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

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.

目的：描述评估现有分诊模型在全危害灾难后对患者激增进行适当分诊的有效性的方法。 设计：利用从格兰尼特维尔氯气灾难受害者的医疗记录中提取的数据，开发了一种评估现有分诊模型的方法。 背景：2005年1月6日，一列载有三节液氯罐车的货运列车在南卡罗来纳州格兰尼特维尔市中心意外地被转到一条工业支线。列车随后撞上一辆停放的机车并脱轨。这导致其中一节氯气罐车破裂，立即释放了约60吨氯气。氯气渗透到这个有7000人口的城镇。 参与者：本研究关注在南卡罗来纳州接受紧急救治的受害者。 结果：通过我们的数据映射和决策树逻辑，作者成功地利用现有的提取临床数据来估计分诊类别，以供我们的研究使用。 结论：本文概述的方法显示了精心设计的二次分析方法在改进大规模伤亡研究方面的潜在用途。这些步骤可靠且可重复，并且可以很容易地扩展或应用于其他灾难数据集。