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

Charlene Esteban Ronquillo; James Mitchell; Dari Alhuwail; Laura-Maria Peltonen; Maxim Topaz; Lorraine J. Block

doi:10.1055/s-0042-1742504

Yearbook of Medical Informatics, Table of Contents

CC BY-NC-ND 4.0 · Yearb Med Inform 2022; 31(01): 094-099
DOI: 10.1055/s-0042-1742504

Special Section: Inclusive Digital Health

Working Group Contributions

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

Authors

Charlene Esteban Ronquillo

¹School of Nursing, University of British Columbia Okanagan, kelowna, Canada
James Mitchell

²School of Computing and Mathematics, Keele University, UK
Dari Alhuwail

³Information Science Department, Kuwait University, Kuwait and Health Informatics Unit, Dasman Diabetes Institute, Kuwait
Laura-Maria Peltonen

⁴Department of Nursing Science, University of Turku, Finland
Maxim Topaz

⁵School of Nursing, Columbia University, New York, USA
Lorraine J. Block

⁶School of Nursing University of British Columbia Vancouver, BC, Canada

Abstract

Summary

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.

Keywords

Artificial intelligence - health equity - social determinants of health - health personnel - informatics

Full Text

References

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