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RESEARCH ARTICLE
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Predicting hospital bed utilisation for post-surgical care by means of the Monte Carlo method with historical data

Andy Wong A B , Rob Eley https://orcid.org/0000-0003-0856-4313 A C , Paul Corry D * , Brendan Hoad E and Prasad Yarlagadda B F
+ Author Affiliations
- Author Affiliations

A Emergency Medicine, Princess Alexandra Hospital, Qld, Australia.

B School of Mechanical, Medical and Process Engineering, Faculty of Engineering, Qld, Australia.

C Faculty of Medicine, University of Queensland, Qld, Australia.

D School of Mathematical Sciences, Faculty of Science, Queensland University of Technology, Qld, Australia.

E Health Services, Queensland Children’s Hospital, Qld, Australia.

F School of Engineering, University of Southern Queensland, Qld, Australia.

* Correspondence to: p.corry@qut.edu.au

Australian Health Review https://doi.org/10.1071/AH24160
Submitted: 14 June 2024  Accepted: 4 September 2024  Published: 24 September 2024

© 2024 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of AHHA.

Abstract

Objective

This study aim was to develop a predictive model of bed utilisation to support the decision process of elective surgery planning and bed management to improve post-surgical care.

Methods

This study undertook a retrospective analysis of de-identified data from a tertiary metropolitan hospital in Southeast Queensland, Australia. With a reference sample from 2 years of historical data, a model based on the Monte Carlo method has been developed to predict hospital bed utilisation for post-surgical care of patients who have undergone surgical procedures. A separate test sample from comparable data of 8 weeks of actual utilisation was employed to assess the performance of the prediction model.

Results

Applying the developed prediction model to an 8-week period test sample, the mean percentage error of the prediction was 1.5% and the mean absolute percentage error 5.4%.

Conclusions

The predictive model developed in this study may assist in bed management and the planning process of elective surgeries, and in so doing also reduce the likelihood of Emergency Department access block.

Keywords: clinical decision support, computer simulation, hospital bed management, hospital bed prediction, hospital length of stay, Monte Carlo method, operations research, post-surgical care.

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