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dc.contributor.authorCulley, Joan Marieen
dc.contributor.authorTavakoli, Abbasen
dc.contributor.authorSvendsen, Erik R.en
dc.contributor.authorCraig, Jean B.en
dc.date.accessioned2013-10-22T20:26:05Z
dc.date.available2013-10-22T20:26:05Z
dc.date.issued2013-10-22
dc.identifier.urihttp://hdl.handle.net/10755/303930
dc.description<p>24th International Nursing Research Congress Theme: Bridge the Gap Between Research and Practice Through Collaboration. Held at the Hilton Prague Hotel.</p>en
dc.description.abstract<p>Session presented on: Wednesday, July 24, 2013:</p> <p><strong>Purpose: </strong>On January 6, 2005, a freight train carrying three tanker cars of liquid chlorine was inadvertently switched onto an industrial spur in central Graniteville, South Carolina. The train then crashed into a parked locomotive and derailed. This caused one of the chlorine tankers to rupture and immediately release ~60 tons of chlorine. Chlorine gas infiltrated the town with a population of 7,000. This research focuses on the victims who received emergency care in South Carolina. The objective of presentation is to describe the methods of evaluating currently available triage models for their efficacy in appropriately triaging the surge of patients after an all-hazards disaster.</p> <p><strong>Methods:</strong> We developed a method for evaluating currently available triage models using extracted data from medical records of the victims from the Graniteville chlorine disaster.</p> <p><strong>Results: </strong>With our data mapping and decision tree logic, we were successful in employing the available extracted clinical data to estimate triage categories for use in triage effectiveness research.</p> <p><strong>Conclusion:</strong> The methodology outlined in this paper can be used to assess the performance of triage models after a disaster. The steps are reliable and repeatable and can easily be extended or applied to other disaster datasets.</p>en
dc.formatText-based Documenten
dc.language.isoenen
dc.subjectTriage Effectiveness Researchen
dc.subjectAll Hazard Mass Casualty Triageen
dc.subjectData Mining Techniquesen
dc.titleGleaning data from disaster: A hospital-based data mining method to studying all-hazard triage after a chemical disasteren
dc.typePresentationen
dc.rights.holder<p> All rights reserved by the author(s) and/or publisher(s) listed in this item record unless relinquished in whole or part by a rights notation or a Creative Commons License present in this item record. </p><p> All permission requests should be directed accordingly and not to the Sigma Repository. </p><p> All submitting authors or publishers have affirmed that when using material in their work where they do not own copyright, they have obtained permission of the copyright holder prior to submission and the rights holder has been acknowledged as necessary. </p>
dc.description.note<p>Items submitted to a conference/event were evaluated/peer-reviewed at the time of abstract submission to the event. No other peer-review was provided prior to submission to the Henderson Repository.</p>
dc.type.categoryFull-texten
dc.evidence.levelN/Aen
dc.research.approachN/Aen
dc.contributor.departmentAlpha Xien
dc.author.detailsJoan Marie Culley, PhD, MPH, MS, RN, CWOCN; Abbas Tavakoli, DrPH, MPH, ME; Erik R. Svendsen, PhD, MS, BS; Jean B. Craig, PhD, MS, BSen
dc.conference.name24th International Nursing Research Congressen
dc.conference.hostSigma Theta Tau Internationalen
dc.conference.locationPrague, Czech Republicen
dc.date.conferenceyear2013
dc.description.reviewtypeAbstract Review Only: Reviewed by Event Hosten
dc.description.acquisitionProxy-submissionen


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