Optimizing population health outcomes in diabetic retinopathy through personalized and scalable screening strategies

Project Summary / Abstract According to the National Eye Institute (NEI), early detection and timely treatment can reduce the risk of severe vision loss from diabetic retinopathy (DR) by 95%. Yet, DR remains the leading cause of blindness among American adults. By 2030, 54.9 million Americans are expected to have diabetes. The prevalence of vision-threatening diabetic retinopathy (VTDR) among people with diabetes is 4.4% and 10.2% in the US and worldwide, respectively, representing over 28 million people at risk of blindness. Because DR causes no pain, vision loss, or other symptoms at its early stages and only 50% of people with diabetes receive annual eye exams, many will be unaware of their disease until vision loss is irreversible. Retinal Care Inc. (RCI) believes that the root cause of this public health failure is more behavioral and educational than clinical; complete adherence to screening and treatment would prevent nearly all vision loss from DR. However, 100% adherence is not feasible, practical, or cost-effective. Eliminating blindness from DR requires a strategy shift that acknowledges that preventing vision loss from DR does not require a 100% annual screening rate; it requires only that all patients with VTDR are evaluated by an eye care provider and adhere to follow up recommendations. RCI's approach to cost-effectively eliminating blindness from DR is to prioritize patients who are most likely to require immediate attention and devote the resources necessary to ensure they are evaluated by an eye care provider. The proposed project is designed to accomplish this goal through VTDR risk prediction; targeted patient engagement, education, and behavioral interventions; and optimization of the full system to achieve maximum population benefit. In Aim 1, RCI will leverage our existing Data Repository and machine learning methods to predict VTDR risk using electronic health record (EHR) and healthcare insurance claims data, with the initial goal of correctly placing over 90% of patients with VTDR in the highest-risk half of the population when ordered by risk. In doing so, RCI can focus patient engagement resources on patients who are most likely to need immediate evaluation and treatment, rather than diverting resources to patients who are less likely to require immediate attention. In Aim 2, RCI will use mixed methods framed by the Integrated Behavior Model to identify barriers and motivators for screening and assess their relative importance, develop and implement a survey instrument to elicit willingness to participate in screening based on motivating factors and barrier removal, and use natural language processing to detect barriers and facilitators for diabetic eye screening from patient communications. In Aim 3, RCI will create an agent-based simulation tool to guide care coordination recommendations in a way that maximizes population health outcomes subject to constraints on time and cost. The tool will identify optimal intervention strategies and be adaptable to changes in cost, disease burden, patient preference, population characteristics, and other parameters. Decisions that will be optimized include resource allocation as a function of risk, intervention timing, screening modality, communication strategies, barrier removal strategies, and other modifiable aspects of the system.