Are EMS call volume predictions based on demand pattern analysis accurate?
Most EMS systems determine the number of crews they will deploy in their communities and when those crews will be scheduled based on anticipated call volumes. Many systems use historical data to calculate their anticipated call volumes, a method of prediction known as demand pattern analysis.


OBJECTIVE
To evaluate the accuracy of call volume predictions calculated using demand pattern analysis.


METHODS
Seven EMS systems provided 73 consecutive weeks of hourly call volume data. The first 20 weeks of data were used to calculate three common demand pattern analysis constructs for call volume prediction: average peak demand (AP), smoothed average peak demand (SAP), and 90th percentile rank (90%R). The 21st week served as a buffer. Actual call volumes in the last 52 weeks were then compared to the predicted call volumes by using descriptive statistics.


RESULTS
There were 61,152 hourly observations in the test period. All three constructs accurately predicted peaks and troughs in call volume but not exact call volume. Predictions were accurate (+/-1 call) 13% of the time using AP, 10% using SAP, and 19% using 90%R. Call volumes were overestimated 83% of the time using AP, 86% using SAP, and 74% using 90%R. When call volumes were overestimated, predictions exceeded actual call volume by a median (Interquartile range) of 4 (2-6) calls for AP, 4 (2-6) for SAP, and 3 (2-5) for 90%R. Call volumes were underestimated 4% of time using AP, 4% using SAP, and 7% using 90%R predictions. When call volumes were underestimated, call volumes exceeded predictions by a median (Interquartile range; maximum under estimation) of 1 (1-2; 18) call for AP, 1 (1-2; 18) for SAP, and 2 (1-3; 20) for 90%R. Results did not vary between systems.


CONCLUSIONS
Generally, demand pattern analysis estimated or overestimated call volume, making it a reasonable predictor for ambulance staffing patterns. However, it did underestimate call volume between 4% and 7% of the time. Communities need to determine if these rates of over-and underestimation are acceptable given their resources and local priorities.
Brown
Lawrence H
LH
Department of Emergency Medicine, SUNY Upstate Medical University, Syracuse, NY, USA.
Lerner
E Brooke
EB
Larmon
Baxter
B
LeGassick
Todd
T
Taigman
Michael
M
D002140:California; D004631:Emergency Medical Service Communication Systems / Q000706:statistics & numerical data*; D005544:Forecasting; D006301:Health Services Needs and Demand; D006801:Humans; D009518:New York; D012189:Retrospective Studies; D014600:Utilization Review / Q000379:methods*