Adaptation of an NLP system to a new healthcare environment to identify social determinants of health.
Open accessAbstract
Social determinants of health (SDoH) are increasingly important factors for population health, healthcare outcomes, and care delivery. However, many of these factors are not reliably captured within structured electronic health record (EHR) data. In this work, we evaluated and adapted a previously published NLP tool to include additional social risk factors for deployment at Vanderbilt University Medical Center in an Acute Myocardial Infarction cohort. We developed a transformation of the SDoH outputs of the tool into the OMOP common data model (CDM) for re-use across many potential use cases, yielding performance measures across 8 SDoH classes of precision 0.83 recall 0.74 and F-measure of 0.78.
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In clinical practice, there has been a lack of systematic collection and analysis of SDoH defined in the EHRs.
“In clinical practice, there has been a lack of systematic collection and analysis of SDoH defined in the EHRs15–17.”
From Social determinants of health extraction from clinical notes across institutions using large language models (2025) · cites this paper as [17]
More recent natural language processing (NLP)-based methods, particularly LLMs like GPT-4, have shown success in capturing SDoH information from unstructured clinical notes and patient narratives, yielding tangible benefits for patient outcomes.
“More recent natural language processing (NLP)-based methods, particularly LLMs like GPT-4, have also shown success in capturing SDoH information from unstructured clinical notes and patient narratives, yielding tangible benefits for patient outcomes [78,79].”
From Bias in medical AI: Implications for clinical decision-making (2024) · cites this paper as [78]
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Bias in medical AI: Implications for clinical decision-making
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“In clinical practice, there has been a lack of systematic collection and analysis of SDoH de fi ned in the EHRs 15 – 17 .”
- Authors
- Reeves, R. M., Christensen, L., Brown, J. R., Conway, M., Levis, M., Gobbel, G. T., Shah, R. U., Goodrich, C., Ricket, I., Minter, F., Bohm, A., Bray, B. E., Matheny, M. E., & Chapman, W.
- Affiliations
- VA Tennessee Valley Healthcare System (US)
- University of Utah (US)
- Dartmouth College (US)
- Vanderbilt University Medical Center (US)
- The University of Melbourne (AU)
- Corresponding author
- Reeves, Ruth M.
- ORCID
- Venue
- Journal of Biomedical Informatics
- Publisher
- Elsevier BV
- Published
- 2021-08
- Volume
- 120
- Pages
- 103851
- ISSN
- 1532-0464 (ISSN-L)
- PMC
- PMC8386129
- OpenAlex
- W3177105417
- Cited by
- 45
- References
- 30
- Citations by year
- 2026: 4 · 2025: 9 · 2024: 13 · 2023: 8 · 2022: 10 · 2021: 1
- FWCI
- 2.51
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Biomedical Informatics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Heart, Lung, and Blood Institute (K08HL136850, R01HL130828)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Keywords
- social determinants of health, natural language processing, social risk factors, electronic health records, acute myocardial infarction, OMOP Common Data Model, clinical text mining, population health
- MeSH terms
- Academic Medical Centers, Delivery of Health Care, Humans, Cohort Studies, Electronic Health Records, Social Determinants of Health
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