The reduction of race and gender bias in clinical treatment recommendations using clinician peer networks in an experimental setting

Damon Centola (), Douglas Guilbeault, Urmimala Sarkar, Elaine Khoong and Jingwen Zhang
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Damon Centola: University of Pennsylvania
Douglas Guilbeault: University of Pennsylvania
Urmimala Sarkar: University of Pennsylvania
Elaine Khoong: University of Pennsylvania
Jingwen Zhang: University of Pennsylvania

Nature Communications, 2021, vol. 12, issue 1, 1-10

Abstract: Abstract Bias in clinical practice, in particular in relation to race and gender, is a persistent cause of healthcare disparities. We investigated the potential of a peer-network approach to reduce bias in medical treatment decisions within an experimental setting. We created “egalitarian” information exchange networks among practicing clinicians who provided recommendations for the clinical management of patient scenarios, presented via standardized patient videos of actors portraying patients with cardiac chest pain. The videos, which were standardized for relevant clinical factors, presented either a white male actor or Black female actor of similar age, wearing the same attire and in the same clinical setting, portraying a patient with clinically significant chest pain symptoms. We found significant disparities in the treatment recommendations given to the white male patient-actor and Black female patient-actor, which when translated into real clinical scenarios would result in the Black female patient being significantly more likely to receive unsafe undertreatment, rather than the guideline-recommended treatment. In the experimental control group, clinicians who were asked to independently reflect on the standardized patient videos did not show any significant reduction in bias. However, clinicians who exchanged real-time information in structured peer networks significantly improved their clinical accuracy and showed no bias in their final recommendations. The findings indicate that clinician network interventions might be used in healthcare settings to reduce significant disparities in patient treatment.

Date: 2021
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DOI: 10.1038/s41467-021-26905-5

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