57-OR: Enhancing Diabetic Eye Disease Detection through Autonomous Artificial Intelligence Implementation in a Federally Qualified Health Center

Abstract

Diabetic eye disease (DED), specifically diabetic retinopathy (DR) and diabetic macular edema (DME), affects nearly 30 percent of people living with diabetes. Despite the severity of DED, almost half of those living with diabetes do not receive an annual eye exam for diabetes (EED) as recommended by leading professional societies. Zufall Health Center (ZHC), a Federally Qualified Health Center, faced a substantial care gap due to the high demand for annual EEDs surpassing the capacity of their onsite optometrist. In response, in April 2021, ZHC implemented an FDA-cleared autonomous artificial intelligence (AI) system for the detection of DR (including DME) into routine diabetes care. We investigated the impact of AI implementation on patient access to annual EEDs, assessing changes in completion rates before and after. Annual EEDs were defined as completion of an evaluation in the eye for DED by either an eyecare provider or autonomous AI. Completion rates for annual EEDs for patients with diabetes increased from 16.0% (314/1,904) (April 2021) to 35.0% (996/2,819) (June 2023), 529 of which were tested with autonomous AI. Between April 2021 to June 2023, 384 patients received a diagnosis from the autonomous AI. Among all patients examined by the autonomous AI, 24.0% (92/384) were identified as having signs of DED and received prompt referrals to eyecare. 292 patients tested negative, avoiding an unnecessary referral to eyecare. The integration of autonomous AI at the point of care effectively reduces access barriers, resulting in a substantial increase in DED testing rates. Disclosure M. Castro: None. D. Bishop: None. D. Weitzman: Employee; Digital Diagnostics. R. Ramirez: None.