A Tool-Based Framework to Assess and Challenge the Responsiveness of Emergency Call Centers

被引:4
作者
Petitdemange, Eva [1 ]
Fontanili, Franck [1 ]
Lamine, Elyes [1 ]
Lauras, Matthieu [1 ]
Okongwu, Uche [2 ]
机构
[1] IMT Mines Albi, CGI, F-81000 Albi, France
[2] Toulouse Business Sch, F-31068 Toulouse, France
关键词
Quality of service; Emergency services; Hospitals; Standards; Organizations; Data mining; diagnosis; discrete event simulation (DES); emergency call centers (ECCs); process mining; responsiveness; MEDICAL-SERVICES; MODELS;
D O I
10.1109/TEM.2019.2954013
中图分类号
F [经济];
学科分类号
02 ;
摘要
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.
引用
收藏
页码:568 / 581
页数:14
相关论文
共 43 条
[21]   Call-type dependence in multiskill call centers [J].
Jaoua, Amel ;
L'Ecuyer, Pierre ;
Delorme, Louis .
SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL, 2013, 89 (06) :722-734
[22]  
JOHNSON NL, 1949, BIOMETRIKA, V36, P297, DOI 10.1093/biomet/36.3-4.297
[23]   Queueing models of call centers: An introduction [J].
Koole, G ;
Mandelbaum, A .
ANNALS OF OPERATIONS RESEARCH, 2002, 113 (1-4) :41-59
[24]  
Lafond M., 2012, PUBLIC SAFETY COMMUN, P30
[25]   Improving the Management of an Emergency Call Service by Combining Process Mining and Discrete Event Simulation Approaches [J].
Lamine, Elyes ;
Fontanili, Franck ;
Di Mascolo, Maria ;
Pingaud, Herve .
RISKS AND RESILIENCE OF COLLABORATIVE NETWORKS, 2015, 463 :535-546
[26]  
Mason AJ, 2013, HDB HEALTHCARE OPERA, V184, P289
[27]   Determining Appropriate Staffing Adjustments in a Call Center Staff Group [J].
Passmore, Curtis M. ;
Zhan, Justin .
2013 ASE/IEEE INTERNATIONAL CONFERENCE ON SOCIAL COMPUTING (SOCIALCOM), 2013, :1046-1053
[28]  
Penverne Y., 2017, EUR J EMERG MED OFF
[29]   Evaluating arrival rate uncertainty in call centers [J].
Robbins, Thomas R. ;
Medeiros, D. J. ;
Dum, Paul .
PROCEEDINGS OF THE 2006 WINTER SIMULATION CONFERENCE, VOLS 1-5, 2006, :2180-+
[30]   DOES THE ERLANG C MODEL FIT IN REAL CALL CENTERS? [J].
Robbins, Thomas R. ;
Medeiros, D. J. ;
Harrison, Terry P. .
PROCEEDINGS OF THE 2010 WINTER SIMULATION CONFERENCE, 2010, :2853-2864