Strategic level proton therapy patient admission planning: a Markov decision process modeling approach

Strategic level proton therapy patient admission planning: a Markov decision process modeling approach

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Article ID: iaor20171686
Volume: 20
Issue: 2
Start Page Number: 286
End Page Number: 302
Publication Date: Jun 2017
Journal: Health Care Management Science
Authors: , ,
Keywords: medicine, programming: markov decision, simulation, stochastic processes, queues: applications, scheduling
Abstract:

A relatively new consideration in proton therapy planning is the requirement that the mix of patients treated from different categories satisfy desired mix percentages. Deviations from these percentages and their impacts on operational capabilities are of particular interest to healthcare planners. In this study, we investigate intelligent ways of admitting patients to a proton therapy facility that maximize the total expected number of treatment sessions (fractions) delivered to patients in a planning period with stochastic patient arrivals and penalize the deviation from the patient mix restrictions. We propose a Markov Decision Process (MDP) model that provides very useful insights in determining the best patient admission policies in the case of an unexpected opening in the facility (i.e., no‐shows, appointment cancellations, etc.). In order to overcome the curse of dimensionality for larger and more realistic instances, we propose an aggregate MDP model that is able to approximate optimal patient admission policies using the worded weight aggregation technique. Our models are applicable to healthcare treatment facilities throughout the United States, but are motivated by collaboration with the University of Florida Proton Therapy Institute (UFPTI).

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