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

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 2years 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 8weeks 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.