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Use of race in clinical algorithms

To answer whether patients’ race belongs in clinical prediction algorithms, two types of prediction models are considered: (i) diagnostic, which describes a patient’s clinical characteristics, and (ii) prognostic, which forecasts a clinical risk or treatment effect that a patient is likely to experi...

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Detalles Bibliográficos
Autor principal: Basu, Anirban
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10219586/
https://www.ncbi.nlm.nih.gov/pubmed/37235647
http://dx.doi.org/10.1126/sciadv.add2704
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author Basu, Anirban
author_facet Basu, Anirban
author_sort Basu, Anirban
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description To answer whether patients’ race belongs in clinical prediction algorithms, two types of prediction models are considered: (i) diagnostic, which describes a patient’s clinical characteristics, and (ii) prognostic, which forecasts a clinical risk or treatment effect that a patient is likely to experience in the future. The ex ante equality of opportunity framework is used, where specific health outcomes, which are prediction targets, evolve dynamically due to the effects of legacy levels of outcomes, circumstances, and current individual efforts. In practical settings, this study shows that failure to include race corrections will propagate systemic inequities and discrimination in any diagnostic model and specific prognostic models that inform decisions by invoking an ex ante compensation principle. In contrast, including race in prognostic models that inform resource allocations following an ex ante reward principle can compromise the equality of opportunities for patients from different races. Simulation results demonstrate these arguments.
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spelling pubmed-102195862023-05-27 Use of race in clinical algorithms Basu, Anirban Sci Adv Social and Interdisciplinary Sciences To answer whether patients’ race belongs in clinical prediction algorithms, two types of prediction models are considered: (i) diagnostic, which describes a patient’s clinical characteristics, and (ii) prognostic, which forecasts a clinical risk or treatment effect that a patient is likely to experience in the future. The ex ante equality of opportunity framework is used, where specific health outcomes, which are prediction targets, evolve dynamically due to the effects of legacy levels of outcomes, circumstances, and current individual efforts. In practical settings, this study shows that failure to include race corrections will propagate systemic inequities and discrimination in any diagnostic model and specific prognostic models that inform decisions by invoking an ex ante compensation principle. In contrast, including race in prognostic models that inform resource allocations following an ex ante reward principle can compromise the equality of opportunities for patients from different races. Simulation results demonstrate these arguments. American Association for the Advancement of Science 2023-05-26 /pmc/articles/PMC10219586/ /pubmed/37235647 http://dx.doi.org/10.1126/sciadv.add2704 Text en Copyright © 2023 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license (https://creativecommons.org/licenses/by-nc/4.0/) , which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.
spellingShingle Social and Interdisciplinary Sciences
Basu, Anirban
Use of race in clinical algorithms
title Use of race in clinical algorithms
title_full Use of race in clinical algorithms
title_fullStr Use of race in clinical algorithms
title_full_unstemmed Use of race in clinical algorithms
title_short Use of race in clinical algorithms
title_sort use of race in clinical algorithms
topic Social and Interdisciplinary Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10219586/
https://www.ncbi.nlm.nih.gov/pubmed/37235647
http://dx.doi.org/10.1126/sciadv.add2704
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