Leveraging Natural Language Processing to Assess Follow-Up Patterns in Glaucoma Care

To develop a natural language processing (NLP) algorithm that identifies follow-up recommendations from the free text of clinical glaucoma clinic notes, and to determine the proportion of patients who lapse beyond recommended follow-up intervals.

A retrospective cohort study.

Adult glaucoma patients treated from January 2016 through December 2022 at our academic ophthalmology practice.

Structured demographic and diagnosis data were extracted alongside the free text of clinical notes. An NLP algorithm was developed to identify recommended follow-up intervals from note-free text. Lapses in care were defined as absence of a return visit within 125% of the recommended follow-up interval. Generalized estimating equations with a Poisson distribution and log link were used to identify risk factors for lapsing in care. Results are reported as adjusted risk ratios (aRRs) with 95% confidence intervals (CIs).

Natural language processing algorithm performance and proportion of patients with an identified lapse in care.

In total, 221 378 clinical notes were assessed, representing 66 228 visits by 6452 unique glaucoma patients. The NLP algorithm identified a follow-up interval for 47 084 of 66 228 (71.1%) patient visits and demonstrated high performance compared to gold standard chart review (F1 score 0.905, precision 0.948, recall 0.867). The proportion of visits that exceeded 125% of the preceding recommended follow-up interval was 20.0% (interquartile range [IQR]: 10.0-33.3). The median excess duration of the lapsed interval compared to recommended follow-up was 52.9 days (IQR: 22.0-111.5 days). The proportion of patients with any event of lapsing in follow-up was 73.7% (4754/6452). Risk factors for having at least 1 lapse in care included Black race (aRR = 1.29, 95% CI: 1.22-1.37, compared to White race) and Hispanic ethnicity (aRR = 1.53, 95% CI: 1.26-1.87), with lower risk for older age (aRR = 0.91, 95% CI: 0.86-0.97, age 71 to 80 years compared to ≤60 years). Among the 4754 patients with at least 1 lapse in care, 1183 (24.9%) never returned after lapsing.

Natural language processing can reliably detect lapses in glaucoma care using individualized follow-up intervals. Identifying lapses in care may facilitate referrals to resources to mitigate risk of loss to follow-up.

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.