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: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:BACKGROUND:Chemical 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. OBJECTIVE:This 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. METHODS:Over 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. RESULTS:Of 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. CONCLUSIONS:The 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.

Project description:OBJECTIVE:Mis-triage may have serious consequences for patients in mass casualty incidents (MCI) at sea. The purpose of this study was to assess outcome, reliability and validity of an analogue and a digital recording system for triage of a MCI at sea. METHODS:The study based on a triage exercise conducted with a cross-over-design. Forty-eight volunteers were presented a fictional MCI with 50 cases. The volunteers were randomly assigned to start with the analogue (Group A, starting with the analogue followed by the digital system) or digital system (Group B, starting with the digital followed by the analogue system). Triage score distribution and agreement between the triage methods and a predefined standard were reported. Reliability was analysed using Cronbach's Alpha and Cohen's Kappa. Validity was measured through sensitivity, specificity and predictive value. Treatment, period and carry-over-effects were analysed using a linear mixed-effects model. RESULTS:The number of patients triaged (total: n = 3545) with the analogue system (n = 1914; 79.75%) was significantly higher (p = 0.001) than with the digital system (n = 1631; 67.96%). A trend towards a higher percentage of correct triages with the digital system was observed (p = 0.282). Ratio of under-triage was significantly smaller with the digital system (p = 0.001). Validity measured with Cronbach's Alpha and Cohen's Kappa was higher with the digital system. So was sensitivity (category; green: 80.67%, yellow: 73.24%, red: 83.54%; analogue: green: 93.28%, yellow: 82.36%, red: 94.04%) and specificity of the digital system (green: 78.07%, yellow: 63.75%, red: 66.25%; analogue: green: 85.50%, yellow: 79.88%, red: 91.50%). Comparing the predictive values and accuracy, the digital system showed higher scores than the analogue system. No significant patterns of carry-over-effects were observed. CONCLUSIONS:Significant differences were found for the number of triages comparing the analogue and digital recording system. The digital system has a slightly higher reliability and validity than the analogue triage system.

Project description:BackgroundIn the United States, asthma is the most common chronic disease of childhood across all socioeconomic classes and is the most frequent cause of hospitalization among children. Asthma exacerbations have been associated with exposure to residential indoor environmental stressors such as allergens and air pollutants as well as numerous additional factors. Simulation modeling is a valuable tool that can be used to evaluate interventions for complex multifactorial diseases such as asthma but in spite of its flexibility and applicability, modeling applications in either environmental exposures or asthma have been limited to date.MethodsWe designed a discrete event simulation model to study the effect of environmental factors on asthma exacerbations in school-age children living in low-income multi-family housing. Model outcomes include asthma symptoms, medication use, hospitalizations, and emergency room visits. Environmental factors were linked to percent predicted forced expiratory volume in 1 second (FEV1%), which in turn was linked to risk equations for each outcome. Exposures affecting FEV1% included indoor and outdoor sources of NO2 and PM2.5, cockroach allergen, and dampness as a proxy for mold.ResultsModel design parameters and equations are described in detail. We evaluated the model by simulating 50,000 children over 10 years and showed that pollutant concentrations and health outcome rates are comparable to values reported in the literature. In an application example, we simulated what would happen if the kitchen and bathroom exhaust fans were improved for the entire cohort, and showed reductions in pollutant concentrations and healthcare utilization rates.ConclusionsWe describe the design and evaluation of a discrete event simulation model of pediatric asthma for children living in low-income multi-family housing. Our model simulates the effect of environmental factors (combustion pollutants and allergens), medication compliance, seasonality, and medical history on asthma outcomes (symptom-days, medication use, hospitalizations, and emergency room visits). The model can be used to evaluate building interventions and green building construction practices on pollutant concentrations, energy savings, and asthma healthcare utilization costs, and demonstrates the value of a simulation approach for studying complex diseases such as asthma.

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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