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

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...

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Autores principales: Viglino, Damien, Vesin, Aurelien, Ruckly, Stephane, Morelli, Xavier, Slama, Rémi, Debaty, Guillaume, Danel, Vincent, Maignan, Maxime, Timsit, Jean-François
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5576313/
https://www.ncbi.nlm.nih.gov/pubmed/28851446
http://dx.doi.org/10.1186/s13049-017-0430-9
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author Viglino, Damien
Vesin, Aurelien
Ruckly, Stephane
Morelli, Xavier
Slama, Rémi
Debaty, Guillaume
Danel, Vincent
Maignan, Maxime
Timsit, Jean-François
author_facet Viglino, Damien
Vesin, Aurelien
Ruckly, Stephane
Morelli, Xavier
Slama, Rémi
Debaty, Guillaume
Danel, Vincent
Maignan, Maxime
Timsit, Jean-François
author_sort Viglino, Damien
collection PubMed
description 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13049-017-0430-9) contains supplementary material, which is available to authorized users.
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spelling pubmed-55763132017-08-30 Daily volume of cases in emergency call centers: construction and validation of a predictive model Viglino, Damien Vesin, Aurelien Ruckly, Stephane Morelli, Xavier Slama, Rémi Debaty, Guillaume Danel, Vincent Maignan, Maxime Timsit, Jean-François Scand J Trauma Resusc Emerg Med Original Research 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13049-017-0430-9) contains supplementary material, which is available to authorized users. BioMed Central 2017-08-29 /pmc/articles/PMC5576313/ /pubmed/28851446 http://dx.doi.org/10.1186/s13049-017-0430-9 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Original Research
Viglino, Damien
Vesin, Aurelien
Ruckly, Stephane
Morelli, Xavier
Slama, Rémi
Debaty, Guillaume
Danel, Vincent
Maignan, Maxime
Timsit, Jean-François
Daily volume of cases in emergency call centers: construction and validation of a predictive model
title Daily volume of cases in emergency call centers: construction and validation of a predictive model
title_full Daily volume of cases in emergency call centers: construction and validation of a predictive model
title_fullStr Daily volume of cases in emergency call centers: construction and validation of a predictive model
title_full_unstemmed Daily volume of cases in emergency call centers: construction and validation of a predictive model
title_short Daily volume of cases in emergency call centers: construction and validation of a predictive model
title_sort daily volume of cases in emergency call centers: construction and validation of a predictive model
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5576313/
https://www.ncbi.nlm.nih.gov/pubmed/28851446
http://dx.doi.org/10.1186/s13049-017-0430-9
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