Using measures of race to make clinical predictions: Decision making, patient health, and fairness

Charles Manski, John Mullahy and Atheendar S. Venkataramani
Additional contact information
John Mullahy: c Department of Population Health Sciences, University of Wisconsin–Madison , Madison , WI 53726
Atheendar S. Venkataramani: d Department of Medical Ethics & Health Policy, University of Pennsylvania , Philadelphia , PA 19104

Proceedings of the National Academy of Sciences, 2023, vol. 120, issue 35, e2303370120

Abstract:

The use of race measures in clinical prediction models is contentious. We seek to inform the discourse by evaluating the inclusion of race in probabilistic predictions of illness that support clinical decision making. Adopting a static utilitarian framework to formalize social welfare, we show that patients of all races benefit when clinical decisions are jointly guided by patient race and other observable covariates. Similar conclusions emerge when the model is extended to a two-period setting where prevention activities target systemic drivers of disease. We also discuss non-utilitarian concepts that have been proposed to guide allocation of health care resources.

Keywords: clinical prediction; patient care; utilitarian welfare analysis; race (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://doi.org/10.1073/pnas.2303370120 (application/pdf)

Related works:
Working Paper: Using Measures of Race to Make Clinical Predictions: Decision Making, Patient Health, and Fairness (2022)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nas:journl:v:120:y:2023:p:e2303370120

Access Statistics for this article

More articles in Proceedings of the National Academy of Sciences from Proceedings of the National Academy of Sciences
Bibliographic data for series maintained by PNAS Product Team ().