Targeted Interventions Lead to Quality Improvement in Year 2 of an Artificial Intelligence-Based Diabetic Retinopathy...

This study evaluates the second-year outcomes of an AI-based diabetic retinopathy (DR) detection program (Stanford Teleophthalmology Autonomous Testing and Universal Screening (STATUS)) implemented in primary care and endocrinology clinics in Northern California. We focused on assessing improvements following implementation of an intervention-based framework to increase AI system gradability and patient encounters.

A retrospective analysis was conducted involving diabetic patients aged 18 years and older with no prior DR diagnosis or examination in the past year. These patients presented for routine DR screening in primary care or endocrinology clinics. In its second year, the STATUS program expanded to additional sites and introduced an intervention-based framework, including targeted training protocols, to enhance screening accuracy and efficiency. Our study measured AI system gradability and tracked patient encounters over Year 2.

The AI system's gradability increased from 62.3% in Year 1 to 71.2% in Year 2, comparable to non-mydriatic gradability rates observed in clinical trials. Patient encounters increased by 21.9%, indicating expanded reach and improved accessibility. Interventions, including enhanced training protocols and camera utilization reports, effectively improved screening efficiency.

The second-year outcomes of the STATUS AI-based DR screening program demonstrate significant improvements in image gradability by the AI system as well as in patient encounter numbers. These findings highlight the potential of interventional methods to continually improve the outcomes of AI-based screening programs and offer a scalable solution to the growing burden of diabetic retinopathy. The success of STATUS supports further integration and expansion of AI-based screening in clinical practice for early detection and management of DR, improving patient outcomes.