Following a mass casualty incident (MCI), a healthcare facility will experience a surge of patients--many of them quickly arriving to the Emergency Department (ED) via their personal vehicles. Emergency responders and hospital personnel will use triage to rapidly assess patients and prioritize their care with the goal of saving as many lives as possible. Following a chemical exposure, local EDs may receive a surge of victims before any chemical identification information is available, thus complicating treatment decisions. Small communities are additionally challenged because they are ill prepared to manage any surge of patients, regardless of the cause. Alongside our partners in the College of Nursing, our lab is currently developing a robust computer-based informatics tool to improve early chemical identification and to enhance patient processing and triage in the ED following an MCI. Our continuous triage process will monitor and aggregate data across all patients to provide ongoing situational awareness. Evolving wireless physiologic and mobile sensing technology and associated signal analysis prototypes will be explored and incorporated as appropriate for the ED MCI application. Our current development is on Android based systems.
Nicholas Boltin, Daniel Vu, Bethany Janos, Alyssa Shofner, Joan Culley, Homayoun Valafar, An AI Model for Rapid and Accurate Identification of Chemical Agents in Mass Casualty Incidents, Proceedings of the International Conference on Health Informatics and Medical Systems (HIMS), July 2016, Las Vegas, NV