Gleaning Data from Disaster: A Hospital-Based Data Mining Method to Studying All-Hazard Triage after a Chemical Disaster

Wednesday, 24 July 2013: 1:50 PM

Joan Marie Culley, PhD, MPH, MS, RN, CWOCN1
Jean B. Craig, PhD, MS, BS2
Abbas Tavakoli, DrPH, MPH, ME1
Erik R. Svendsen, PhD, MS, BS3
(1)College of Nursing, University of South Carolina, Columbia, SC
(2)Office of Biomedical Informatics Services, Medical University of South Carolina, Charleston, SC
(3)Department of Global Environmental Health Sciences, Tulane University School of Public Health and Tropical Medicine,, New Orleans, LA

Learning Objective 1: The learner will be able to describe the use of data mining methods to evaluate mass casualty triage models.

Learning Objective 2: The learner will be able to describe the role of secondary data analysis methods for use in triage effectiveness research.


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.


We developed a method for evaluating currently available triage models using extracted data from medical records of the victims from the Graniteville chlorine disaster.


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.


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.