This award will contribute to the advancement of the national health and welfare by studying effective use of teleretinal screening and design of an integrated screening system for diabetic retinopathy. Diabetic retinopathy is the most common diabetic eye disease in the US and the leading cause of blindness in American adults. While diabetes-related vision loss is mostly preventable with early detection and treatment, national screening rates are significantly lower than desired, with disproportionately limited access for minority patients with a low socioeconomic status. This award supports a fundamental understanding of screening system design that utilizes teleretinal imaging technology to reduce the disparities and increase population-level screening. This project is expected to have a particular impact on the health and welfare of minority and underrepresented patients by identifying cost-effective screening policies and optimal screening locations. This award will fund a doctoral student in inter-disciplinary research and create educational materials for both operations researchers and healthcare professionals in ophthalmology, telehealth, and public health.
This research will model an integrated screening system design in a bilevel optimization framework: (i) the leader's problem is modeled as a probabilistic maximal covering location problem to identify optimal locations of teleretinal screening facilities in a large county health system, and (ii) the followers' problems address screening decisions for individual patients and are modeled as partially observable Markov decision processes to maximize both health benefit and patient adherence. Because of the computational challenges of the bilevel framework, the research will develop appropriate approximations by analyzing solution structures and value functions of the lower-level problems that enable simple solution forms and developing geography-based, distributed heuristics that approximate the interaction between the location decisions and patient-level screening decisions. The research is expected to lead to advances in bilevel optimization where there are many lower-level problems involving stochastic and sequential decision making.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
