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Identifying Transportation Needs in Ophthalmology Clinic Notes Using Natural Language Processing: Retrospective,

Lauren M Wasser1, Hai-Wei Liang1, Chenyu Li2

  • 1Department of Ophthalmology, University of Pittsburgh School of Medicine, 1622 Locust Street, 5th floor, Pittsburgh, PA, 15219, United States, 1 412-642-5382.

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|September 6, 2025
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Summary
This summary is machine-generated.

Natural language processing (NLP) can identify transportation insecurity in eye care notes, helping patients access resources. This method accurately detects challenges in accessing healthcare, improving patient support and outcomes.

Keywords:
electronic health recordnatural language processingsocial determinants of healthsocial needs screeningtransportation needs

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Area of Science:

  • Ophthalmology
  • Health Informatics
  • Natural Language Processing

Background:

  • Transportation insecurity is a significant barrier to accessing eye care, negatively impacting patient visual outcomes.
  • Electronic health records often lack structured data on transportation challenges, hindering patient identification and support.
  • Free-text clinical notes offer a potential avenue for capturing transportation-related barriers more effectively.

Purpose of the Study:

  • To develop and validate a natural language processing (NLP) algorithm for identifying transportation insecurity in free-text ophthalmology clinic notes.
  • To assess the feasibility of using NLP to detect transportation barriers in electronic health records.
  • To improve the identification of patients facing challenges in accessing eye care due to transportation.

Main Methods:

  • A retrospective, cross-sectional study analyzing 1,801,572 ophthalmology clinic notes from adult patients (2016-2023).
  • Development of a rule-based NLP algorithm to detect transportation insecurity within deidentified free-text clinical documentation.
  • Validation of the NLP algorithm against a gold-standard expert review, using precision, recall, and F1-scores to evaluate performance.

Main Results:

  • The NLP algorithm successfully identified 726 patients (0.6%) with transportation insecurity, demonstrating high performance (precision 0.860, recall 0.960, F1-score 0.778).
  • Older patients (≥80 years) were significantly more likely to experience transportation insecurity compared to younger adults.
  • Asian patients were less likely to have transportation insecurity identified compared to White patients; no significant differences were found by sex or race (Black vs. White).

Conclusions:

  • Natural language processing (NLP) is a viable tool for accurately identifying transportation insecurity from unstructured ophthalmology clinical notes.
  • This NLP approach can aid in proactively identifying patients with transportation barriers, facilitating timely referrals to necessary resources.
  • Improving the capture of transportation insecurity can lead to better patient support and potentially enhance visual outcomes in eye care settings.