Choice of models for the analysis and forecasting of hospital beds.
There is growing concern that current health care services are not sustainable. The compartmental flow model provides the opportunity for improved decision-making about bed occupancy decisions, particularly those of a strategic nature. This modelling can be applied to complement infrastructure and workforce-planning methods. Discussion about appropriateness of the level of model complexity, the degree of fit and the ability to use compartmental flow models for generalization and forecasting has been lacking. The authors investigated model selection and assessment in relation to hospital bed compartment flow models. A compartment model for a range of scenarios was created. The training and test data related to the 1998 and 1999 calendar years, respectively. The majority of scenarios tested were based upon commonly used periods that describe periods of time. The goodness-of-fit achieved by optimisation was measured against the training and test data. Model fit improved with increasing complexity as expected. The analysis of model fit against the test data showed that increasing model complexity did result in over-fitting, and better prediction was achieved with a relatively simple model. In terms of generalisation, the seasonal models performed best. Single day census type models, which have been used by Millard and his colleagues, were also generated. The performance of these models was similar, but inferior to that of the models generated from a full year of training data. The additional data make the models better able to capture the variation across the year in activity.
Mackay
Mark
M
Department of Psychology, University of Adelaide, South Australia. mark.mackay@health.sa.gov.au
Lee
Michael
M
D001509:Bed Occupancy / Q000706:statistics & numerical data; D003198:Computer Simulation; D003660:Decision Support Systems, Management; D005544:Forecasting / Q000379:methods; D006742:Hospital Bed Capacity; D006752:Hospital Planning / Q000379:methods*; D006784:Hospitals, Teaching / Q000706:statistics & numerical data*; D006801:Humans; D007902:Length of Stay / Q000706:statistics & numerical data; D015233:Models, Statistical; D020380:Needs Assessment / Q000706:statistics & numerical data*; D013021:South Australia