TITLE:  Optimal Coinsurance Rates for a Heterogeneous Patient Population under Constraints on Inequality and Resources

ABSTRACT:

While operations research has contributed heavily to the derivation of optimal treatment guidelines for chronic disease, patient adherence to treatment plans is low and variable. It is estimated that half of all patients at risk of cardiovascular disease are not fully adherent to their prescribed medications. One mechanism for improving patient adherence to guidelines is to tailor coinsurance rates for prescription medications to patient characteristics. For patients insured by Medicare, we seek to find coinsurance rates which maximize the welfare of the heterogeneous patient population at risk for cardiovascular disease. We analyze the problem as a bilevel optimization model where the lower optimization problem has the structure of a Markov decision process which determines the optimal hypertension treatment plan for each patient class. The upper optimization problem is a nonlinear resource allocation problem with constraints on total expenditures and coinsurance inequality. The models are parameterized using data from the National Health and Nutrition Examination Survey (NHANES). We find that optimizing coinsurance rates can be a cost-effective intervention for improving patient adherence and health outcomes, particularly for those patients at high risk for cardiovascular disease. This research is done in collaboration with Greggory J. Schell (former PhD student) as well as University of Michigan and U.S. Department of Veteran Affairs clinicians: Rodney A. Hayward, and Jeremy B. Sussman.

BIO:

Dr. Mariel Lavieri is an Assistant Professor in the Department of Industrial and Operations Engineering at the University of Michigan.  She has bachelor's degrees in Industrial and Systems Engineering and Statistics and a minor in String Bass Performance from the University of Florida. She holds a Masters and PhD in Management Science from the University of British Columbia. In her work, she applies operations research to healthcare topics. Her most recent research develops dynamic programming, stochastic control, and continuous, partially observable state space models to guide screening, monitoring and treatment decisions of chronic disease patients. She has also developed models for health workforce planning which take into account training requirements, workforce attrition, capacity planning, promotion rules and learning. Dr. Lavieri is the recipient of the Bonder Scholarship, and an honorary mention in the George B. Dantzig Dissertation award. She received the 2009 Pierskalla Award for the best paper presented in the Health Applications Society at INFORMS and mentored students who won the 2012 Doing Good with Good OR, the 2013 Society for Medical Decision Making Lee Lusted Award for Quantitative Methods and Theoretical Developments and the 2015 IBM Research Service Science Best Student Paper Award (hosted by the INFORMS Service Science Section). Dr. Lavieri was named the 2013 Young Participant with Most Practical Impact by the International Conference on Operations Research.