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Cited by 17Artificial Intelligence in Healthcare and EducationNursing Diagnosis and DocumentationEthics and Social Impacts of AI

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

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Ronquillo, Charlene Esteban, Mitchell, James, Alhuwail, Dari, Peltonen, Laura-Maria, Topaz, Maxim, Block, Lorraine J.·2022·Yearbook of Medical Informatics
DOI 10.1055/s-0042-1742504PMC PMC9719779
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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. 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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  • Label bias occurs when labels assigned to data points during training or validation reflect inherent inaccuracies, inconsistencies, or prejudices.

    “Label and Social Bias were recurrent as well (n = 4), for instance, label bias occurs when the labels assigned to data points during training or validation reflect inherent inaccuracies, inconsistencies, or prejudices [9,39].”

    From Frameworks encompassing intersectional perspective of artificial intelligence in healthcare. Scoping review (2026) · cites this paper as [9]

    The Introduction discusses incomplete EHR data, underrepresentation, and AI bias, but does not define label bias. Searches for labels assigned during training or validation and for inaccurate or prejudiced ground-truth labels found no passage addressing that definition; page numbers were unavailable in our copy. (LLM, conf=0.73)

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Authors
Ronquillo, C. E., Mitchell, J., Alhuwail, D., Peltonen, L.-M., Topaz, M., & Block, L. J.
Affiliations
  • University of British Columbia, Okanagan Campus (CA)
  • Keele University (GB)
  • Kuwait University (KW)
  • University of Turku (FI)
  • Columbia University (US)
  • University of British Columbia (CA)
Corresponding author
Ronquillo, Charlene Esteban
ORCID
Venue
Yearbook of Medical Informatics
Publisher
Georg Thieme Verlag KG
Published
2022-06-02
Volume
31
Issue
01
Pages
094-099
ISSN
  • 0943-4747 (ISSN-L)
  • 2364-0502
DOI
10.1055/s-0042-1742504
PMC
PMC9719779
OpenAlex
W4281631623
Cited by
17
References
4
Citations by year
2026: 4 · 2025: 11 · 2024: 1 · 2023: 1
FWCI
0.22
Type
journal article
Language
en
Open access
Licence
Locations
  • Yearbook of Medical Informatics (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Nursing › Issues, ethics and legal aspects › Nursing Diagnosis and Documentation
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
Keywords
social determinants of health, intersectionality, nursing documentation, electronic health records, artificial intelligence in healthcare, health equity, algorithmic fairness, clinical documentation
MeSH terms
Intersectional Framework, Artificial Intelligence, Humans, Medical Informatics, Electronic Health Records, Social Determinants of Health

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