Patient Flow Simulation Using Historically Informed Synthetic Data

Abstract

Hospital overcrowding is a major problem for healthcare systems around the globe. In order to better estimate future demands and adequate resources for coping with such demands, statistical and computerised modelling can be applied. This can then allow healthcare administrators and decision makers to quantify the impacts of various “what-if” scenarios on hospital performance measures. This paper investigates the application of Discrete Event Simulation towards optimising Emergency Department resources while measuring overall length of stay and queuing time of emergency patients as a target performance measure. In particular, we explore strategies for generating historically informed synthetic data that helps the simulation model track patient flow through the target hospital over a future time frame. Using the developed simulation model, several resource configurations are tested using data from one of the busiest emergency departments in the state of Queensland as the baseline while quantifying the impacts of such changes on key patient flow metrics. It was found that adding a single bed (and associated resources) to the emergency department would result in a 23% decrease in average patient treatment delay.