Gleaning data from disaster: A hospital-based data mining method to study all-hazard triage after a chemical disaster

Abstract

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. A method was developed for evaluating currently available triage models using extracted data from medical records of the victims from the Graniteville chlorine disaster. On January 6, 2005, a freight train carrying three tanker cars of liquid chlorine was inadvertently switched onto an industrial spur in central Graniteville, SC. 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. With our data mapping and decision tree logic, the authors were successful in using the available extracted clinical data to estimate triage categories for use in our study. The methodology outlined in this article shows the potential use of well-designed secondary analysis methods to improve mass casualty research. The steps are reliable and repeatable and can easily be extended or applied to other disaster datasets.