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Hospitals come in different sizes and operate under different conditions. The problem confronting healthcare planners for a long time has been: How does hospital efficiency relate to its size? Due to their complex organisational structures, it would be unrealistic, costly and risky for hospitals to conduct field experiments to test the effects of any major organisational change. Mathematical modelling and computer simulations offer an effective and risk-free approach to assess likely impacts of any proposed change. This study replicates and extends the “Hospital Event Simulation Model: Arrivals to Discharge (HESMAD)” (Ben-Tovim et al., 2016), originally built for the Flinders Medical Centre (SA), to use simulation to model patient flow in another hospital – Nambour on the Sunshine Coast, Queensland. The study will take advantage of the unique opportunity – a natural experiment occurring in the Sunshine Coast, where Nambour hospital has been downsized by more than 50% – for subsequent validation of the HESMAD model, and simulation modelling in general, with data collected before and after the downsizing. The simulations investigate hospital efficiency by examining the following organisational metrics: (1) length of stay (LOS) by diagnosis-related group (DRG); (2) total hospital utilisation; (3) emergency department (ED) utilisation, where utilisation is synonymous with occupancy rate, that is, number of occupied beds divided by total number of available beds. The model is used to run experiments on different hospital sizes, especially that of Nambour Hospital before its restructure – 100%, then 50% and 200% of the original size. The base scenario is set to be a reduction/increase in arrivals in line with hospital size while preserving the proportion of ambulance and walkin (self-presented) patients and the pre-downsizing mix of DRGs. Alternative scenarios tested also include changing the mix of ambulance/walk-in arrivals, as well as DRGs, while maintaining the total rate of arrivals. Such scenarios are realistic because, while ambulance arrivals can be controlled and diverted to other hospitals, self-presentations are much more difficult to control. Simulation results demonstrate that for the base scenario (proportional reduction) there is no change in average figures of hospital utilisation and LOS (both overall and for individual DRGs). However, there is a dramatic change in variability of the utilisation. A smaller-size hospital has a much greater dispersion in distributions and, consequently, a much higher propensity for becoming overcrowded. Alternative scenarios (corresponding to disproportional reductions) can be considered as new and very different hospitals. Changing the proportion of ambulance and walk-in patients affects the DRG mix. As a result, the overall LOS, hospital occupancy and waiting times, all change considerably. The main conclusions are: (1) Smaller size hospitals are operationally more risky; they are at a higher risk of overcrowding, while large hospitals have better ability to absorb spikes in arrivals. This may mean that provision of additional ‘surge’ capacity may be required when commissioning smaller facilities. (2) While deciding on the size of a hospital, attention should be given to the clinical function that the facility is to perform (i.e. the potential mix of DRGs) rather than just the size of a population that the hospital is serving.

Size does matter: a simulation study of hospital size and operational efficiency