ArticleNPJ digital medicine2025
Social determinants of health extraction from clinical notes across institutions using large language models.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 1 synthesis or guideline 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
- A unified framework and benchmark for generalizable biomedical knowledge extraction and applications with large language models.Cell reports. Medicine · 2026Article
- AI-Assisted Clinical Data Abstraction From Electronic Health Records: Retrospective Concordance Study.JMIR formative research · 2026Article
- Extracting Social Determinants of Health From Electronic Health Records: Development and Comparison of Rule-Based and Large Language Model Methods.JMIR medical informatics · 2026Article
- Review
- Impact of social determinants on opioid use disorder treatment engagement after release from jail: a case note content analysis.Addiction science & clinical practice · 2026Article
- Social Determinants of Health and 1-Year Buprenorphine Initiation Among Justice-Involved Veterans.JAMA network open · 2026Article
- A Dataset and Resources for Identifying Patient Health Literacy Information from Clinical Notes.ArXiv · 2026Article
- SynthEHR-eviction: enhancing eviction SDoH detection with LLM-augmented synthetic EHR data.NPJ digital medicine · 2026Article
- Automated Tumor and Node Staging from Esophageal Cancer Endoscopic Ultrasound Reports: A Benchmark of Advanced Reasoning Models with Prompt Engineering and Cross-Lingual Evaluation.Diagnostics (Basel, Switzerland) · 2026Article
- Comparing LLM and Fine-Tuned Model Performance on NVDRS Circumstance Extraction with Varying Prompt Complexity.Proceedings. IEEE International Conference on Healthcare Informatics · 2026Article
- Application of artificial intelligence in hepatology.Frontiers in digital health · 2026Review
- Social Work-Supported Strategies to Reduce Postoperative Readmissions in Orthopedic Surgery: A Narrative Review.Cureus · 2026Review
- Improving social determinants of health documentation in French electronic health records using large language models.Scientific reports · 2025Article
- Extracting social determinants of health from electronic health records: development and comparison of rule-based and large language models-based methods.medRxiv : the preprint server for health sciences · 2025Article
- Unveiling social determinants of health impact on adverse pregnancy outcomes through natural language processing.Scientific reports · 2025Article
- The Development Landscape of Large Language Models for Biomedical Applications.Annual review of biomedical data science · 2025Review
- Article
Corrections and comments
- Update of
Authors and funding
16 authors.
Funding
Abstract
Detailed social determinants of health (SDoH) is often buried within clinical text in EHRs. Most current NLP efforts for SDoH have limitations, investigating limited factors, deriving data from a single institution, using specific patient cohorts/note types, with reduced focus on generalizability. We aim to address these issues by creating cross-institutional corpora and developing and evaluating the generalizability of classification models, including large language models (LLMs), for detecting SDoH factors using data from four institutions. Clinical notes were annotated with 21 SDoH factors at two levels: level 1 (SDoH factors only) and level 2 (SDoH factors and associated values). Compared to other models, instruction tuned LLM achieved top performance with micro-averaged F1 over 0.9 on level 1 corpora and over 0.84 on level 2 corpora. While models performed well when trained and tested on individual datasets, cross-dataset generalization highlighted remaining obstacles. Access to trained models will be made available at https://github.com/BIDS-Xu-Lab/LLMs4SDoH .
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.