Abstract— For years, emergency medical response communities have relied upon paper triage tags, clipboards of notes, and voice communications to share information during medical emergencies. This workflow, however, has proven labor intensive, time consuming, and prone to human error [1]. In collaboration with three EMS groups in the Washington, DC Metropolitan area, we have developed a next generation triage system to improve the effectiveness of emergency response. This system includes: 1) electronic triage tags, 2) wearable vital sign sensors, 3) base stations laptops to monitor and manage patients, 4) pervasive tracking software to locate patients at all stages of the disaster response process, and 5) PDAs to support documentation and communication. Our system has evolved through three iterations of rapid- development, field-studies, usability reviews, and focus- group interview. This paper summarizes engineering considerations for technologies that must operate under constraints of medical emergencies. It is our hope that the lessons reported in this paper will help technologists in developing future emergency response systems.]
I. INTRODUCTION
Medical emergencies, where the responders must collaborate effectively to care for and track an often overwhelming number of casualties, pose numerous challenges. In mass-casualty emergencies, the rapid and accurate triage (counting and sorting) of patients is a critical early step of the response process, and triggers a chain of events in the medical and resource coordination. For years, responders performed these critical tasks with paper triage tags, clipboards of notes, and voice communications (over telephones and hand-held radios). This workflow, however, has proven labor intensive, time consuming, and prone to human error.
Responders conduct initial triage by attaching red, yellow, green or black colored paper triage tags to patients based upon assessed priority. The medics then call their EMS officers using their handheld radios, and verbally report the patient count. The officer manually tallies the patient counts on clipboards and, again, verbally reports the patient count to transportation coordinators and requests for the necessary number of ambulances.
After initial triage, patients wait at the scene until their ambulance arrives. With a resource limited response team, patients often wait for an extended period of time before transport. During this waiting period, patient conditions may deteriorate. Secondary injuries such as hypoxemia, hypotension, and cardiac tamponade become life-threatening if not treated immediately. To address these problems, current emergency response protocols require responders to periodically re-triage patients [2]. During a mass casualty emergency, however, this important protocol is often neglected by the overwhelmed responders [3]. In addition, patients with minor injuries often depart the scene without notifying the response team, thus creating an organizational headache for EMS officers who are responsible for tracking the whereabouts of each patient.
The problems listed above point towards a growing necessity to alleviate the overwhelmed responders through automation. Unfortunately, there are no systems available for automated mass casualty monitoring and tracking. Monitoring packs used by responders during routine ambulance runs can only track the vitals sign trends of a single patient, and multi-patient bedside monitoring systems require mainframe computing systems that are not suitable for field use [4][5][6]. It is with these considerations in mind that we have developed a next generation electronic triage system that facilitates collaborative and time-critical patient care in multiple levels of the medical response community. Recent events in global terrorism, military conflicts, and natural disasters raised international concern on casualty care and suggest the growing need for efficient emergency medical response solutions in the future [7].
Manuscript received June 10, 2006. This work was supported by the U.S. National Library of Medicine under Grant N01-LM-3-3516.
T. Gao is with the Johns Hopkins University Applied Physics Lab, Laurel, MD 20723 USA (phone: 240-228-3475; fax: 301-762-8230; e-mail: tia.gao@jhuapl.edu).
D. White is with the Johns Hopkins University Applied Physics Lab, Laurel, MD 20723 USA (e-mail: david.white@jhuapl.edu).
Tia Gao and David White
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