Department of Emergency Medicine, Yale University School of Medicine, New Haven, Connecticut 06519, USA.
Eur J Emerg Med. 2011 Dec;18(6):314-21. doi: 10.1097/MEJ.0b013e328345d6fd.
Virtual reality systems may allow for organized study of mass casualty triage systems by allowing investigators to replicate the same mass casualty incident, with the same victims, for a large number of rescuers. The study objectives were to develop such a virtual reality system, and use it to assess the ability of trained paramedic students to triage simulated victims using two triage systems.
Investigators created 25 patient scenarios for a highway bus crash in a virtual reality simulation system. Paramedic students were trained to proficiency on the new 'Sort, Assess, Life saving interventions, Treat and Transport (SALT)' triage system, and 22 students ran the simulation, applying the SALT algorithm to each victim. After a 3-month washout period, the students were retrained on the 'Smart' triage system, and each student ran the same crash simulation using the Smart system. Data inputs were recorded by the simulation software and analyzed with the paired t-tests.
The students had a mean triage accuracy of 70.0% with SALT versus 93.0% with Smart (P=0.0001). Mean overtriage was 6.8% with SALT versus 1.8% with Smart (P=0.0015), and mean undertriage was 23.2% with SALT versus 5.1% with Smart (P=0.0001). The average time for a student to triage the scene was 21 min 3 s for SALT versus 11 min 59 s for Smart (P=0.0001).
The virtual reality platform seems to be a viable research tool for examining mass casualty triage. A small sample of trained paramedic students using the virtual reality system was able to triage simulated patients faster and with greater accuracy with 'Smart' triage than with 'SALT' triage.
虚拟现实系统可以通过允许研究人员复制相同的大规模伤亡事件,用相同的受害者,来对大量救援人员进行大规模伤亡分诊系统的有组织研究。本研究的目的是开发这样一个虚拟现实系统,并利用它来评估经过培训的护理学生使用两种分诊系统对模拟受害者进行分诊的能力。
研究人员在虚拟现实模拟系统中为公路公共汽车事故创建了 25 个患者场景。护理学生接受了新的“分类、评估、救生干预、治疗和转运(SALT)”分诊系统的熟练培训,22 名学生进行了模拟,将 SALT 算法应用于每个受害者。经过 3 个月的洗脱期后,学生们重新接受了“智能”分诊系统的培训,并使用 Smart 系统对相同的碰撞模拟进行了测试。模拟软件记录了数据输入,并进行了配对 t 检验分析。
学生使用 SALT 的分诊准确率平均为 70.0%,而使用 Smart 的准确率为 93.0%(P=0.0001)。SALT 的过度分诊率平均为 6.8%,而 Smart 的过度分诊率为 1.8%(P=0.0015),SALT 的分诊不足率平均为 23.2%,而 Smart 的分诊不足率为 5.1%(P=0.0001)。学生对场景进行分诊的平均时间为 SALT 21 分 3 秒,而 Smart 为 11 分 59 秒(P=0.0001)。
虚拟现实平台似乎是研究大规模伤亡分诊的一种可行的研究工具。使用虚拟现实系统的一小部分经过培训的护理学生能够更快、更准确地对模拟患者进行分诊,使用 Smart 分诊的准确率高于 SALT 分诊。