A stochastic model for the patient-bed assignment problem with random arrivals and departures

Mojtaba Heydar (), Małgorzata M. O’Reilly (), Erin Trainer, Mark Fackrell, Peter G. Taylor and Ali Tirdad
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Mojtaba Heydar: University of Tasmania
Małgorzata M. O’Reilly: University of Tasmania
Erin Trainer: University of Tasmania
Mark Fackrell: Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers
Peter G. Taylor: Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers
Ali Tirdad: Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers

Annals of Operations Research, 2022, vol. 315, issue 2, No 9, 813-845

Abstract: Abstract We consider the patient-to-bed assignment problem that arises in hospitals. Both emergency patients who require hospital admission and elective patients who have had surgery need to be found a bed in the most appropriate ward. The patient-to-bed assignment problem arises when a bed request is made, but a bed in the most appropriate ward is unavailable. In this case, the next-best decision out of a many alternatives has to be made, according to some suitable decision making algorithm. We construct a Markov chain to model this problem in which we consider the effect on the length of stay of a patient whose treatment and recovery consists of several stages, and can be affected by stays in or transfers to less suitable wards. We formulate a dynamic program recursion to optimise an objective function and calculate the optimal decision variables, and discuss simulation techniques that are useful when the size of the problem is too large. We illustrate the theory with some numerical examples.

Keywords: Patient-bed assignment problem; Emergency department; Health care modelling; Markov chain; Dynamic programming; Approximate dynamic programming; Simulation; Optimisation (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10479-021-03982-9

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