Assessing the impact of streaming in a regional emergency department.

OBJECTIVE: To evaluate the impact of a streaming model, previously validated in metropolitan EDs, on selected performance indicators in a regional ED. METHOD: Multiple linear regression models were applied to monthly time series data from 43 months prior to the intervention and 15 months following the intervention to measure the impact of the streaming model on the following performance indicators: (i) percentage of emergency patients admitted to an inpatient bed within 8 h; (ii) percentage of non-admitted emergency patients with a length of stay of less than 4 h; and (iii) percentage of emergency patients who left without being seen by a doctor or nurse practitioner. SETTING: Bendigo Health ED in regional Victoria. RESULTS: Prior to the introduction of streaming, there was a downward trend in both the percentage of emergency patients admitted to an inpatient bed within 8 h, and the percentage of non-admitted emergency patients with a length of stay of less than 4 h. After the introduction of streaming, these trends were reversed (P = 0.008 and P = 0.004, respectively). There was no statistically significant change in the trend associated with the percentage of emergency patients who left without being seen (P = 0.904). CONCLUSIONS: The implementation of the streaming model had an impact on the two performance indicators associated with length of stay in this regional ED, but did not have a significant impact (positive or negative) on the percentage of patients who did not wait to be seen. These results might interest other EDs in regional hospitals.

Forecasting emergency department presentations.

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.