↓ Skip to main content

How Should Clinicians Respond to Race-Based Algorithms as Sources of Iatrogenic Harm?

Overview of attention for article published in The AMA Journal of Ethic, August 2022
Altmetric Badge

Mentioned by

news
2 news outlets
twitter
18 X users
facebook
1 Facebook page

Citations

dimensions_citation
2 Dimensions

Readers on

mendeley
6 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
How Should Clinicians Respond to Race-Based Algorithms as Sources of Iatrogenic Harm?
Published in
The AMA Journal of Ethic, August 2022
DOI 10.1001/amajethics.2022.720
Pubmed ID
Authors

Madeleine Maddy Kane, Rachel Bervell, Angela Y Zhang, Jennifer Tsai

Abstract

Some clinical algorithms use race as an epidemiological shorthand to "correct" for health determinants that are clinically influential but also variable because they are historical, social, cultural, or economic in origin. Such "correction factors" are both clinically and ethically relevant when their use reinforces racial essentialism and exacerbates racial health inequity. This commentary on a case in which the original vaginal birth after cesarean calculator is used argues that this and similar race-based algorithms should be considered sources of iatrogenic harm by undermining decision sharing in patient-clinician relationships and Black birthing peoples' rights to self-determination.

X Demographics

X Demographics

The data shown below were collected from the profiles of 18 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 2 33%
Professor > Associate Professor 1 17%
Researcher 1 17%
Unknown 2 33%
Readers by discipline Count As %
Social Sciences 2 33%
Psychology 1 17%
Computer Science 1 17%
Unknown 2 33%