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Cited by 9Machine Learning in HealthcareEmergency and Acute Care StudiesArtificial Intelligence in Healthcare and Education

Leveraging artificial intelligence and data science for integration of social determinants of health in emergency medicine: scoping review

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Abbott, Ethan E, Apakama, Donald, Richardson, Lynne D, Chan, Lili, Nadkarni, Girish N·2024·JMIR Medical Informatics
DOI 10.2196/57124PMC PMC11539921
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Abstract

Background: Social determinants of health (SDOH) are critical drivers of health disparities and patient outcomes. However, accessing and collecting patient-level SDOH data can be operationally challenging in the emergency department (ED) clinical setting, requiring innovative approaches. Objective: This scoping review examines the potential of AI and data science for modeling, extraction, and incorporation of SDOH data specifically within EDs, further identifying areas for advancement and investigation. Methods: We conducted a standardized search for studies published between 2015 and 2022, across Medline (Ovid), Embase (Ovid), CINAHL, Web of Science, and ERIC databases. We focused on identifying studies using AI or data science related to SDOH within emergency care contexts or conditions. Two specialized reviewers in emergency medicine (EM) and clinical informatics independently assessed each article, resolving discrepancies through iterative reviews and discussion. We then extracted data covering study details, methodologies, patient demographics, care settings, and principal outcomes. Results: Of the 1047 studies screened, 26 met the inclusion criteria. Notably, 9 out of 26 (35%) studies were solely concentrated on ED patients. Conditions studied spanned broad EM complaints and included sepsis, acute myocardial infarction, and asthma. The majority of studies (n=16) explored multiple SDOH domains, with homelessness/housing insecurity and neighborhood/built environment predominating. Machine learning (ML) techniques were used in 23 of 26 studies, with natural language processing (NLP) being the most commonly used approach (n=11). Rule-based NLP (n=5), deep learning (n=2), and pattern matching (n=4) were the most commonly used NLP techniques. NLP models in the reviewed studies displayed significant predictive performance with outcomes, with F1-scores ranging between 0.40 and 0.75 and specificities nearing 95.9%. Conclusions: Although in its infancy, the convergence of AI and data science techniques, especially ML and NLP, with SDOH in EM offers transformative possibilities for better usage and integration of social data into clinical care and research. With a significant focus on the ED and notable NLP model performance, there is an imperative to standardize SDOH data collection, refine algorithms for diverse patient groups, and champion interdisciplinary synergies. These efforts aim to harness SDOH data optimally, enhancing patient care and mitigating health disparities. Our research underscores the vital need for continued investigation in this domain.

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  • Social determinants of health influence outcomes among patients with diabetes, particularly in vulnerable populations.

    “At the same time, there is growing recognition that social determinants of health (SDoH)—including socioeconomic status, food access, housing stability, and transportation—influence outcomes among patients with diabetes, particularly in vulnerable populations [31–34].”

    From Machine learning-driven risk prediction for post-hospitalization diabetes case management: Integrating clinical and social determinants of health (2026) · cites this paper as [34]

    The Introduction (page not provided) says SDoH can affect every patient’s health trajectory and clinical outcomes. The Results, under “SDOH Domains” (page not provided), include socioeconomic status, food insecurity, and housing insecurity. The paper addresses these issues in emergency medicine, but the passages do not establish their effects on diabetes outcomes specifically or address transportation as a determinant. (LLM, conf=0.86)

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Authors
Abbott, E. E., Apakama, D., Richardson, L. D., Chan, L., & Nadkarni, G. N.
Affiliations
Icahn School of Medicine at Mount Sinai (US)
Corresponding author
Abbott, Ethan E
ORCID
Venue
JMIR Medical Informatics
Publisher
JMIR Publications Inc.
Published
2024-10-30
Volume
12
Pages
e57124-e57124
ISSN
2291-9694 (ISSN-L)
DOI
10.2196/57124
PMC
PMC11539921
OpenAlex
W4403921271
Cited by
9
References
19
Citations by year
2026: 4 · 2025: 5
FWCI
1.33
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JMIR Medical Informatics (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
  • National Institutes of Health (1k08hl169980-01a1)
  • National Heart, Lung, and Blood Institute (K08 HL169980)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
  • Health Sciences › Medicine › Emergency Medicine › Emergency and Acute Care Studies
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
social determinants of health, emergency department, artificial intelligence, natural language processing, machine learning, homelessness, housing insecurity, built environment, health disparities, clinical decision support, sepsis, acute myocardial infarction, asthma, clinical informatics, interdisciplinary collaboration
MeSH terms
Data Science, Artificial Intelligence, Emergency Medicine, Emergency Service, Hospital, Humans, Social Determinants of Health

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