ArticleNPJ digital medicine2024
Large language models to identify social determinants of health in electronic health records.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 174 papers, 2 of them syntheses that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
174 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Applications of Natural Language Processing and Large Language Models for Social Determinants of Health: Systematic Review.Journal of medical Internet research · 2026Pooled it
- The TRIPOD-LLM reporting guideline for studies using large language models: a Korean translation.Ewha medical journal · 2025Guideline
- Development of a Comprehensive Decision Support Tool for Chemotherapy-Cycle Prescribing: Initial Usability Study.JMIR formative research · 2025Trial
- Outcomes Among Patients with Systolic Heart Failure and Limited English Proficiency.Journal of immigrant and minority health · 2026Article
- Large language model-based triage to identify antiretroviral therapy adherence barriers and risk levels in patient messages.JAMIA open · 2026Article
- A Conversational Agent for Providing Personalized Preexposure Prophylaxis (PrEP) Support: Protocol for Chatbot Implementation and Evaluation.JMIR research protocols · 2026Article
- Prompt Engineering of Large Language Models for Medication Dose Calculation.Clinical pharmacology and therapeutics · 2026Article
- Harmonizing Multi-Institutional Clinical Documentation Using Natural Language Processing in Neurofibromatosis Type 1.Neurology. Clinical practice · 2026Observational
- Quality of Life of People Living With Dementia Residing in Nursing Homes: Secondary Analysis of Observational Data.Journal of medical Internet research · 2026Article
- Hierarchical multi-label structuring of Japanese SOAP clinical notes with large language models.Scientific reports · 2026Article
- Leveraging Data Science for Conducting Observational Studies: Highlighting Advantages and Limitations Throughout the Evaluation of a Use Case.Mayo Clinic proceedings. Digital health · 2026Article
- Posttraumatic Stress Disorder, Health-Related Social Needs, and Cognitive Outcomes in US Veterans.JAMA network open · 2026Article
- Anticipating health and care trajectories from routinely collected social care records.npj health systems · 2026Article
- AI-based multimodal integration of genomics and electronic health records.Nature reviews. Genetics · 2026Review
- Scalable extraction of social determinants of health from clinical notes in a sepsis cohort using instruction-tuned language models.JAMIA open · 2026Article
- Article
- From "No Mortality Difference" to "No Communication Debt": Operationalizing Language Access as a Heart Failure Quality Metric.Journal of immigrant and minority health · 2026Article
- Bias and Fairness Across the Healthcare AI Lifecycle: A Clinician-Oriented Review.Balkan medical journal · 2026Review
- Article
- Dynamic Closed-Loop Medical Record Quality Management Using an AI-Driven Multilevel Quality Control System: Development and Implementation Study.JMIR medical informatics · 2026Article
114 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
Abstract
Social determinants of health (SDoH) play a critical role in patient outcomes, yet their documentation is often missing or incomplete in the structured data of electronic health records (EHRs). Large language models (LLMs) could enable high-throughput extraction of SDoH from the EHR to support research and clinical care. However, class imbalance and data limitations present challenges for this sparsely documented yet critical information. Here, we investigated the optimal methods for using LLMs to extract six SDoH categories from narrative text in the EHR: employment, housing, transportation, parental status, relationship, and social support. The best-performing models were fine-tuned Flan-T5 XL for any SDoH mentions (macro-F1 0.71), and Flan-T5 XXL for adverse SDoH mentions (macro-F1 0.70). Adding LLM-generated synthetic data to training varied across models and architecture, but improved the performance of smaller Flan-T5 models (delta F1 + 0.12 to +0.23). Our best-fine-tuned models outperformed zero- and few-shot performance of ChatGPT-family models in the zero- and few-shot setting, except GPT4 with 10-shot prompting for adverse SDoH. Fine-tuned models were less likely than ChatGPT to change their prediction when race/ethnicity and gender descriptors were added to the text, suggesting less algorithmic bias (p < 0.05). Our models identified 93.8% of patients with adverse SDoH, while ICD-10 codes captured 2.0%. These results demonstrate the potential of LLMs in improving real-world evidence on SDoH and assisting in identifying patients who could benefit from resource support.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.