A Markov Decision Model for Hospital Ward Admission Scheduling

Abstract

Hospitals continually face the challenge of planning and allocating hospitals wards to incoming patients in a stochastic demand environment. In this paper, a finite horizon Markov decision process model is developed and analyzed for the hospital ward allocation activity. The model focuses explicitly on developing policies for determining when to admit additional patients using weekly data from incoming patients and the availability of vacancies in hospital wards. Adopting a Markov decision process approach, the states of a Markov chain represent possible states of demand for ward occupancy. The decision of whether or not to admit additional patients is made using dynamic programming over a finite period planning horizon. A numerical example demonstrates the existence of an optimal state-dependent admission policy as well as the corresponding total operational costs in hospital wards.