Autonomous AI testing for diabetic eye disease closes care gap and improves health equity on a systems level

Abstract

We aimed to examine the change in adherence to annual diabetic eye disease (DED) testing in an integrated healthcare system (Johns Hopkins Community Physicians 30+ community based primary care clinics) before and after deployment of autonomous artificial intelligence (AI). Adherence to annual DED testing is defined as completed evaluation by either a human ophthalmology provider, or autonomous AI within a given calendar year. During the COVID pandemic, autonomous AI (IDx‐DR, Digital Diagnostics, Coralville, IA) was deployed at multiple clinics, so that by 2021, some clinics had autonomous AI (AI sites), while others did not (non‐AI sites). Overall adherence in 2019 (pre‐AI) and 2021 (with AI) were compared, and stratified by demographics, using chi‐square or Fisher's exact test. Changes from 2019 to 2021 in odds of adherence within each subgroup were assessed for significant differences by site type (AI vs. non‐AI), using logistic regression with a site type‐by‐time interaction term. From 2019 to 2021, the overall adherence rate increased from 42.6% to 55.5% at AI sites (1949 patients), and increased from 38.0% to 41.0% at non‐AI sites (5379 patients). The increase in overall adherence rate at AI sites was significantly greater than that at non‐AI sites.