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The Untapped Potential of Nursing and Allied Health Data for Improved Representation of Social Determinants of Health and Intersectionality in Artificial Intelligence Applications: A Rapid Review: IMIA Student and Emerging Professionals Group

Objectives : The objective of this paper is to draw attention to the currently underused potential of clinical documentation by nursing and allied health professions to improve the representation of social determinants of health (SDoH) and intersectionality data in electronic health records (EHRs),...

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Autores principales: Ronquillo, Charlene Esteban, Mitchell, James, Alhuwail, Dari, Peltonen, Laura-Maria, Topaz, Maxim, Block, Lorraine J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Georg Thieme Verlag KG 2022
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9719779/
https://www.ncbi.nlm.nih.gov/pubmed/35654435
http://dx.doi.org/10.1055/s-0042-1742504
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author Ronquillo, Charlene Esteban
Mitchell, James
Alhuwail, Dari
Peltonen, Laura-Maria
Topaz, Maxim
Block, Lorraine J.
author_facet Ronquillo, Charlene Esteban
Mitchell, James
Alhuwail, Dari
Peltonen, Laura-Maria
Topaz, Maxim
Block, Lorraine J.
author_sort Ronquillo, Charlene Esteban
collection PubMed
description Objectives : The objective of this paper is to draw attention to the currently underused potential of clinical documentation by nursing and allied health professions to improve the representation of social determinants of health (SDoH) and intersectionality data in electronic health records (EHRs), towards the development of equitable artificial intelligence (AI) technologies. Methods : A rapid review of the literature on the inclusion of nursing and allied health data and the nature of health equity information representation in the development and/or use of artificial intelligence approaches alongside expert perspectives from the International Medical Informatics Association (IMIA) Student and Emerging Professionals Working Group. Results : Consideration of social determinants of health and intersectionality data are limited in both the medical AI and nursing and allied health AI literature. As a concept being newly discussed in the context of AI, the lack of discussion of intersectionality in the literature was unsurprising. However, the limited consideration of social determinants of health was surprising, given its relatively longstanding recognition and the importance of representation of the features of diverse populations as a key requirement for equitable AI. Conclusions : Leveraging the rich contextual data collected by nursing and allied health professions has the potential to improve the capture and representation of social determinants of health and intersectionality. This will require addressing issues related to valuing AI goals (e.g., diagnostics versus supporting care delivery) and improved EHR infrastructure to facilitate documentation of data beyond medicine. Leveraging nursing and allied health data to support equitable AI development represents a current open question for further exploration and research.
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spelling pubmed-97197792022-12-05 The Untapped Potential of Nursing and Allied Health Data for Improved Representation of Social Determinants of Health and Intersectionality in Artificial Intelligence Applications: A Rapid Review: IMIA Student and Emerging Professionals Group Ronquillo, Charlene Esteban Mitchell, James Alhuwail, Dari Peltonen, Laura-Maria Topaz, Maxim Block, Lorraine J. Yearb Med Inform Objectives : The objective of this paper is to draw attention to the currently underused potential of clinical documentation by nursing and allied health professions to improve the representation of social determinants of health (SDoH) and intersectionality data in electronic health records (EHRs), towards the development of equitable artificial intelligence (AI) technologies. Methods : A rapid review of the literature on the inclusion of nursing and allied health data and the nature of health equity information representation in the development and/or use of artificial intelligence approaches alongside expert perspectives from the International Medical Informatics Association (IMIA) Student and Emerging Professionals Working Group. Results : Consideration of social determinants of health and intersectionality data are limited in both the medical AI and nursing and allied health AI literature. As a concept being newly discussed in the context of AI, the lack of discussion of intersectionality in the literature was unsurprising. However, the limited consideration of social determinants of health was surprising, given its relatively longstanding recognition and the importance of representation of the features of diverse populations as a key requirement for equitable AI. Conclusions : Leveraging the rich contextual data collected by nursing and allied health professions has the potential to improve the capture and representation of social determinants of health and intersectionality. This will require addressing issues related to valuing AI goals (e.g., diagnostics versus supporting care delivery) and improved EHR infrastructure to facilitate documentation of data beyond medicine. Leveraging nursing and allied health data to support equitable AI development represents a current open question for further exploration and research. Georg Thieme Verlag KG 2022-06-02 /pmc/articles/PMC9719779/ /pubmed/35654435 http://dx.doi.org/10.1055/s-0042-1742504 Text en IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. ( https://creativecommons.org/licenses/by-nc-nd/4.0/ ) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License, which permits unrestricted reproduction and distribution, for non-commercial purposes only; and use and reproduction, but not distribution, of adapted material for non-commercial purposes only, provided the original work is properly cited.
spellingShingle Ronquillo, Charlene Esteban
Mitchell, James
Alhuwail, Dari
Peltonen, Laura-Maria
Topaz, Maxim
Block, Lorraine J.
The Untapped Potential of Nursing and Allied Health Data for Improved Representation of Social Determinants of Health and Intersectionality in Artificial Intelligence Applications: A Rapid Review: IMIA Student and Emerging Professionals Group
title The Untapped Potential of Nursing and Allied Health Data for Improved Representation of Social Determinants of Health and Intersectionality in Artificial Intelligence Applications: A Rapid Review: IMIA Student and Emerging Professionals Group
title_full The Untapped Potential of Nursing and Allied Health Data for Improved Representation of Social Determinants of Health and Intersectionality in Artificial Intelligence Applications: A Rapid Review: IMIA Student and Emerging Professionals Group
title_fullStr The Untapped Potential of Nursing and Allied Health Data for Improved Representation of Social Determinants of Health and Intersectionality in Artificial Intelligence Applications: A Rapid Review: IMIA Student and Emerging Professionals Group
title_full_unstemmed The Untapped Potential of Nursing and Allied Health Data for Improved Representation of Social Determinants of Health and Intersectionality in Artificial Intelligence Applications: A Rapid Review: IMIA Student and Emerging Professionals Group
title_short The Untapped Potential of Nursing and Allied Health Data for Improved Representation of Social Determinants of Health and Intersectionality in Artificial Intelligence Applications: A Rapid Review: IMIA Student and Emerging Professionals Group
title_sort untapped potential of nursing and allied health data for improved representation of social determinants of health and intersectionality in artificial intelligence applications: a rapid review: imia student and emerging professionals group
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9719779/
https://www.ncbi.nlm.nih.gov/pubmed/35654435
http://dx.doi.org/10.1055/s-0042-1742504
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