ArticleMayo Clinic proceedings. Digital health2024
Automated Identification of Patients' Unmet Social Needs in Clinical Text Using Natural Language Processing.
Article in Mayo Clinic proceedings. Digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.
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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
7 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
- Computational Use of Patient-Provider Secure Messaging Data to Achieve Better Clinical Efficiency and Quality of Communication: A Systematic Review.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Pooled it
- A scoping review of studies on secure messaging through patient portals: persistent challenges and potential solutions.npj health systems · 2026Article
- Identification of Patients for a Community Health Worker Program Using an Artificial Intelligence Algorithm.Learning health systems · 2026Article
- Exploring a Digital Health Solution to Collect and Manage Health-Related Needs for Patients Who Undergo Complex Surgery: Mixed Methods Study.Journal of medical Internet research · 2025Article
- Extracting Housing and Food Insecurity Information From Clinical Notes Using cTAKES.Health services research · 2025Article
- Implementing Social Determinants of Health Screening in US Emergency Departments.JAMA network open · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Objective: To develop natural language processing (NLP) solutions for identifying patients' unmet social needs to enable timely intervention. Patients and Methods: Design: A retrospective cohort study with review and annotation of clinical notes to identify unmet social needs, followed by using the annotations to develop and evaluate NLP solutions. Participants: A total of 1103 primary care patients seen at a large academic medical center from June 1, 2019, to May 31, 2021 and referred to a community health worker (CHW) program. Clinical notes and portal messages of 200 age and sex-stratified patients were sampled for annotation of unmet social needs. Systems: Two NLP solutions were developed and compared. The first solution employed similarity-based classification on top of sentences represented as semantic embedding vectors. The second solution involved designing of terms and patterns for identifying each domain of unmet social needs in the clinical text. Measures: Precision, recall, and f1-score of the NLP solutions. Results: A total of 5675 clinical notes and 475 portal messages were annotated, with an inter-annotator agreement of 0.938. The best NLP solution achieved an f1-score of 0.95 and was applied to the entire CHW-referred cohort (n=1103), of whom >80% had at least 1 unmet social need within the 6 months before the first CHW referral. Financial strain and health literacy were the top 2 domains of unmet social needs across most of the sex and age strata. Conclusion: Clinical text contains rich information about patients' unmet social needs. The NLP can achieve good performance in identifying those needs for CHW referral and facilitate data-driven research on social determinants of health.
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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.