Unseen Insights: An AI-Powered Exploration of Secure Patient Messages in Ophthalmology

To characterize the clinical and administrative concerns communicated through secure ophthalmology messaging and to assess differences in message content across patient sociodemographic groups.

Cross-sectional study of de-identified, patient-initiated secure messages sent between June 2014 and July 2024.

Patients with ophthalmic conditions who initiated secure electronic health record portal messages. Of 48 516 extracted message threads, 30 390 patient medical advice request messages from 4 817 unique patients were included after exclusion of questionnaires, courtesy messages, and clinician responses. Participants were 55.5% female, 56.9% aged 50 years or older, 48.7% White, and 85.7% non-Hispanic.

Natural language processing and large language model-assisted topic classification were used to categorize message content. Differences in message frequency by demographic subgroup were assessed using 2-proportion z tests.

Distribution of message topics and frequency of clinical concerns stratified by age, sex, race, ethnicity, and marital status.

Nearly half of all messages addressed administrative issues, including scheduling, medication refills, and insurance. Among clinical concerns, vision disturbances (20.8%), glaucoma-related symptoms (8.7%), imaging or tumor-related questions (7.5%), and postoperative concerns (7.4%) were most common. Message content differed significantly by demographic characteristics. Non-White patients more frequently raised issues related to pharmacy refills, insurance, glaucoma, and disability documentation, whereas White patients more often reported surgical concerns. Older patients more frequently messaged about glaucoma, surgery, and tumor-related issues, while female patients more often reported complications and swelling or infection.

Secure patient messages frequently include clinically relevant symptoms with potential triage implications and demonstrate demographic differences in care-seeking behavior. Systematic analysis of message content may support safer triage, improved workflow efficiency, and more equitable delivery of ophthalmic care.