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.