Using Machine Learning to Identify Ophthalmology Subspecialty Care and Advance Workforce Research with the IRIS® Registry (Intelligent Research in Sight).

PURPOSE: To develop machine-learning models to identify ophthalmology subspecialists using deidentified patient data from a large database.

DESIGN

Cross-sectional.

PARTICIPANTS

All ophthalmologists participating in the American Academy of Ophthalmology's IRIS® Registry (Intelligent Research in Sight) from 2013 to 2023 were classified under one of the following general or subspecialty categories: comprehensive, cataract, cornea, glaucoma, retina, oculofacial, pediatric, or neuro-ophthalmology.

METHODS

We collected the diagnosis, procedure, and prescription codes linked to each ophthalmologist. We performed binary subspecialty classification using random forest models with fivefold cross validation and multispecialty classification using 4 approaches (diagnosis only, procedure only, prescription only, and combined).

MAIN OUTCOME MEASURES: Model performance was assessed using area under the receiver operating characteristic curve (AUROC), F1 scores, and Matthews correlation coefficient.

RESULTS

The study included 9032 ophthalmologists. Classification accuracy differed by subspecialty (AUROC, retina: 0.981; oculofacial: 0.975; pediatric: 0.972; glaucoma: 0.937; cornea: 0.932; neuro: 0.912; cataract: 0.861; and comprehensive: 0.760). The procedure-only random forest model had better performance (AUROC, 0.903) than the diagnosis-only (0.880) and prescription-only (0.835) model.

CONCLUSIONS

Machine learning models leveraging the IRIS Registry can provide a near real-time assessment of the landscape of ophthalmic subspecialty care. Identifying subspecialty physicians through practice patterns may provide valuable insights into the future trends of eye care delivery with implications for workforce research and policy interventions.

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