Citation

Petitdemange E, Fontanili F, Lamine E, Lauras M, Okongwu U. IEEE Trans. Eng. Manag. 2020; 67(3): 568-581.

Copyright

(Copyright © 2020, Institute of Electrical and Electronics Engineers)

DOI

10.1109/TEM.2019.2954013

PMID

unavailable

Abstract

Emergency call centers (ECCs) are upstream of the prehospital emergency medical system and the life of many people depends on their effectiveness and responsiveness. This notwithstanding, the way their operations are organized and managed differs from one place to another. Also, depending on the number of incoming calls and available resources, they can operate differently. In the face of these heterogeneous situations, some ECCs do not always meet the expected performance levels: people still wait for too long before their call is answered. Moreover, they may have difficulties in managing an important upsurge of calls, especially in periods of crisis. Therefore, to support ECCs' organizational improvement steps, this article aims to develop a tool-based framework that would enable to make clear and objective diagnoses, especially as regards responsiveness. Our proposal allows considering both nominal (normal days) and exceptional (crisis days) demands. It is based on data science, process mining, and discrete event simulation tools. By experimenting it on a French real case, the results show that such a tool-based framework can be very valuable for improving the performance of ECC organizational setups in both normal and disrupted situations.

Language: en

Keywords

Data mining; diagnosis; discrete event simulation (DES); emergency call centers (ECCs); Emergency services; Hospitals; Organizations; process mining; Quality of service; responsiveness; Standards