SynthesisJournal of the American Medical Informatics Association : JAMIA2021
Extracting social determinants of health from electronic health records using natural language processing: a systematic review.
Synthesis in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 134 papers, 4 of them syntheses 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
134 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Biopsychosocial and Environmental Factors That Impact Brain-Gut-Microbiome Interactions in Obesity.Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association · 2026Pooled it
- Geo-Enabling Public Health: A Systematic Review of GIS Applications.Advances in experimental medicine and biology · 2026Pooled it
- Population risk stratification tools and interventions for chronic disease management in primary care: a systematic literature review.BMC health services research · 2025Pooled it
- Machine learning-based infection diagnostic and prognostic models in post-acute care settings: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2025Pooled it
- Article
- Scalable extraction of social determinants of health from clinical notes in a sepsis cohort using instruction-tuned language models.JAMIA open · 2026Article
- Artificial intelligence for personalized multiple micronutrient supplementation in maternal health.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2026Review
- Extracting Social Determinants of Health From Electronic Health Records: Development and Comparison of Rule-Based and Large Language Model Methods.JMIR medical informatics · 2026Article
- Article
- SynthEHR-eviction: enhancing eviction SDoH detection with LLM-augmented synthetic EHR data.NPJ digital medicine · 2026Article
- Integration of fairness-awareness into clinical language processing models.Communications medicine · 2026Article
- Experiences of Social Risk Screening in the Safety-Net Among Patients with Mental Health Needs.Journal of general internal medicine · 2026Article
- Association between PTSD and health-related social needs in US Veterans: an NLP analysis using Veterans Health Administration Data.Journal of affective disorders · 2026Article
- Shifting emergency department utilization patterns among vulnerable populations during the COVID-19 pandemic.BMC public health · 2026Observational
- Development of a rule-based natural language processing algorithm to extract sleep information in pediatric primary care patients with a sleep diagnosis.Sleep advances : a journal of the Sleep Research Society · 2026Article
- Multimodal Training to Unimodal Deployment: Leveraging Unstructured Data During Training to Optimize Structured Data Only Deployment.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- SDoH-GPT: using large language models to extract social determinants of health.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Comparison of local large language models for extraction of signs and symptoms data from electronic health records.PloS one · 2026Article
- Augmenting large language models to predict social determinants of mental health in opioid use disorder using patient clinical notes.JAMIA open · 2025Article
- Leveraging Artificial Intelligence to Inform Care Coordination by Identifying and Intervening in Patients' Unmet Social Needs: A Scoping Review.Journal of advanced nursing · 2025Article
74 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
22 authors.
Funding
Abstract
objectiveSocial determinants of health (SDoH) are nonclinical dispositions that impact patient health risks and clinical outcomes. Leveraging SDoH in clinical decision-making can potentially improve diagnosis, treatment planning, and patient outcomes. Despite increased interest in capturing SDoH in electronic health records (EHRs), such information is typically locked in unstructured clinical notes. Natural language processing (NLP) is the key technology to extract SDoH information from clinical text and expand its utility in patient care and research. This article presents a systematic review of the state-of-the-art NLP approaches and tools that focus on identifying and extracting SDoH data from unstructured clinical text in EHRs. MATERIALS AND
methodsA broad literature search was conducted in February 2021 using 3 scholarly databases (ACL Anthology, PubMed, and Scopus) following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 6402 publications were initially identified, and after applying the study inclusion criteria, 82 publications were selected for the final review.
resultsSmoking status (n = 27), substance use (n = 21), homelessness (n = 20), and alcohol use (n = 15) are the most frequently studied SDoH categories. Homelessness (n = 7) and other less-studied SDoH (eg, education, financial problems, social isolation and support, family problems) are mostly identified using rule-based approaches. In contrast, machine learning approaches are popular for identifying smoking status (n = 13), substance use (n = 9), and alcohol use (n = 9).
conclusionNLP offers significant potential to extract SDoH data from narrative clinical notes, which in turn can aid in the development of screening tools, risk prediction models, and clinical decision support systems.
Indexed as
Identifiers
What Socratic 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 Socratic graph.