Conditional phase‐type distributions for modelling patient length of stay in hospital

Abstract

Abstract The proportion of elderly in the population is continuing to increase, placing additional demands on highly competitive medical budgets. The management of the care of the elderly within hospitals can be assisted by the accurate modelling of the length of stay of patients in hospital. This paper uses conditional phase‐type distributions for modelling the length of stay of a group of elderly patients in hospital. The model incorporates the use of Bayesian belief networks with Coxian phase‐type distributions, a special type of Markov model that describes the duration of stay in hospital as a process consisting of a sequence of latent phases. The incorporation of the Bayesian belief network in the model permits the inclusion of additional patient information which may provide a better understanding of the system, in particular the incorporation of any potential causal information that may exist in the data.