ArticleJMIR medical informatics2025
Identifying Transportation Needs in Ophthalmology Clinic Notes Using Natural Language Processing: Retrospective, Cross-Sectional Study.
Article in JMIR medical informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 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
- Loss to follow-up and increase in cup-to-disc ratio among glaucoma patients in the IRIS Registry.Canadian journal of ophthalmology. Journal canadien d'ophtalmologie · 2026Article
- Artificial intelligence-driven diabetic retinopathy research: mapping the evolution, coupling, and global collaboration landscape (1996-2026).Frontiers in endocrinology · 2026Review
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Authors and funding
8 authors.
Funding
Abstract
Background: Transportation insecurity is a known barrier to accessing eye care and is associated with poorer visual outcomes for patients. However, its mention is seldom captured in structured data fields in electronic health records, limiting efforts to identify and support affected patients. Free-text clinical documentation may more efficiently capture information on transportation-related challenges than structured data. Objective: In this study, we aimed to identify mention of transportation insecurity in free-text ophthalmology clinic notes using natural language processing (NLP). Methods: In this retrospective, cross-sectional study, we examined ophthalmology clinic notes of adult patients with an encounter at a tertiary academic eye center from 2016 to 2023. Demographic information and free text from clinical notes were extracted from electronic health records and deidentified for analysis. Free text was used to develop a rule-based NLP algorithm to identify transportation insecurity. The NLP algorithm was trained and validated using a gold-standard expert review, and precision, recall, and F1-scores were used to evaluate the algorithm's performance. Logistic regression evaluated associations between demographics and transportation insecurity. Results: A total of 1,801,572 clinical notes of 118,518 unique patients were examined, and the NLP algorithm identified 726 (0.6%) patients with transportation insecurity. The algorithm's precision, recall, and F1-score were 0.860, 0.960, and 0.778, respectively, indicating high agreement with the gold-standard expert review. Patients with identified transportation insecurity were more likely to be older (OR 3.01, 95% CI 2.38-3.78 for those aged ≥80 vs 18-60 y) and less likely to identify as Asian (OR 0.04, 95% CI 0-0.18 for Asian patients vs White patients). There was no difference by sex (OR 1.13, 95% CI 0.97-1.31) or between the Black and White races (OR 0.98, 95% CI 0.79-1.22). Conclusions: NLP has the potential to identify patients experiencing transportation insecurity from ophthalmology clinic notes, which may help to facilitate referrals to transportation resources.
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