To mitigate the "surge" of casualties into a healthcare facility after a mass casualty incident (MCI), emergency responders and hospital personnel use triage to rapidly assess patients and prioritize their care with the goal of saving as many lives a possible. Three main challenges are encountered in the treatment of victims of toxic inhalation hazard chemicals (TIH chemicals) MCIs: 1) quickly identifying that a MCI has occurred, 2) rapidly detecting the chemical involved, and 3) identifying, triaging and processing those exposed accurately, precisely and efficiently to improve patient outcomes. The US produces and transports nearly 1.7 million railcars of hazardous materials each year. A spill of such chemicals as they move through a city could injure or kill hundreds of thousands of people. However, the proposed national guideline for existing mass casualty triage does not fully account for events that include chemicals. Findings from the our previous NIH/NLM R21LM10833 funded study of the Graniteville, SC chlorine disaster, found that: 1) The Emergency Severity Index (ESI) hospital triage system had poor predictive quality for victims exposed to chlorine;2) the surge of victims into the ED came before any chemical exposure information was available, leading to confusion and difficult victim processing;and there exists more sensitive triage assessments (e.g., oxygen saturation measured by pulse oximetry [SpO2]). Currently, there are no informatics tools to rapidly identify the early stages of a surge, process victims efficiently, nor make triage recommendations for TIH chemicals or any other MCIs. We propose a new ED Informatics Computational Tool (EDICT) that incorporates a new triage algorithm (TIH Chemical Triage Algorithm), and integrates the NLM Wireless Information System for Emergency Responders (WISER) system with real disaster data to more accurately, precisely and efficiently triage ED patients, using a chemical MCI as a first step. SpO2 monitoring will be used in the TIH Chemical Triage Algorithm to better assess injury latency common with TIH chemical exposures. Computer-based informatics solutions that improve early identification, processing, and triage for patients admitted to the ED following a MCI will enhance the science of disaster informatics. Using EDICT in routine ED practice could potentially lead to a breakthrough in the general use of informatics technology to dramatically improve the way patients are processed in EDs. A flexible, robust and scalable informatics computational solution has the potential for broader applications in other types of MCIs (e.g., foodborne and communicable disease outbreaks), as well as day-to-day use in EDs. This study is the first step to developing new ED informatics tools, which can change all ED patient processing.

Public Health Relevance

Three main challenges encountered in the treatment of victims of toxic inhalation (TIH) chemical (eg, chlorine) mass casualty incidents (MCIs) are: 1) quickly identifying that a MCI has occurred, 2) rapidly detecting the chemical involved, and 3) identifying, triaging and processing those exposed accurately, precisely and efficiently to improve patient outcomes. We will develop and test a new ED Informatics Computational Tool (EDICT) that incorporates a new triage algorithm (TIH Chemical Triage Algorithm), and integrates the NLM Wireless Information System for Emergency Responders (WISER) system with real disaster data to more accurately, precisely and efficiently triage ED patients, using a chemical MCI as a first step. A flexible, robust and scalable informatics computational solution has the potential for broader applications in other types of MCIs (eg, foodborne and communicable disease outbreaks), as well as day-to-day use in EDs.

Agency
National Institute of Health (NIH)
Type
Research Project (R01)
Project #
1R01LM011648-01A1
Application #
8758881
Study Section
Biomedical Library and Informatics Review Committee (BLR)
Program Officer
Sim, Hua-Chuan
Project Start
Project End
Budget Start
Budget End
Support Year
1
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of South Carolina at Columbia
Department
None
Type
Schools of Nursing
DUNS #
City
Columbia
State
SC
Country
United States
Zip Code
29208