Predicting patient arrivals to an accident and emergency department.
OBJECTIVE
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.
Au-Yeung
S W M
SW
Department of Computing, Imperial College London, London, UK. swa02@doc.ic.ac.uk
Harder
U
U
McCoy
E J
EJ
Knottenbelt
W J
WJ
D000552:Ambulances / Q000706:statistics & numerical data; D004636:Emergency Service, Hospital / Q000458:organization & administration / Q000706:statistics & numerical data*; D005544:Forecasting; D006301:Health Services Needs and Demand; D006302:Health Services Research / Q000379:methods; D006801:Humans; D008131:London; D017146:Models, Organizational; D012621:Seasons; D019037:Urban Health Services / Q000458:organization & administration / Q000706:statistics & numerical data; D016138:Walking; D016526:Workload