11 research outputs found
To P. E. Laliberte, Sales Dept.--Correspondence
To P. E. Laliberte, Sales Dept. re: participation of VA division in developing proposal to the Navy for GE's bid on Sidewinder missile
Cardiovascular and metabolic responses at rest and to exercise during 48 hours of head-out immersion: a case report
The energetics of surface events in finswimming: Analysis using the critical velocity method
Statistical Methods for Developmental Toxicity: Analysis of Clustered Multivariate Binary Data
Pars Plana Vitrectomy with Internal Limiting Membrane Peeling for Nontractional Diabetic Macular Edema
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