Dataset Information

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.

SUBMITTER: Debacker M

PROVIDER: S-EPMC5069323 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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SIMEDIS: a Discrete-Event Simulation Model for Testing Responses to Mass Casualty Incidents.

Debacker Michel M Van Utterbeeck Filip F Ullrich Christophe C Dhondt Erwin E Hubloue Ives I

Journal of medical systems 20161018 12

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 an ...[more]

PMID: 27757716

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Project description:BackgroundIn the event of a mass casualty incident (MCI), the situation-related shortage of medical resources does not end when the patients are transported from the scene of the incident. Consequently, an initial triage is required in the receiving hospitals. In the first step, the aim of this study was to create a reference patient vignette set with defined triage categories. This allowed a computer-aided evaluation of the diagnostic quality of triage algorithms for MCI situations in the second step.MethodsA total of 250 case vignettes validated in practice were entered into a multistage evaluation process by initially 6 and later 36 triage experts. This algorithm-independent expert evaluation of all vignettes-served as the gold standard for analyzing the diagnostic quality of the following triage algorithms: Manchester triage system (MTS module MCI), emergency severity index (ESI), Berlin triage algorithm (BER), the prehospital algorithms PRIOR and mSTaRT, and two project algorithms from a cooperation between the Federal Office of Civil Protection and Disaster Assistance (BBK) and the Hashemite Kingdom of Jordan-intrahospital Jordanian-German project algorithm (JorD) and prehospital triage algorithm (PETRA). Each patient vignette underwent computerized triage through all specified algorithms to obtain comparative test quality outcomes.ResultsOf the original 250 vignettes, a triage reference database of 210 patient vignettes was validated independently of the algorithms. These formed the gold standard for comparison of the triage algorithms analyzed. Sensitivities for intrahospital detection of patients in triage category T1 ranged from 1.0 (BER, JorD, PRIOR) to 0.57 (MCI module MTS). Specificities ranged from 0.99 (MTS and PETRA) to 0.67 (PRIOR). Considering Youden's index, BER (0.89) and JorD (0.88) had the best overall performance for detecting patients in triage category T1. Overtriage was most likely with PRIOR, and undertriage with the MCI module of MTS. Up to a decision for category T1, the algorithms require the following numbers of steps given as the median and interquartile range (IQR): ESI 1 (1-2), JorD 1 (1-4), PRIOR 3 (2-4), BER 3 (2-6), mSTaRT 3 (3-5), MTS 4 (4-5) and PETRA 6 (6-8). For the T2 and T3 categories the number of steps until a decision and the test quality of the algorithms are positively interrelated.ConclusionIn the present study, transferability of preclinical algorithm-based primary triage results to clinical algorithm-based secondary triage results was demonstrated. The highest diagnostic quality for secondary triage was provided by the Berlin triage algorithm, followed by the Jordanian-German project algorithm for hospitals, which, however, also require the most algorithm steps until a decision.

Project description:Introduction and objectiveScientific reporting on major incidents, mass-casualty incidents (MCIs), and disasters is challenging and made difficult by the nature of the medical response. Many obstacles might explain why there are few and primarily non-heterogenous published articles available. This study examines the process of scientific reporting through first-hand experiences from authors of published reports. It aims to identify learning points and challenges that are important to address to mitigate and improve scientific reporting after major incidents.MethodsThis was a qualitative study design using semi-structured interviews. Participants were selected based on a comprehensive literature search. Ten researchers, who had published reports on major incidents, MCIs, or disasters from 2013-2018 were included, of both genders, from eight countries on three continents. The researchers reported on large fires, terrorist attacks, shootings, complex road accidents, transportation accidents, and earthquakes.ResultsThe interview was themed around initiation, workload, data collection, guidelines/templates, and motivation factors for reporting. The most challenging aspects of the reporting process proved to be a lack of dedicated time, difficulties concerning data collection, and structuring the report. Most researchers had no prior experience in reporting on major incidents. Guidelines and templates were often chosen based on how easily accessible and user-friendly they were.Conclusion and relevanceThere are few articles presenting first-hand experience from the process of scientific reporting on major incidents, MCIs, and disasters. This study presents motivation factors, challenges during reporting, and factors that affected the researchers' choice of reporting tools such as guidelines and templates. This study shows that the structural tools available for gathering data and writing scientific reports need to be more widely promoted to improve systematic reporting in Emergency and Disaster Medicine. Through gathering, comparing, and analyzing data, knowledge can be acquired to strengthen and improve responses to future major incidents. This study indicates that transparency and willingness to share information are requisite for forming a successful scientific report.

Project description:BackgroundChemical exposures pose a significant threat to life. A rapid assessment by first responders and emergency nurses is required to reduce death and disability. Currently, no informatics tools exist to process victims of chemical exposures efficiently. The surge of patients into a hospital emergency department during a mass casualty incident creates additional stress on an already overburdened system, potentially placing patients at risk and challenging staff to process patients for appropriate care and treatment efficacy. Traditional emergency department triage models are oversimplified during highly stressed mass casualty incident scenarios in which there is little margin for error. Emerging mobile technology could alleviate the burden placed on nurses by allowing the freedom to move about the emergency department and stay connected to a decision support system.ObjectiveThis study aims to present and evaluate a new mobile tool for assisting emergency department personnel in patient management and triage during a chemical mass casualty incident.MethodsOver 500 volunteer nurses, students, and first responders were recruited for a study involving a simulated chemical mass casualty incident. During the exercise, a mobile application was used to collect patient data through a kiosk system. Nurses also received tablets where they could review patient information and choose recommendations from a decision support system. Data collected was analyzed on the efficiency of the app to obtain patient data and on nurse agreement with the decision support system.ResultsOf the 296 participants, 96.3% (288/296) of the patients completed the kiosk system with an average time of 3 minutes, 22 seconds. Average time to complete the entire triage process was 5 minutes, 34 seconds. Analysis of the data also showed strong agreement among nurses regarding the app's decision support system. Overall, nurses agreed with the system 91.6% (262/286) of the time when it came to choose an exposure level and 84.3% (241/286) of the time when selecting an action.ConclusionsThe app reliably demonstrated the ability to collect patient data through a self-service kiosk system thus reducing the burden on hospital resources. Also, the mobile technology allowed nurses the freedom to triage patients on the go while staying connected to a decision support system in which they felt would give reliable recommendations.

Dataset Information

SIMEDIS: a Discrete-Event Simulation Model for Testing Responses to Mass Casualty Incidents.

Publications

SIMEDIS: a Discrete-Event Simulation Model for Testing Responses to Mass Casualty Incidents.

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