Preliminary Data Collection for Collaborative Emergency Department Crowd Management using Wearable Devices

Abstract

Emergency department (ED) visits have risen to more than 60% since 1997, with more than 90% of U.S EDs being over-stretched due to overcrowding which has only been compounded by the recent pandemic. Consequences for ED overcrowding range from less severe effects such as patient inconvenience to more severe outcomes such as patient fatality. Research shows poor crowd management at the ED does not only affect patients but takes a toll on ED staff as well. To attempt to address this issue, our study researches how patient vitals collected and transmitted in real time to ED staff can help manage patients in the ED using a triage system that orders vitals in an urgent priority listing. We gathered data from participants using non-invasive wearable devices (CareTaker4 & Oximeter) to collect vital signs information such as heart rate, respiratory rate, blood pressure, and oxygen levels. We aim to use the data to feed a mathematical model that will create a priority algorithm that can sort patients in an ED according to the urgency of their vital signs and transmit the data in real time to health personnel. This way, the patients can be moved automatically in the list as they deteriorate while waiting. We were able to plot the data to show which patients' health are deteriorating quickly and that would require immediate attention. This will be instrumental by helping ED staff attend to pressing cases faster and help control crowds according to medical urgency instead of a first come first serve basis which is not always effective.