Rössler Julian, Kaserer Alexander, Albiez Benjamin, Braun Julia, Breckwoldt Jan, Spahn Donat Rudolf, Nöthiger Christoph, Tscholl David Werner
University Hospital Zurich, Zurich, Switzerland.
Biostatistics and Prevention Institute, Departments of Epidemiology and Biostatistics, University of Zurich, Zurich, Switzerland.
JMIR Med Educ. 2020 Apr 23;6(1):e17922. doi: 10.2196/17922.
Visual Patient is an avatar-based alternative to standard patient monitor displays that significantly improves the perception of vital signs. Implementation of this technology in larger organizations would require it to be teachable by brief class instruction to large groups of professionals. Therefore, our study aimed to investigate the efficacy of such a large-scale introduction to Visual Patient.
In this study, we aimed to compare 2 different educational methods, one-on-one instruction and class instruction, for training anesthesia providers in avatar-based patient monitoring.
We presented 42 anesthesia providers with 30 minutes of class instruction on Visual Patient (class instruction group). We further selected a historical sample of 16 participants from a previous study who each received individual instruction (individual instruction group). After the instruction, the participants were shown monitors with either conventional displays or Visual Patient displays and were asked to interpret vital signs. In the class instruction group, the participants were shown scenarios for either 3 or 10 seconds, and the numbers of correct perceptions with each technology were compared. Then, the teaching efficacy of the class instruction was compared with that of the individual instruction in the historical sample by 2-way mixed analysis of variance and mixed regression.
In the class instruction group, when participants were presented with the 3-second scenario, there was a statistically significant median increase in the number of perceived vital signs when the participants were shown the Visual Patient compared to when they were shown the conventional display (3 vital signs, P<.001; effect size -0.55). No significant difference was found for the 10-second scenarios. There was a statistically significant interaction between the teaching intervention and display technology in the number of perceived vital signs (P=.04; partial η=.076). The mixed logistic regression model for correct vital sign perception yielded an odds ratio (OR) of 1.88 (95% CI 1.41-2.52; P<.001) for individual instruction compared to class instruction as well as an OR of 3.03 (95% CI 2.50-3.70; P<.001) for the Visual Patient compared to conventional monitoring.
Although individual instruction on Visual Patient is slightly more effective, class instruction is a viable teaching method; thus, large-scale introduction of health care providers to this novel technology is feasible.
“可视化患者”是一种基于虚拟形象的设备，可替代标准的患者监护仪显示屏，能显著提高对生命体征的感知。在大型机构中应用这项技术，需要通过简短的课堂教学向大量专业人员传授。因此，我们的研究旨在调查对“可视化患者”进行如此大规模引入的效果。
在本研究中，我们旨在比较两种不同的教育方法，即一对一指导和课堂教学，用于培训麻醉医护人员使用基于虚拟形象的患者监护设备。
我们为42名麻醉医护人员提供了30分钟关于“可视化患者”的课堂教学（课堂教学组）。我们还从之前的一项研究中选取了16名参与者作为历史样本，他们每人接受了单独指导（单独指导组）。教学结束后，向参与者展示配备传统显示屏或“可视化患者”显示屏的监护仪，并要求他们解读生命体征。在课堂教学组中，向参与者展示3秒或10秒的场景，并比较每种技术下正确感知的数量。然后，通过双向混合方差分析和混合回归，将课堂教学的教学效果与历史样本中的单独指导效果进行比较。
在课堂教学组中，当向参与者展示3秒的场景时，与展示传统显示屏相比，当展示“可视化患者”时，参与者感知到的生命体征数量中位数有统计学意义的显著增加（3个生命体征，P<0.001；效应量-0.55）。对于10秒的场景，未发现显著差异。在感知到的生命体征数量方面，教学干预和显示技术之间存在统计学意义的交互作用（P=0.04；偏η=0.076）。正确生命体征感知的混合逻辑回归模型显示，与课堂教学相比，单独指导的优势比（OR）为1.88（95%CI 1.41-2.52；P<0.001），与传统监测相比，“可视化患者”的OR为3.03（95%CI 2.50-3.70；P<0.001）。
虽然对“可视化患者”的单独指导效果稍好，但课堂教学是一种可行的教学方法；因此，向医疗保健人员大规模引入这项新技术是可行的。
