JMIR Publications

ABSTRACT

Background:

Over the past several decades, naturally occurring and man-made mass casualty incidents (MCI) have increased in frequency and number, worldwide. To test the impact of such event on medical resources, simulations can provide a safe, controlled setting while replicating the chaotic environment typical of an actual disaster. A standardised method to collect and analyse data from mass casualty exercises is needed, in order to assess preparedness and performance of the healthcare staff involved.

Objective:

We report on the use of wearable proximity sensors to measure proximity events during a MCI simulation. We investigated the interactions between medical staff and patients, to evaluate the time dedicated by the medical staff with respect to the severity of the injury of the victims depending on the roles. Moreover, we estimated the presence of the patients in the different spaces of the field hospital, in order to study the patients’ flow.

Methods:

Data were obtained and collected through the deployment of wearable proximity sensors during a mass casualty incident functional exercise. The scenario included two areas: the accident site and the Advanced Medical Post (AMP), and the exercise lasted 3 hours. A total of 238 participants were involved in the exercise and classified in categories according to their role: 14 medical doctors, 16 nurses, 134 victims, 47 Emergency Medical Services staff members, and 27 healthcare assistants and other hospital support staff. Each victim was assigned a score related to the severity of his injury. Each participant wore a proximity sensor and, in addition, 30 fixed devices were placed in the field hospital.

Results:

The contact networks show a heterogeneous distribution of the cumulative time spent in proximity by participants. We obtained contact matrices based on cumulative time spent in proximity between victims and the rescuers. Our results showed that the time spent in proximity by the healthcare teams with the victims is related to the severity of the patient’s injury. The analysis of patients’ flow showed that the presence of patients in the rooms of the hospital is consistent with triage code and diagnosis, and no obvious bottlenecks were found.

Conclusions:

Our study shows the feasibility of the use of wearable sensors for tracking close contacts among individuals during a mass casualty incident simulation. It represents, to our knowledge, the first example of un- supervised data collection of face-to-face contacts during a MCI exercise. The unsupervised measurement of contact patterns with proximity sensors provides an unique opportunity to monitor the interactions between participants without the involvement of direct observers, which could compromise the realism of the exercise. Moreover, the use of the sensors as fixed devices allowed to analyse the flow of the patients in the field hospital, in order to assess if they were optimally headed by the healthcare personnel.