Using Machine Learning to Identify Ophthalmology Subspecialty Care and Advance Workforce Research with the IRIS® Registry (Intelligent Research in Sight).
Publication/Presentation Date
1-1-2025
Abstract
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.
Volume
5
Issue
6
First Page
100855
Last Page
100855
ISSN
2666-9145
Published In/Presented At
Jeon, J. H., Lee, J. Y., Elze, T., Miller, J. W., Lorch, A. C., Ong, M. S., Wu, A. C., Hunter, D. G., Oke, I., & IRIS Registry Analytic Center Consortium (2025). Using Machine Learning to Identify Ophthalmology Subspecialty Care and Advance Workforce Research with the IRIS® Registry (Intelligent Research in Sight). Ophthalmology science, 5(6), 100855. https://doi.org/10.1016/j.xops.2025.100855
Disciplines
Business Administration, Management, and Operations | Health and Medical Administration | Management Sciences and Quantitative Methods
PubMedID
40727325
Department(s)
Administration and Leadership
Document Type
Article