title: SIMEDIS: a Discrete-Event Simulation Model for Testing Responses to Mass Casualty Incidents.
abstract: It is recognized that the study of the disaster medical response (DMR) is a relatively new field. To date, there is no evidence-based literature that clearly defines the best medical response principles, concepts, structures and processes in a disaster setting. Much of what is known about the DMR results from descriptive studies and expert opinion. No experimental studies regarding the effects of DMR interventions on the health outcomes of disaster survivors have been carried out. Traditional analytic methods cannot fully capture the flow of disaster victims through a complex disaster medical response system (DMRS). Computer modelling and simulation enable to study and test operational assumptions in a virtual but controlled experimental environment. The SIMEDIS (Simulation for the assessment and optimization of medical disaster management) simulation model consists of 3 interacting components: the victim creation model, the victim monitoring model where the health state of each victim is monitored and adapted to the evolving clinical conditions of the victims, and the medical response model, where the victims interact with the environment and the resources at the disposal of the healthcare responders. Since the main aim of the DMR is to minimize as much as possible the mortality and morbidity of the survivors, we designed a victim-centred model in which the casualties pass through the different components and processes of a DMRS. The specificity of the SIMEDIS simulation model is the fact that the victim entities evolve in parallel through both the victim monitoring model and the medical response model. The interaction between both models is ensured through a time or medical intervention trigger. At each service point, a triage is performed together with a decision on the disposition of the victims regarding treatment and/or evacuation based on a priority code assigned to the victim and on the availability of resources at the service point. The aim of the case study is to implement the SIMEDIS model to the DMRS of an international airport and to test the medical response plan to an airplane crash simulation at the airport. In order to identify good response options, the model then was used to study the effect of a number of interventional factors on the performance of the DMRS. Our study reflects the potential of SIMEDIS to model complex systems, to test different aspects of DMR, and to be used as a tool in experimental research that might make a substantial contribution to provide the evidence base for the effectiveness and efficiency of disaster medical management.
authors.affiliation: Research Group on Emergency and Disaster Medicine, Vrije Universiteit Brussel, Brussels, Belgium. michel.debacker@vub.ac.be.
authors.forename: Michel
authors.identifier: http://orcid.org/0000-0003-1901-6865
authors.initials: M
authors.lastname: Debacker
authors.affiliation: Department of Mathematics, Royal Military Academy, Brussels, Belgium.
authors.forename: Filip
authors.initials: F
authors.lastname: Van Utterbeeck
authors.affiliation: Department of Mathematics, Royal Military Academy, Brussels, Belgium.
authors.forename: Christophe
authors.initials: C
authors.lastname: Ullrich
authors.affiliation: COMOPSMED/B Spec Sp, Medical Component, Belgian Armed Forces, Brussels, Belgium.
authors.forename: Erwin
authors.initials: E
authors.lastname: Dhondt
authors.affiliation: Research Group on Emergency and Disaster Medicine, Vrije Universiteit Brussel, Brussels, Belgium.
authors.forename: Ives
authors.initials: I
authors.lastname: Hubloue
mesh_terms: D003198:Computer Simulation; D004189:Disaster Planning / Q000458:organization & administration*; D004632:Emergency Medical Services / Q000458:organization & administration*; D006801:Humans; D054527:Mass Casualty Incidents; D008962:Models, Theoretical; D008991:Monitoring, Physiologic; D016019:Survival Analysis; D014218:Triage / Q000458:organization & administration
keywords: Disaster medical response; Disaster research; Discrete-event simulation; Pre-hospital disaster management; Victim modelling