Mobile Decision Support Tool for Emergency Departments and Mass Casualty Incidents (EDIT): Initial Study

Abstract

BackgroundChemical exposures pose a significant threat to life. A rapid assessment by first responders and emergency nurses is required to reduce death and disability. Currently, no informatics tools exist to process victims of chemical exposures efficiently. The surge of patients into a hospital emergency department during a mass casualty incident creates additional stress on an already overburdened system, potentially placing patients at risk and challenging staff to process patients for appropriate care and treatment efficacy. Traditional emergency department triage models are oversimplified during highly stressed mass casualty incident scenarios in which there is little margin for error. Emerging mobile technology could alleviate the burden placed on nurses by allowing the freedom to move about the emergency department and stay connected to a decision support system.ObjectiveThis study aims to present and evaluate a new mobile tool for assisting emergency department personnel in patient management and triage during a chemical mass casualty incident.MethodsOver 500 volunteer nurses, students, and first responders were recruited for a study involving a simulated chemical mass casualty incident. During the exercise, a mobile application was used to collect patient data through a kiosk system. Nurses also received tablets where they could review patient information and choose recommendations from a decision support system. Data collected was analyzed on the efficiency of the app to obtain patient data and on nurse agreement with the decision support system.ResultsOf the 296 participants, 96.3% (288/296) of the patients completed the kiosk system with an average time of 3 minutes, 22 seconds. Average time to complete the entire triage process was 5 minutes, 34 seconds. Analysis of the data also showed strong agreement among nurses regarding the app’s decision support system. Overall, nurses agreed with the system 91.6% (262/286) of the time when it came to choose an exposure level and 84.3% (241/286) of the time when selecting an action.ConclusionsThe app reliably demonstrated the ability to collect patient data through a self-service kiosk system thus reducing the burden on hospital resources. Also, the mobile technology allowed nurses the freedom to triage patients on the go while staying connected to a decision support system in which they felt would give reliable recommendations.

Designing and executing a functional exercise to test a novel informatics tool for mass casualty triage

“…EDICT is a mobile application designed to triage and manage patients during a chemical MCI using a client-server transaction model. 21 Users interacted with EDICT through unique modes, kiosk mode and nurse mode. Kiosk mode is designed for patient data entry while nurse mode utilizes the AI algorithm to provide decision support on exposure status and triage recommendations.…”

“…The on-site server allowed for real-time bidirectional communication with EDICT to receive patient data from the kiosk stations and, based upon the tested AI algorithm discussed above, provided AI decision support to the nurse stations. 21 The server used the patients' symptoms (ie, type and number) and vitals (ie, heart rate, respiratory rate, oxygen saturation) along with the patients' geographical locations to provide the secondary triage nurse with a recommended exposure status (ie, exposed, potentially exposed, not exposed) and triage category (ie, exit, monitor, urgent, immediate). The interoperability allowed for data integrity in the event of a hardware failure.…”

“…Overall, the triage process progressed faster than anticipated and allowed the testing to be completed ahead of schedule. 21 This was due, in part, to the flexibility of EDICT to meet the demand.…”

Patient triage system for supporting the operation of dispatch centres and rescue teams

“…The triage in healthcare management consists of three stages, namely, pre-hospital triage (stage 1), which involves dispatching an ambulance and pre-hospital care resources, triage at the scene (stage 2) by first response emergency staff who attend the patient, and triage on arrival at the hospital or emergency unit (stage 3) [ 16 ]. Several smart systems have been widely used in order to reduce the time consumed in the triage process based on supporting medical staff’s rapid decision-making [ 17 ] and patients’ self-triage [ 18 ].…”

Automated Analysis of Femoral Artery Calcification Using Machine Learning Techniques

“…Extensive work has been performed to create new mechanisms of predictive modeling [12] (such as LSTMs, DNNs, and CNNs) as well as to improve already existing methods (such as ANNs, decision trees, etc). In the closer domain of healthcare [9], [13], ANNs have been used in medical fraud detection [14], [15] and in prediction of a patient's response to a drug [16], [17], thereby paving the way to personalized medicine. More specific to our investigation, the key question for clinicians, patients, regulators and insurance providers is: Can Artificial Intelligence use personalized data in patients with PAD to derive a predictive model of individual outcomes following femoral endarterectomy in terms of complications (death, lower extremity amputation, disease progression and need for intervention) at presentation?…”