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在大规模伤亡模拟中借助无人机协助进行分类、评估、救生干预及分诊:教育效果分析

Sort, Assess, Life-Saving Intervention, Triage With Drone Assistance in Mass Casualty Simulation: Analysis of Educational Efficacy.

作者信息

Hartman Ethan N, Daines Benjamin, Seto Christina, Shimshoni Deborah, Feldman Madison E, LaBrunda Michelle

机构信息

Medicine, University of Central Florida College of Medicine, Orlando, USA.

Internal Medicine, University of Central Florida College of Medicine, Orlando, USA.

出版信息

Cureus. 2020 Sep 21;12(9):e10572. doi: 10.7759/cureus.10572.

Abstract

Introduction Mass casualty incident (MCI) simulation and triage are educational methods used to provide high fidelity training to first response teams. Simulation and triage need to be as effective as possible to train professionals for true emergencies involving mass casualty. Although MCI simulation and triage have been used in the pre-professional setting (i.e. medical school, nursing school, etc.), more data is required regarding quality improvement of these simulations. This study focuses on quality improvement of MCI simulation and triage in the pre-professional training. In order to evaluate simulation quality to optimize future triage simulations, this study had three specific aims: (1) assess participant accuracy of triage after training in Sort, Assess, Life-Saving Interventions, Triage/Transport (SALT); (2) evaluate the role of stress and confidence in participants of triage simulation; (3) determine trainees' perception of unmanned aerial vehicles (drones) in the setting of mass casualty simulation. Methods A total of 44 attendees of the University of Central Florida (UCF) College of Medicine Global Health Conference participated in this study across three groups. Each group was provided a 15-minute lecture on SALT protocol. After the training, the participants continued to a 30-minute simulation in which they were asked to accurately triage up to 46 patient-actors. Each participants' triage designations were compared to the previously assigned designations of each patient-actor. Pre- and post-simulation surveys were collected and analyzed using Statistical Package for the Social Sciences (SPSS) (IBM Corp., Chicago, IL). All other data were analyzed using descriptive statistics.  Results Qualitative and Likert data for the simulation were collected from 44 participants. Given a total of 1,113 triage scores (average of 25.29 triage designations per person), there was data to support that novice learners in this study tended to under-triage using the SALT protocol after 15-minute SALT training, with an overall accuracy of 52.43%. Survey data showed that confidence in mass casualty triage improved post-simulation, improving from median 3/10 to 5/10. Most participants were unaware of the use of unmanned aerial vehicles in MCI but most had positive opinions of their usefulness in MCI after the simulation, with a median score of 8/10. Conclusions Participant accuracy of triage after undergoing a 15-minute training in SALT triage was 52.43%, with a non-statistically significant tendency to under-triage. This accuracy level is consistent with other studies of SALT triage in MCI, but the tendency to undertriage requires further study for validation. Stress levels during the simulation were significantly elevated, while post-simulation confidence increased significantly from pre-simulation. The perception of drone utility in MCI was favorable among participants in this study, indicating drones may be useful for first response teams in future mass casualty simulations.

摘要

引言 大规模伤亡事件(MCI)模拟和分诊是用于为第一反应小组提供高保真培训的教育方法。模拟和分诊需要尽可能有效,以便为应对涉及大规模伤亡的真实紧急情况培训专业人员。尽管MCI模拟和分诊已在专业前环境(即医学院、护理学院等)中使用,但关于这些模拟的质量改进还需要更多数据。本研究聚焦于专业前培训中MCI模拟和分诊的质量改进。为了评估模拟质量以优化未来的分诊模拟,本研究有三个具体目标:(1)评估在“分类、评估、救生干预、分诊/转运”(SALT)培训后参与者分诊的准确性;(2)评估压力和信心在分诊模拟参与者中的作用;(3)确定学员在大规模伤亡模拟场景中对无人驾驶飞行器(无人机)的看法。方法 中佛罗里达大学(UCF)医学院全球健康会议的44名参会者分三组参与了本研究。每组都接受了关于SALT协议的15分钟讲座。培训后,参与者继续进行30分钟的模拟,要求他们对多达46名患者模拟角色进行准确分诊。将每个参与者的分诊指定与每个患者模拟角色先前指定的情况进行比较。使用社会科学统计软件包(SPSS)(IBM公司,伊利诺伊州芝加哥)收集和分析模拟前后的调查问卷。所有其他数据使用描述性统计进行分析。结果 从44名参与者收集了模拟的定性和李克特数据。在总共1113个分诊分数(每人平均25.29个分诊指定)中,有数据支持本研究中的新手学习者在15分钟的SALT培训后使用SALT协议时倾向于分诊不足,总体准确率为52.43%。调查数据显示,大规模伤亡分诊的信心在模拟后有所提高,从中位数3/10提高到5/10。大多数参与者不知道在MCI中使用无人驾驶飞行器,但大多数人在模拟后对其在MCI中的有用性持积极看法,中位数得分为8/10。结论 参与者在接受15分钟的SALT分诊培训后的分诊准确率为52.43% , 存在分诊不足的趋势,但无统计学意义。这个准确率水平与其他关于MCI中SALT分诊的研究一致,但分诊不足的趋势需要进一步研究以验证。模拟期间压力水平显著升高,而模拟后的信心较模拟前显著增加。本研究中的参与者对MCI中无人机效用的看法良好,表明无人机可能对未来大规模伤亡模拟中的第一反应小组有用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/739a/7577607/b058a81341d7/cureus-0012-00000010572-i01.jpg

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