Abstract
261-OR: Autonomous Artificial Intelligence (AI) Testing for Diabetic Eye Disease (DED) Closes Care Gap and Improves Health Equity on a Systems Level
Diabetes (New York, N.Y.), Vol.72(Supplement_1)
06/20/2023
DOI: 10.2337/db23-261-OR
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 (p<0.001). Various patient subgroups (M, F, ≥65 yo, <65 yo, Black, White, Medicare/military/commercial insurance) at AI sites demonstrated a significantly greater (p<0.05) compliance rate increase when compared to the same subgroups at non-AI sites. For Black patients, there was no difference in adherence rate between AI sites and non-AI sites (38.5% vs. 37.1%, p=0.45) in 2019, but the difference became significant (p<0.001) in 2021 due to increased adherence at the AI sites (58.0%) vs. 38.8% at non-AI sites. Autonomous AI improved DED testing adherence in a large, diverse patient cohort and decreased disparity in Black patients, a historically-disadvantaged subgroup. Disclosure T.Liu: None. J.Huang: None. H.Lehmann: None. R.M.Wolf: Research Support; Dexcom, Inc., Boehringer Ingelheim Inc. R.Channa: None. M.D.Abràmoff: Board Member; Digital Diagnostics, Consultant; AbbVie Inc., NovaGo Therapeutics AG, Other Relationship; Digital Diagnostics, Stock/Shareholder; Digital Diagnostics. Funding Digital Diagnostics
Details
- Title: Subtitle
- 261-OR: Autonomous Artificial Intelligence (AI) Testing for Diabetic Eye Disease (DED) Closes Care Gap and Improves Health Equity on a Systems Level
- Creators
- T Y Alvin LiuJANE HuangHAROLD LehmannRISA M. WolfROOMASA ChannaMICHAEL D. Abràmoff
- Resource Type
- Abstract
- Publication Details
- Diabetes (New York, N.Y.), Vol.72(Supplement_1)
- DOI
- 10.2337/db23-261-OR
- ISSN
- 0012-1797
- eISSN
- 1939-327X
- Language
- English
- Date published
- 06/20/2023
- Academic Unit
- Ophthalmology and Visual Sciences; Electrical and Computer Engineering; Fraternal Order of Eagles Diabetes Research Center; Roy J. Carver Department of Biomedical Engineering
- Record Identifier
- 9984443050702771
Metrics
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