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.

Journal: Yearbook of medical informatics
PMID: 35654435

Abstract

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.

Authors

  • Charlene Esteban Ronquillo
    Daphne Cockwell School of Nursing, Faculty of Community Services, Ryerson University, Toronto, ON, Canada.
  • James Mitchell
    School of Computing and Mathematics, Keele University, United Kingdom. Electronic address: j.a.mitchell@keele.ac.uk.
  • Dari Alhuwail
    Information Science Department, College of Life Sciences, Kuwait University, Kuwait, Kuwait.
  • Laura-Maria Peltonen
    Nursing Science, University of Turku, and Turku University Hospital, Turku, Finland.
  • Maxim Topaz
    Division of General Internal Medicine and Primary Care, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA.
  • Lorraine J Block
    School of Nursing, University of British Columbia, Vancouver, British Columbia, Canada.

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