Daily volume of cases in emergency call centers: construction and validation of a predictive model

Abstract

Background: Variations in the activity of emergency dispatch centers are an obstacle to the rationalization of resource allocation. Many explanatory factors are well known, available in advance and could predict the volume of emergency cases. Our objective was to develop and evaluate the performance of a predictive model of daily call center activity.

Methods: A retrospective survey was conducted on all cases from 2005 to 2011 in a large medical emergency call center (1,296,153 cases). A generalized additive model of daily cases was calibrated on data from 2005 to 2008 (1461 days, development sample) and applied to the prediction of days from 2009 to 2011 (1095 days, validation sample). Seventeen calendar and epidemiological variables and a periodic function for seasonality were included in the model.

Results: The average number of cases per day was 507 (95% confidence interval: 500 to 514) (range, 286 to 1251). Factors significantly associated with increased case volume were the annual increase, weekend days, public holidays, regional incidence of influenza in the previous week and regional incidence of gastroenteritis in the previous week. The adjusted R for the model was 0.89 in the calibration sample. The model predicted the actual number of cases within ± 100 for 90.5% of the days, with an average error of -13 cases (95% CI: -17 to 8).

Conclusions: A large proportion of the variability of the medical emergency call center's case volume can be predicted using readily available covariates.

Keywords: Emergency medical services; Health service needs and demand/trends; Models; Safety management/methods; Theoretical.

Fig. 1

Periodic (yearly) function used to adjust the daily activity to the period of the year (seasonality)

Fig. 2

Number of cases observed. Circles and red square are number of cases observed each day. A circle represents a day correctly predicted (close to 100 cases) by the model. A red square represents a day with incorrect prediction. The curve represents the number of cases which would have been predicted by the periodic function only

Fig. 3

Agreement between number of cases predicted and number observed (test period, Bland and Altman method). The average difference shows if one of our two methods of measurement tends to produce consistently lower or higher values than the other (Predicted number of cases tends to be lower than observed number, here the mean bias is − 13 cases). 95% of the differences between each pair of points are between Mean + 1.96SD and Mean - 1.96 SD (here 95% of the differences between predicted number of cases and observed number were comprised between − 124 and + 150 which are the “limits of agreement”)