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Artificial intelligence deployed on Electronic Health Record data: Strategies for identification and referral of at-risk patients from primary care physicians to eyecare specialists.
ABSTRACT
Background:
Identification and referral of at-risk patients from primary care practitioners (PCPs) to eye care professionals remains problematic. Approximately 1.9 million Americans suffer from vision loss as a result of undiagnosed or untreated ophthalmic conditions. Artificial intelligence modeling of 1,486,078 patients identifies individuals at higher risk for glaucoma, age-related macular degeneration, diabetic retinopathy, visually significant cataracts, and ocular surface disease.
Objective:
To build and compare artificial intelligence (AI) strategies, applicable to Electronic Health Records of primary care physicians (PCPs), capable of triaging patients for referral to eyecare specialists.
Methods:
From the Optum® de-identified Electronic Health Record dataset, 743,039 patients with age-related macular degeneration (AMD), visually significant cataract, diabetic retinopathy, glaucoma, or ocular surface disease (OSD) were exact matched on age and gender to 743,039 controls without eye conditions. Between 142-182 non-ophthalmic parameters per patient were inputted into five AI models: Generalized Linear Model (GLM), L1-regularized logistic regression, random forest, XGBoost, and J-48 decision trees. Model performance was compared for each pathology to select the most predictive algorithm. Area under the curve (AUC) was assessed for all algorithms for each outcome.
Results:
XGBoost demonstrated the best performance, showing, respectively, prediction accuracy and AUC of 78.6% and 0.878 for visually significant cataract, 77.4% and 0.858 for exudative AMD, 79.2% and 0.879 for non-exudative AMD, 72.2% and 0.803 for OSD requiring medication, 70.8% and 0.785 for glaucoma, 85.0% and 0.924 for type 1 nonproliferative diabetic retinopathy (NPDR), 82.2% and 0.911 for type 1 proliferative diabetic retinopathy (PDR), 81.3% and 0.891 for type 2 NPDR, and 82.1% and 0.900 for type 2 PDR.
Conclusions:
The five AI strategies deployed were able to successfully identify patients with elevated odds ratios (ORs [95% CI]), thus capable of patient triage, for ocular pathology ranging from 2.4 [2.4-2.5] (glaucoma) to 5.7 [5.0-6.4] (type 1 NPDR), with an average OR of 3.9. The application of these models could enable PCPs to better identify and triage patients at risk for ophthalmic pathology. Clinical Trial: N/A
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