OBJECTIVE: To forecast the number of patients who will present each month at the emergency department of a hospital in regional Victoria. METHODS: The data on which the forecasts are based are the number of presentations in the emergency department for each month from 2000 to 2005. The statistical forecasting methods used are exponential smoothing and Box-Jenkins methods as implemented in the software package SPSS version 14.0 (SPSS Inc, Chicago, Ill, USA). RESULTS: For the particular time series, of the available models, a simple seasonal exponential smoothing model provides optimal forecasting performance. Forecasts for the first five months in 2006 compare well with the observed attendance data. CONCLUSIONS: Time series analysis is shown to provide a useful, readily available tool for predicting emergency department demand. The approach and lessons from this experience may assist other hospitals and emergency departments to conduct their own analysis to aid planning.
OBJECTIVE: To forecast the number of patients who will present each month at the emergency department of a hospital in regional Victoria. METHODS: The data on which the forecasts are based are the number of presentations in the emergency department for each month from 2000 to 2005. The statistical forecasting methods used are exponential smoothing and Box-Jenkins methods as implemented in the software package SPSS version 14.0 (SPSS Inc, Chicago, Ill, USA). RESULTS: For the particular time series, of the available models, a simple seasonal exponential smoothing model provides optimal forecasting performance. Forecasts for the first five months in 2006 compare well with the observed attendance data. CONCLUSIONS: Time series analysis is shown to provide a useful, readily available tool for predicting emergency department demand. The approach and lessons from this experience may assist other hospitals and emergency departments to conduct their own analysis to aid planning.
Authors: Nathan R Hoot; Larry J LeBlanc; Ian Jones; Scott R Levin; Chuan Zhou; Cynthia S Gadd; Dominik Aronsky Journal: Ann Emerg Med Date: 2008-04-03 Impact factor: 5.721