• Damien Viglino, Aurelien Vesin, Stephane Ruckly, Xavier Morelli, Rémi Slama, Guillaume Debaty, Vincent Danel, Maxime Maignan, and Jean-François Timsit.
• University Grenoble Alps, Emergency Department and Mobile Intensive Care Unit, CHU Grenoble Alps, Grenoble, France. dviglino@chu-grenoble.fr.
• Scand J Trauma Resus. 2017 Aug 29; 25 (1): 86.

BackgroundVariations 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.MethodsA 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.ResultsThe 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).ConclusionsA large proportion of the variability of the medical emergency call center's case volume can be predicted using readily available covariates.

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