Ozella Laura, Gauvin Laetitia, Carenzo Luca, Quaggiotto Marco, Ingrassia Pier Luigi, Tizzoni Michele, Panisson André, Colombo Davide, Sapienza Anna, Kalimeri Kyriaki, Della Corte Francesco, Cattuto Ciro
Data Science Laboratory, Institute for Scientific Interchange Foundation, Torino, Italy.
Department of Translational Medicine, Eastern Piedmont University, Novara, Italy.
J Med Internet Res. 2019 Apr 26;21(4):e12251. doi: 10.2196/12251.
Over the past several decades, naturally occurring and man-made mass casualty incidents (MCIs) have increased in frequency and number worldwide. To test the impact of such events on medical resources, simulations can provide a safe, controlled setting while replicating the chaotic environment typical of an actual disaster. A standardized method to collect and analyze data from mass casualty exercises is needed to assess preparedness and performance of the health care staff involved.
In this study, we aimed to assess the feasibility of using wearable proximity sensors to measure proximity events during an MCI simulation. In the first instance, our objective was to demonstrate how proximity sensors can collect spatial and temporal information about the interactions between medical staff and patients during an MCI exercise in a quasi-autonomous way. In addition, we assessed how the deployment of this technology could help improve future simulations by analyzing the flow of patients in the hospital.
Data were obtained and collected through the deployment of wearable proximity sensors during an MCI functional exercise. The scenario included 2 areas: the accident site and the Advanced Medical Post, 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 health care assistants and other hospital support staff. Each victim was assigned a score related to the severity of his/her injury. Each participant wore a proximity sensor, and in addition, 30 fixed devices were placed in the field hospital.
The contact networks show a heterogeneous distribution of the cumulative time spent in proximity by the participants. We obtained contact matrices based on the cumulative time spent in proximity between the victims and rescuers. Our results showed that the time spent in proximity by the health care 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 the triage code and diagnosis, and no obvious bottlenecks were found.
Our study shows the feasibility of the use of wearable sensors for tracking close contacts among individuals during an MCI simulation. It represents, to our knowledge, the first example of unsupervised data collection-ie, without the need for the involvement of observers, which could compromise the realism of the exercise-of face-to-face contacts during an MCI exercise. Moreover, by permitting detailed data collection about the simulation, such as data related to the flow of patients in the hospital, such deployment provides highly relevant input for the improvement of MCI resource allocation and management.
在过去几十年里，全球范围内自然发生和人为造成的大规模伤亡事件（MCI）在频率和数量上都有所增加。为了测试此类事件对医疗资源的影响，模拟可以提供一个安全、可控的环境，同时再现实际灾难中典型的混乱场景。需要一种标准化的方法来收集和分析大规模伤亡演习中的数据，以评估参与的医护人员的准备情况和表现。
在本研究中，我们旨在评估在MCI模拟期间使用可穿戴式接近传感器测量接近事件的可行性。首先，我们的目标是展示接近传感器如何以准自主的方式收集关于医护人员与患者在MCI演习期间互动的空间和时间信息。此外，我们通过分析医院内患者的流动情况，评估了该技术的部署如何有助于改进未来的模拟。
在一次MCI功能演习期间，通过部署可穿戴式接近传感器来获取和收集数据。场景包括两个区域：事故现场和高级医疗站，演习持续3小时。共有238名参与者参与了演习，并根据他们的角色进行分类：14名医生、16名护士、134名受害者、47名紧急医疗服务人员以及27名医护助理和其他医院支持人员。为每个受害者分配了一个与其受伤严重程度相关的分数。每个参与者都佩戴一个接近传感器，此外，在野战医院还放置了30个固定设备。
接触网络显示了参与者在接近状态下累计时间的不均匀分布。我们根据受害者与救援人员之间接近状态下的累计时间获得了接触矩阵。我们的结果表明，医护团队与受害者接近的时间与患者的受伤严重程度相关。对患者流动情况的分析表明，医院病房内患者的情况与分诊代码和诊断结果一致，未发现明显的瓶颈。
我们的研究表明，在MCI模拟期间使用可穿戴传感器跟踪个体之间的密切接触是可行的。据我们所知，这代表了在MCI演习期间无监督数据收集（即无需观察员参与，否则可能会损害演习的真实性）面对面接触的首个实例。此外，通过允许对模拟进行详细的数据收集，例如与医院内患者流动相关的数据，这种部署为改进MCI资源分配和管理提供了高度相关的输入。
