RT Journal Article
SR Electronic
T1 Predicting patient arrivals to an accident and emergency department
JF Emergency Medicine Journal
JO Emerg Med J
FD BMJ Publishing Group Ltd and the British Association for Accident & Emergency Medicine
SP 241
OP 244
DO 10.1136/emj.2007.051656
VO 26
IS 4
A1 S W M Au-Yeung
A1 U Harder
A1 E J McCoy
A1 W J Knottenbelt
YR 2009
UL http://emj.bmj.com/content/26/4/241.abstract
AB Objectives: To characterise and forecast daily patient arrivals into an accident and emergency (A&E) department based on previous arrivals data.Methods: Arrivals between 1 April 2002 and 31 March 2007 to a busy case study A&E department were allocated to one of two arrival streams (walk-in or ambulance) by mode of arrival and then aggregated by day. Using the first 4 years of patient arrival data as a “training” set, a structural time series (ST) model was fitted to characterise each arrival stream. These models were used to forecast walk-in and ambulance arrivals for 1–7 days ahead and then compared with the observed arrivals given by the remaining 1 year of “unseen” data.Results: Walk-in arrivals exhibited a strong 7-day (weekly) seasonality, with ambulance arrivals showing a distinct but much weaker 7-day seasonality. The model forecasts for walk-in arrivals showed reasonable predictive power (r = 0.6205). However, the ambulance arrivals were harder to characterise (r = 0.2951).Conclusions: The two separate arrival streams exhibit different statistical characteristics and so require separate time series models. It was only possible to accurately characterise and forecast walk-in arrivals; however, these model forecasts will still assist hospital managers at the case study hospital to best use the resources available and anticipate periods of high demand since walk-in arrivals account for the majority of arrivals into the A&E department.