DC ElementWertSprache
dc.contributor.authorTolg, Boris-
dc.contributor.authorLorenz, Jürgen-
dc.date.accessioned2024-02-29T11:49:14Z-
dc.date.available2024-02-29T11:49:14Z-
dc.date.issued2020-10-09-
dc.identifier.issn2059-0628en_US
dc.identifier.urihttp://hdl.handle.net/20.500.12738/14947-
dc.description.abstractBackground: 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.en
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofAdvances in simulationen_US
dc.subjectGroup recognitionen_US
dc.subjectMass casualty incidenten_US
dc.subjectMovement patternsen_US
dc.subjectSimulationen_US
dc.subject.ddc004: Informatiken_US
dc.titleAn analysis of movement patterns in mass casualty incident simulationsen
dc.typeArticleen_US
dc.description.versionPeerRevieweden_US
tuhh.container.issue1en_US
tuhh.container.volume5en_US
tuhh.oai.showtrueen_US
tuhh.publication.instituteFakultät Life Sciencesen_US
tuhh.publication.instituteDepartment Medizintechniken_US
tuhh.publication.instituteDepartment Umwelttechniken_US
tuhh.publisher.doi10.1186/s41077-020-00147-9-
tuhh.type.opus(wissenschaftlicher) Artikel-
dc.rights.cchttps://creativecommons.org/licenses/by/4.0/en_US
dc.type.casraiJournal Article-
dc.type.diniarticle-
dc.type.driverarticle-
dc.type.statusinfo:eu-repo/semantics/publishedVersionen_US
dcterms.DCMITypeText-
tuhh.container.articlenumber27-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.fulltextNo Fulltext-
item.grantfulltextnone-
item.openairetypeArticle-
item.creatorGNDTolg, Boris-
item.creatorGNDLorenz, Jürgen-
item.languageiso639-1en-
item.creatorOrcidTolg, Boris-
item.creatorOrcidLorenz, Jürgen-
item.cerifentitytypePublications-
crisitem.author.deptDepartment Umwelttechnik-
crisitem.author.deptDepartment Medizintechnik-
crisitem.author.parentorgFakultät Life Sciences-
crisitem.author.parentorgFakultät Life Sciences-
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