Aryan Ayati, Shadera Azzam, Stella Ko, Cobi Ben-David, Michelle Wang, Nicole Bonine, David Tabano, Nina Malik, Frank Brodie, Mitul C Mehta, Vivek A Rudrapatna
{"title":"Quantifying Barriers to Diabetic Eye Screening: A Two-Center Study at the University of California.","authors":"Aryan Ayati, Shadera Azzam, Stella Ko, Cobi Ben-David, Michelle Wang, Nicole Bonine, David Tabano, Nina Malik, Frank Brodie, Mitul C Mehta, Vivek A Rudrapatna","doi":"10.2337/dc25-0951","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to evaluate the diabetic eye disease screening continuum at two academic centers and identify its barriers.</p><p><strong>Research design and methods: </strong>We analyzed health records from the University of California, San Francisco and University of California, Irvine to identify primary care patients needing diabetic eye screening. We tracked referrals, screenings, diagnoses, and treatments to evaluate predictors and the impact of an automated referral system. We analyzed physician notes using GPT-4o to determine reasons for missed screenings.</p><p><strong>Results: </strong>Of 8,240 unscreened patients with type 2 diabetes mellitus (T2DM), 43% received a referral, and only 16% completed screening within 1 year. Demographic, provider, and socioeconomic factors predicted adherence, with referrals being the strongest predictor. An automated referral system could improve screening rates to 22-34%. Clinician notes cited comorbidities, scheduling challenges, logistical issues, coronavirus disease 2019, and personal circumstances as barriers.</p><p><strong>Conclusions: </strong>Many patients with T2DM remain unscreened after primary care visits. Although an automated referral system may partially improve adherence, additional tailored strategies are needed.</p>","PeriodicalId":93979,"journal":{"name":"Diabetes care","volume":" ","pages":""},"PeriodicalIF":16.6000,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Diabetes care","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2337/dc25-0951","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Objective: This study aimed to evaluate the diabetic eye disease screening continuum at two academic centers and identify its barriers.
Research design and methods: We analyzed health records from the University of California, San Francisco and University of California, Irvine to identify primary care patients needing diabetic eye screening. We tracked referrals, screenings, diagnoses, and treatments to evaluate predictors and the impact of an automated referral system. We analyzed physician notes using GPT-4o to determine reasons for missed screenings.
Results: Of 8,240 unscreened patients with type 2 diabetes mellitus (T2DM), 43% received a referral, and only 16% completed screening within 1 year. Demographic, provider, and socioeconomic factors predicted adherence, with referrals being the strongest predictor. An automated referral system could improve screening rates to 22-34%. Clinician notes cited comorbidities, scheduling challenges, logistical issues, coronavirus disease 2019, and personal circumstances as barriers.
Conclusions: Many patients with T2DM remain unscreened after primary care visits. Although an automated referral system may partially improve adherence, additional tailored strategies are needed.