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An analysis of movement patterns in mass casualty incident simulations.

ABSTRACT: Background:Mass casualty incidents (MCI) such as train or bus crashes, explosions, collapses of buildings, or terrorist attacks result in rescue teams facing many victims and in huge challenges for hospitals. Simulations are performed to optimize preparedness for MCI. To maximize the benefits of MCI simulations, it is important to collect large amounts of information. However, a clear concept and standardization of a data-driven post-exercise evaluation and debriefing are currently lacking. Methods:GPS data loggers were used to track the trajectories of patients, medics, and paramedics in two simulated MCI scenarios using real human actors. The distribution of patients over the treatment area and their time of arrival at the hospital were estimated to provide information on the quality of triage and for debriefing purposes. Results:The results show the order in which patients have been treated and the time for the individual arrivals as an indicator for the triage performance. The distribution of patients at the accident area suggested initial confusion and unclear orders for the placement of patients with different grades of injury that can be used for post-exercise debriefing. The dynamics of movement directions allowed to detect group behavior during different phases of the MCI. Conclusions:Results indicate that GPS data loggers can be used to collect precise information about the trajectories of patients and rescue teams at an MCI simulation without interfering with the realism of the simulation. The exact sequence of the deliverance of patients of different triage categories to their appropriate destinations can be used to evaluate team performance for post-exercise debriefing. Future MCI simulations are planned to validate the use of GPS loggers by providing "hot-debrief" immediately after the MCI simulation and to explore ways in which group detection can provide relevant information for post-exercise evaluations. Trial registration:Not applicable.

SUBMITTER: Tolg B

PROVIDER: S-EPMC7547474 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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An analysis of movement patterns in mass casualty incident simulations.

Tolg Boris B Lorenz Juergen J

Advances in simulation (London, England) 20201009

<h4>Background</h4>Mass casualty incidents (MCI) such as train or bus crashes, explosions, collapses of buildings, or terrorist attacks result in rescue teams facing many victims and in huge challenges for hospitals. Simulations are performed to optimize preparedness for MCI. To maximize the benefits of MCI simulations, it is important to collect large amounts of information. However, a clear concept and standardization of a data-driven post-exercise evaluation and debriefing are currently lacki ...[more]

PMID: 33062308

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Project description:BackgroundOver the past several decades, naturally occurring and man-made mass casualty incidents (MCIs) have increased in frequency and number worldwide. To test the impact of such events on medical resources, simulations can provide a safe, controlled setting while replicating the chaotic environment typical of an actual disaster. A standardized method to collect and analyze data from mass casualty exercises is needed to assess preparedness and performance of the health care staff involved.ObjectiveIn this study, we aimed to assess the feasibility of using wearable proximity sensors to measure proximity events during an MCI simulation. In the first instance, our objective was to demonstrate how proximity sensors can collect spatial and temporal information about the interactions between medical staff and patients during an MCI exercise in a quasi-autonomous way. In addition, we assessed how the deployment of this technology could help improve future simulations by analyzing the flow of patients in the hospital.MethodsData were obtained and collected through the deployment of wearable proximity sensors during an MCI functional exercise. The scenario included 2 areas: the accident site and the Advanced Medical Post, and the exercise lasted 3 hours. A total of 238 participants were involved in the exercise and classified in categories according to their role: 14 medical doctors, 16 nurses, 134 victims, 47 Emergency Medical Services staff members, and 27 health care assistants and other hospital support staff. Each victim was assigned a score related to the severity of his/her injury. Each participant wore a proximity sensor, and in addition, 30 fixed devices were placed in the field hospital.ResultsThe contact networks show a heterogeneous distribution of the cumulative time spent in proximity by the participants. We obtained contact matrices based on the cumulative time spent in proximity between the victims and rescuers. Our results showed that the time spent in proximity by the health care teams with the victims is related to the severity of the patient's injury. The analysis of patients' flow showed that the presence of patients in the rooms of the hospital is consistent with the triage code and diagnosis, and no obvious bottlenecks were found.ConclusionsOur study shows the feasibility of the use of wearable sensors for tracking close contacts among individuals during an MCI simulation. It represents, to our knowledge, the first example of unsupervised data collection-ie, without the need for the involvement of observers, which could compromise the realism of the exercise-of face-to-face contacts during an MCI exercise. Moreover, by permitting detailed data collection about the simulation, such as data related to the flow of patients in the hospital, such deployment provides highly relevant input for the improvement of MCI resource allocation and management.

Project description:OBJECTIVES:To assess public attitudes towards data sharing to facilitate a mental health screening programme for people caught up in a mass casualty incident. DESIGN:Two, identical, cross-sectional, online surveys, using quotas to ensure demographic representativeness of people aged 18-65?years in England. Participants were randomly allocated to consider a scenario in which they witness a terrorism-related radiation incident or mass shooting, after which a police officer records their contact details. SETTING:Participants were drawn from an online panel maintained by a market research company. Surveys were conducted before and immediately after a series of terrorist attacks and a large tower block fire occurred in England. PARTICIPANTS:One thousand people aged 18-65 years participated in each survey. MAIN OUTCOME MEASURES:Three questions asking participants if it would be acceptable for police to share their contact details, without asking first, with 'a health-related government organisation, so they can send you a questionnaire to find out if you might benefit from extra care or support', 'a specialist NHS team, to provide you with information about ways to get support for any physical or mental health issues' and 'your GP, so they can check how you are doing'. RESULTS:A minority of participants reported that it would be definitely not acceptable for their details to be shared with the government organisation (n=259, 13.0%), the National Health Service (NHS) (n=141, 7.1%) and their general practitioner (GP) (n=166, 8.3%). There was a small, but significant increase in acceptability for the radiation incident compared with the mass shooting. No major differences were observed between the preincident and postincident surveys. CONCLUSIONS:Although most people believe it is acceptable for their details to be shared in order to facilitate a mental health response to a major incident, care must be taken to communicate with those affected about how their information will be used.

Project description:BackgroundIn a mass casualty situation, medical personnel must rapidly assess and prioritize patients for treatment and transport. Triage is an important tool for medical management in disaster situations. Lack of common international and Swedish triage guidelines could lead to confusion. Attending the Advanced Trauma Life Support (ATLS) provider course is becoming compulsory in the northern part of Europe. The aim of the ATLS guidelines is provision of effective management of single critically injured patients, not mass casualties incidents. However, the use of the ABCDE algorithms from ATLS, has been proposed to be valuable, even in a disaster environment. The objective for this study was to determine whether the mnemonic ABCDE as instructed in the ATLS provider course, affects the ability of Swedish physician's to correctly triage patients in a simulated mass casualty incident.MethodsThe study group included 169 ATLS provider students from 10 courses and course sites in Sweden; 153 students filled in an anonymous test just before the course and just after the course. The tests contained 3 questions based on overall priority. The assignment was to triage 15 hypothetical patients who had been involved in a bus crash. Triage was performed according to the ABCDE algorithm. In the triage, the ATLS students used a colour-coded algorithm with red for priority 1, yellow for priority 2, green for priority 3 and black for dead. The students were instructed to identify and prioritize 3 of the most critically injured patients, who should be the first to leave the scene. The same test was used before and after the course.ResultsThe triage section of the test was completed by 142 of the 169 participants both before and after the course. The results indicate that there was no significant difference in triage knowledge among Swedish physicians who attended the ATLS provider course. The results also showed that Swedish physicians have little experience of real mass casualty incidents and exercises.ConclusionThe mnemonic ABCDE doesn't significantly affect the ability of triage among Swedish physicians. Actions to increase Swedish physicians' knowledge of triage, within the ATLS context or separately, are warranted.

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An analysis of movement patterns in mass casualty incident simulations.

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An analysis of movement patterns in mass casualty incident simulations.

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