AbstractsEngineering

Chronic diseases are persistent medical conditions which affect half of all adults in the United States. The nature of these long-term chronic conditions present monitoring and treatment challenges to practicing clinicians and medical researchers: (1) how to use information learned about each patient's disease characteristics over time to tailor monitoring and treatment decisions, (2) how to make sequential decisions when each decision has strong implications for future decisions, and (3) how to incentivize adherence to prescribed medications. By combining operations research with the principles of personalized medicine, this work develops novel mathematical models to answer high impact clinical questions faced when managing patients with chronic conditions. We begin our research by understanding how information about a single patient can be used to personalize the patient's forecasted disease dynamics and likelihood of disease progression. Next, we consider how models of heterogeneity in disease characteristics and patient behavior can be embedded within an optimization framework to design individualized treatment plans. Finally, we develop a model for copayment restructuring to improve patient adherence to individualized treatment plans.