title: Forecasting emergency department arrivals: a tutorial for emergency department directors.
abstract: This article is a tutorial for emergency department (ED) medical directors needing to anticipate ED arrivals in support of strategic, tactical, and operational planning and activities. The authors demonstrate our regression-based forecasting models based on data obtained from a large teaching hospital's ED. The versatility of the regression analysis is shown to readily accommodate a variety of forecasting situations. Trend regression analysis using annual ED arrival data shows the long-term growth. The monthly and daily variation in ED arrivals is captured using zero/one variables while Fourier regression effectively describes the wavelike patterns observed in hourly ED arrivals. In our study hospital, these forecasting methods uncovered: long-term growth in demand of about 1,000 additional arrivals per year; February was generally the slowest month of the year while July was the busiest month of the year; there were about 20 fewer arrivals on Fridays (the slowest day) than Sundays (the busiest); and arrivals typically peaked at about 10 per hour in the afternoons from 1 p.m. to 6 p.m., approximately. Because similar data are routinely collected by most hospitals and regression analysis software is widely available, the forecasting models described here can serve as an important tool to support a wide range of ED resource planning activities.
authors: Department of Health Policy and Management, Texas A&M Health Science Center, College Station, Texas, USA.
authors: Murray J
authors: MJ
authors: Côté
authors: Marlene A
authors: MA
authors: Smith
authors: David R
authors: DR
authors: Eitel
authors: Elif
authors: E
authors: Akçali
mesh_terms: D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005544:Forecasting / Q000379:methods; D005583:Fourier Analysis; D006295:Health Resources / Q000458:organization & administration; D006801:Humans; D020380:Needs Assessment / Q000706:statistics & numerical data; D010414:Pennsylvania; D008486:Physician Executives / Q000193:education*; D012044:Regression Analysis