke University.
Ziad ObermeyerZiad Obermeyer, University of California Berkeley, Berkeley, California.
Michael J PencinaMichael J. Pencina, Duke University.

Funding

Support for QA/QC for Prior Approval ProcessUL1TR002553 · NCATS · DUKE UNIVERSITY · PI LI, JENNIFER S, MCNAMARA, JAMES O. · 2018 to 2023
$58.5M
NCATS NIH HHS UL1 TR002553
6 · The paper itself

Abstract

In August 2022 the Department of Health and Human Services (HHS) issued a notice of proposed rulemaking prohibiting covered entities, which include health care providers and health plans, from discriminating against individuals when using clinical algorithms in decision making. However, HHS did not provide specific guidelines on how covered entities should prevent discrimination. We conducted a scoping review of literature published during the period 2011-22 to identify health care applications, frameworks, reviews and perspectives, and assessment tools that identify and mitigate bias in clinical algorithms, with a specific focus on racial and ethnic bias. Our scoping review encompassed 109 articles comprising 45 empirical health care applications that included tools tested in health care settings, 16 frameworks, and 48 reviews and perspectives. We identified a wide range of technical, operational, and systemwide bias mitigation strategies for clinical algorithms, but there was no consensus in the literature on a single best practice that covered entities could employ to meet the HHS requirements. Future research should identify optimal bias mitigation methods for various scenarios, depending on factors such as patient population, clinical setting, algorithm design, and types of bias to be addressed.

Indexed as

Health EquityAlgorithmsDelivery of Health CareHealth PersonnelHumansRacial Groups

Identifiers

PMID37782868
PMCPMC10668606

What OpenQuestion holds

Textmetadata
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Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.