Using structured and unstructured data to identify patients’ need for services that address the social determinants of health
Abstract
Introduction Increasingly, health care providers are adopting population health management approaches that address the social determinants of health (SDH). However, effectively identifying patients needing services that address a SDH in primary care settings is challenging. The purpose of the current study is to explore how various data sources can identify adult primary care patients that are in need of services that address SDH. Methods A cross-sectional study described patients in need of SDH services offered by a safety-net hospital's federally qualified health center clinics. SDH services of social work, behavioral health, nutrition counseling, respiratory therapy, financial planning, medical-legal partnership assistance, patient navigation, and pharmacist consultation were offered on a co-located basis and were identified using structured billing and scheduling data, and unstructured electronic health record data. We report the prevalence of the eight different SDH service needs and the patient characteristics associated with service need. Moreover, characteristics of patients with SDH services need documented in structured data sources were compared with those documented by unstructured data sources. Results More than half (53%) of patients needed SDH services. Those in need of such services tended to be female, older, more medically complex, and higher utilizers of services. Structured and unstructured data sources exhibited poor agreement on patient SDH services need…
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The inclusion of SDoH in clinical notes can vary depending on the healthcare system, individual clinician practices, and the specific context of patient encounters.
“The inclusion of SDoH in clinical notes can vary depending on various factors, including the healthcare system, individual clinician practices, and the specific context of patient encounters47,48.”
From Social determinants of health extraction from clinical notes across institutions using large language models (2025) · cites this paper as [48]
The Limitations section (p. 13) notes differences in documentation practices across providers, and the discussion (p. 12) says unstructured data vary across health IT systems. These support variation by clinician and system, but the paper does not establish that the specific context of a patient encounter affects what SDoH information appears in clinical notes. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThere is a better probability of finding less studied SDoH factors such as social support, adverse childhood, and physical abuse-related information in clinical notes of patients experiencing mental health issues as these factors are highly relevant for mental health cohorts.
“There is a better probability of finding some of the less studied SDoH factors such as social support, adverse childhood, and physical abuse-related information in clinical notes of patients experiencing mental health issues as these factors are highly relevant for mental health cohorts.”
From Social determinants of health extraction from clinical notes across institutions using large language models (2025) · cites this paper as [48]
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Experimental — accuracy not yet measuredThese factors might be less prevalent in clinical notes written by physicians from other specialties, and SDoH documentation depends also on the patient population.
“These factors might be less prevalent in clinical notes written by physicians from other specialties47,48 and hence SDoH documentation depends also on the patient population.”
From Social determinants of health extraction from clinical notes across institutions using large language models (2025) · cites this paper as [48]
The Limitations section (p. 13) says the findings may vary with patient population and documentation practices across providers. It does not compare physicians’ specialties or establish that the factors would be less prevalent in notes from other specialties. (LLM, conf=0.84)
Experimental — accuracy not yet measured
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References 39 · Cited by 82
Research works printed but not linked: 5
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- Authors
- Vest, J. R., Grannis, S. J., Haut, D. P., Halverson, P. K., & Menachemi, N.
- Affiliations
- Regenstrief Institute (US)
- Eskenazi Health (US)
- Corresponding author
- Vest, Joshua R.
- Venue
- International Journal of Medical Informatics
- Publisher
- Elsevier BV
- Published
- 2017-11
- Volume
- 107
- Pages
- 101-106
- ISSN
- 1386-5056 (ISSN-L)
- OpenAlex
- W2755642596
- Cited by
- 82
- References
- 39
- Citations by year
- 2026: 5 · 2025: 13 · 2024: 13 · 2023: 7 · 2022: 9 · 2021: 10 · 2020: 12 · 2019: 7 · 2018: 6
- FWCI
- 9.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Medical Informatics (publishedVersion)
- PubMed (publishedVersion)
- IUScholarWorks (Indiana University) (submittedVersion)
- Funders
- Robert Wood Johnson Foundation
- Topics
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- social determinants of health, primary care, safety-net hospitals, federally qualified health centers, structured and unstructured data, electronic health records, social work services, behavioral health services, patient navigation, medical-legal partnerships, population health management, data source agreement
- MeSH terms
- Adult, Counseling, Cross-Sectional Studies, Female, Humans, Male, Middle Aged, Primary Health Care, Needs Assessment, Electronic Health Records, Social Determinants of Health
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