TY - JOUR
T1 - Implementing a Monte-Carlo simulation on admission decisions
AU - Ben-Assuli, Ofir
AU - Leshno, Moshe
PY - 2013/2
Y1 - 2013/2
N2 - 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.
AB - 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.
KW - Accident and emergency
KW - Admissions
KW - Cohort simulation
KW - Decision making
KW - Health care
KW - Hospitals
KW - Medical decision making
KW - Monte-Carlo simulation
UR - http://www.scopus.com/inward/record.url?scp=84873436650&partnerID=8YFLogxK
U2 - 10.1108/17410391311289604
DO - 10.1108/17410391311289604
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AN - SCOPUS:84873436650
SN - 1741-0398
VL - 26
SP - 154
EP - 164
JO - Journal of Enterprise Information Management
JF - Journal of Enterprise Information Management
IS - 1
ER -