A2 Vertaisarvioitu katsausartikkeli tieteellisessä lehdessä
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
Tekijät: Ronquillo Charlene Esteban, Mitchell James, Alhuwail Dari, Peltonen Laura-Maria, Topaz Maxim, Block Lorraine J
Kustantaja: Schattauer
Julkaisuvuosi: 2022
Journal: IMIA Yearbook of Medical Informatics
Tietokannassa oleva lehden nimi: Yearbook of medical informatics
Lehden akronyymi: Yearb Med Inform
Vuosikerta: 31
Numero: 1
Aloitussivu: 94
Lopetussivu: 99
ISSN: 0943-4747
eISSN: 2364-0502
DOI: https://doi.org/10.1055/s-0042-1742504
Rinnakkaistallenteen osoite: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9719779/
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.