Implementing a Monte‐Carlo simulation on admission decisions

Purpose – Although very significant and applicable, there have been no formal justifications for the use of Monte‐Carlo models and Markov chains in evaluating hospital admission decisions or concrete data supporting their use. For these reasons, this research was designed to provide a deeper understanding of these models. The purpose of this paper is to examine the usefulness of a computerized Monte‐Carlo simulation of admission decisions under the constraints of emergency departments. Design/methodology/approach – The authors construct a simple decision tree using the expected utility method to represent the complex admission decision process terms of quality adjusted life years (QALY) then show the advantages of using a Monte‐Carlo simulation in evaluating admission decisions in a cohort simulation, using a decision tree and a Markov chain. Findings – After showing that the Monte‐Carlo simulation outperforms an expected utility method without a simulation, the authors develop a decision tree with such a model. real cohort simulation data are used to demonstrate that the integration of a Monte‐Carlo simulation shows which patients should be admitted. Research limitations/implications – This paper may encourage researchers to use Monte‐Carlo simulation in evaluating admission decision implications. The authors also propose applying the model when using a computer simulation that deals with various CVD symptoms in clinical cohorts. Originality/value – Aside from demonstrating the value of a Monte‐Carlo simulation as a powerful analysis tool, the paper's findings may prompt researchers to conduct a decision analysis with a Monte‐Carlo simulation in the healthcare environment.