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Forecasting the Demand for Emergency Medical Services

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Title:Forecasting the Demand for Emergency Medical Services
Authors:Steins, Krisjanis
Matinrad, Niki
Granberg, Tobias
Keywords:Service Analytics
Decision Analytics, Mobile Services, and Service Science
Ambulance management, Emergency Medical Services, Forecasting, Statistical modeling
Date Issued:08 Jan 2019
Abstract:Accurate forecast of the demand for emergency medical services (EMS) can help in providing quick and efficient medical treatment and transportation of out-of-hospital patients. The aim of this research was to develop a forecasting model and investigate which factors are relevant to include in such model. The primary data used in this study was information about ambulance calls in three Swedish counties during the years 2013 and 2014. This information was processed, assigned to spatial grid zones and complemented with population and zone characteristics. A Zero-Inflated Poisson (ZIP) regression approach was then used to select significant factors and develop the forecasting model. The model was compared to the forecasting model that is currently incorporated in the EMS information system used by the ambulance dispatchers. The results show that the proposed model performs better than the existing one.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/59625
ISBN:978-0-9981331-2-6
DOI:10.24251/HICSS.2019.225
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Service Analytics


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