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

Abstract

Objective: 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.Design: A method was developed for evaluating currently available triage models using extracted data from medical records of the victims from the Graniteville chlorine disaster.Setting: 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.Participants: This research focuses on the victims who received emergency care in South Carolina.Results: 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.Conclusions: 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.