Emergency department (ED) overcrowding is a challenge faced by many hospitals. One approach to mitigate overcrowding is to anticipate high levels of overcrowding. The purpose of this study was to forecast a measure of ED overcrowding four hours in advance to allow clinicians to prepare for high levels of overcrowding. The chosen measure of ED overcrowding was ED length of stay compliance measures set by the Ontario government. A feed-forward artificial neural network (ANN) was designed to perform a time series forecast on the number of patients that were non-compliant. Using the ANN compared to historical averages, a 70% reduction in the root mean squared error was observed as well as good discriminatory ability of the ANN model with an area under the receiver operating characteristic curve of 0.804. Therefore, using ANNs to forecast ED overcrowding gives clinicians an opportunity to be proactive, rather than reactive, in ED overcrowding crises.
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/33580 |
| Date | 27 November 2012 |
| Creators | Wang, Jonathan |
| Contributors | Carter, Michael W. |
| Source Sets | University of Toronto |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
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