This research study is being conducted to improve eye care by using artificial intelligence (AI) to make diabetic eye screenings faster and more accessible. AI technology mimics human decision-making, enabling computers and systems to analyze medication information. Specifically for this screening, AI examines digital images of the eye and based on that information, may identify if a participant has diabetic retinopathy. It can assist doctors in making decisions about a participant's diagnosis, treatment or care plans to improve patient care. This is a collaboration between San Ysidro Health (SYHealth), University of California, San Diego (UC San Diego), and Eyenuk. The Kaiser Permanente Augmented Intelligence in Medicine and Healthcare Initiative (AIM-HI) awarded SYHealth funds to demonstrate the value of AI technologies in diverse, real-world settings.
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AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
DR screening completion and result
Timeframe: the enrollment (baseline) and through study completion, an average of 1 year
DR screening efficiency
Timeframe: the enrollment (baseline) and through study completion, an average of 1 year
Knowledge and attitudes about DR
Timeframe: Baseline survey at study visit and follow up survey 6-months after.
DM self-efficacy
Timeframe: Baseline survey at study visit and follow up survey 6-months after.
DM self-management
Timeframe: Baseline survey at study visit and follow up survey 6-months after.
DR Screening Satisfaction Survey (Intervention Group)
Timeframe: Baseline survey at study visit and follow up survey 12-months after (at next DR screening).
Demographic and Clinical Data
Timeframe: Intake at study 1 day visit.
Social Determinants of Health
Timeframe: Intake at study 1 day visit.