Pa2Pa: Patient to patient communication for emergency response support

Abstract

Usually, first responders estimate the medical needs in mass casualties scenarios from subjective observations gathered through uncoordinated emergency calls from non-experts in the incident location. Accordingly, they command specific teams to move to the location. At arrival the teams make local measurements, based on which they rank the priorities of patients, and give local treatments or decide to transport them to a specific hospital. Nevertheless the advances in the measurement of vital signs, still the human estimation may be error prone and not-in-time since usually the ratio of first responders to casualties can reach one to hundreds or even thousands in some cases. In this paper, we present a novel method to rank the urgency level of mass casualties through a localized ad-hoc sensor network and localized Real-Time (RT) sensor data processing. Our approach is based on plain patient to patient communication without relying on the existence of first responders nor a communication infrastructure. This allows for the first time to classify, rank and schedule casualties without experts in the loop. The casualties, the responders and the administration gains are very compelling.