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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),...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Georg Thieme Verlag KG
2022
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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. |
format | Online Article Text |
id | pubmed-9719779 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Georg Thieme Verlag KG |
record_format | MEDLINE/PubMed |
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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