Nonhomogeneous Markov decision processes in medical decision making

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The purpose of this proposal is to develop a mathematical framework for sequential stochastic decision making when the set of available actions, and their effects, changes over time. Modeled as nonhomogeneous Markov decision processes (MDPs), such situations apply to decisions about equipment replacement, facility location, and workforce planning. Our motivation comes from medical decision making questions. For example, when should a patient initiate or switch drug therapies when billions of dollars are invested annually in the research and development (R&D) of new therapies? Standard decision models do not consider this dynamic, yet websites have recently been created to disseminate information to physicians and their patients on clinical trials under way and results from trials already completed. Presumably, models that explicitly consider the uncertainty around effective therapies becoming available in the future would yield different results than models that do not consider this uncertainty.