Diabetic Retinopathy Screening Among Federally Qualified Health Center Patients Using Point-of-Care AI: DRES-POCAI: A Trial Protocol
Open accessAbstract
Importance: Diabetic retinopathy screening (DRS) rates have historically been low among underserved populations due to barriers in accessing traditional eye care. Although artificial intelligence (AI)–powered DRS provides a potential strategy to improve screening rates, its optimal integration into primary care workflows within federally qualified health centers (FQHCs) requires rigorous evaluation. The clinical workflow of the Diabetic Retinopathy Screening Point-of-Care Artificial Intelligence (DRES-POCAI) trial in FQHCs integrates AI-powered DRS with electronic health records (EHRs) to automate results and prompt referrals, aiming to improve screening rates and facilitate early diagnosis and timely treatment. Objective: To increase DRS rates, facilitate early-stage DR detection, improve timely eye specialist follow-up, and assess the effect of DRS on patients’ knowledge, attitudes, self-efficacy, and satisfaction. Design, Setting, and Participants: DRES-POCAI is a patient-level, multiclinic, open-label, parallel superiority randomized clinical trial at 2 FQHC sites of San Ysidro Health in San Diego County, California. The study recruitment targets 848 active FQHC patients aged 22 years or older with diabetes, no DRS in the prior 11 months, and scheduled medical visits during the intervention period. Patients with a history of retinopathy or retinal vascular occlusion and other physical or mental conditions are excluded. The study started in June 2024, with recruitment anticipated to conclude in August 2025 and follow-up until February 2026. Intervention: The intervention arm receives DRS at their primary care clinic using an AI-powered DRS system, with retinal image analysis to identify more than mild DR and vision-threatening DR. Results are immediately available in the EHRs, and practitioners receive risk-stratified referral recommendations. The usual care arm receives referrals to an FQHC optometrist or external eye care practitioner, with results transmitted to the medical home later. Main Outcomes and Measures: The primary outcome is DRS completion status. Secondary outcomes include DR diagnosis stage, specialist referrals, and participants’ DR knowledge, attitudes, and intentions regarding future AI-powered DRS. Results: Findings will be disseminated in peer-reviewed publications after data collection and analysis. Conclusions and Relevance: DRES-POCAI will determine the effectiveness of an AI-powered DRS intervention to increase DRS rates in FQHC primary care workflows. Trial Registration: ClinicalTrials.gov Identifier: NCT06721351.
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AI systems have expanded access to diabetic retinopathy screening.
“AI systems have expanded access to diabetic retinopathy screening 12”
From AI-induced never-skilling in medical education (2026) · cites this paper as [12]
The Discussion (page unavailable) says point-of-care AI offers a readily accessible screening tool that reduces barriers to care. But the Abstract’s Findings describe a trial that aims to demonstrate improved screening adherence, not a completed finding that AI systems have expanded access. The claim presents the anticipated benefit as an established outcome. (LLM, conf=0.86)
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- Authors
- Diaz, E. A., Seifert, M. L., Gruning, V., Stadnick, N. A., Lugo-Butler, E., Servin, A. N., Rodríguez-Rosales, C. I., Geremia, C., Ramachandra, C., Bhaskaranand, M., Howard, D., Solis, O., Velasquez, S., Snook, B., Tucker, S., & Muñoz, F. A.
- Affiliations
- San Ysidro Health (US)
- University of California San Diego (US)
- Eyenuk (United States) (US)
- San Ysidro Health, San Diego, California
- Corresponding author
- Diaz, Edgar A.
- Venue
- JAMA Network Open
- Publisher
- American Medical Association (AMA)
- Published
- 2025-10-21
- Volume
- 8
- Issue
- 10
- Pages
- e2538114
- ISSN
- 2574-3805 (ISSN-L)
- PMC
- PMC12541539
- OpenAlex
- W4415401594
- Cited by
- 9
- References
- 49
- Citations by year
- 2026: 9
- FWCI
- 5.19
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JAMA Network Open (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Gordon and Betty Moore Foundation (GBMF11960)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Health Sciences › Medicine › Ophthalmology › Ophthalmology and Visual Health Research
- Health Sciences › Medicine › Ophthalmology › Retinal Diseases and Treatments
- Keywords
- diabetic retinopathy screening, artificial intelligence, point-of-care screening, federally qualified health centers, underserved populations, primary care integration, electronic health records, retinal image analysis, specialist referral, screening rates, patient knowledge and attitudes, diabetes mellitus, vision-threatening retinopathy, health equity, access to eye care, patient satisfaction, self-efficacy
- MeSH terms
- Equivalence Trials as Topic, Adult, Artificial Intelligence, Diabetic Retinopathy, Female, Humans, Male, Mass Screening, Middle Aged, Primary Health Care, Randomized Controlled Trials as Topic, Point-of-Care Systems, Electronic Health Records
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