Evidence map›Paper›PMID 38076213›Full record

ArticleHealth equity2023

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 and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Health equity, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Head circumference values among Inuit children in Nunavut, Canada: a retrospective cohort study.CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne · 2024
    Article
  2. Article
  3. Article
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

15 authors.

Justin M ListVA Office of Health Equity, Washington, District of Columbia, USA.ORCID https://orcid.org/0000-0002-6934-6419
Paul PalevskyKidney Medicine Section, Medical Service, VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania, USA.ORCID https://orcid.org/0000-0002-7334-5400
Suzanne TamangDepartment of Veterans Affairs, Palo Alto, California, USA.
Susan CrowleyNephrology Section, Department of Medicine, Yale School of Medicine, New Haven, Connecticut, USA.
David AuHealth Services Research and Development, VA Puget Sound Health Care System, Seattle, Washington, USA.
William C YarbroughDepartment of Internal Medicine, The University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Amol S NavatheCorporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania, USA.
Craig KreislerAnalytics and Performance Integration (API), Office of Quality and Patient Safety, Veterans Health Administration, Washington, District of Columbia, USA.
Ravi B ParikhCorporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania, USA.
Jessica Wang-RodriguezVA National Pathology and Laboratory Medicine Service, Washington, District of Columbia, USA.
J Stacey KluttsNational VHA Diagnostics Office, Washington, District of Columbia, USA.
Paul ConlinVA Boston Healthcare System, Boston, Massachusetts, USA.
Leonard PogachDepartment of Veterans Affairs, New Jersey Health Care System, East Orange, New Jersey, USA.
Esther MeerwijkDepartment of Veterans Affairs, Palo Alto, California, USA.
Ernest MoyVA Office of Health Equity, Washington, District of Columbia, USA.

Funding

Human-Machine Collaborations to Improve Prognosis and Clinical Decision-Making in Advanced CancerK08CA263541 · NCI · UNIVERSITY OF PENNSYLVANIA · PI PARIKH, RAVI BHARAT · 2021 to 2025
$1.2M
6 · The paper itself

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.

Indexed as

clinical equationshealth equitypredictive modelsracial biasVeterans Health Administration

Identifiers

PMID38076213
PMCPMC10698768

What OpenQuestion holds

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LicenceCC BY
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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.