Ruberto Aaron J, Rodenburg Dirk, Ross Kyle, Sarkar Pritam, Hungler Paul C, Etemad Ali, Howes Daniel, Clarke Daniel, McLellan James, Wilson Daryl, Szulewski Adam
Kingston Health Sciences Centre Department of Emergency Medicine Queen's University Kingston Ontario Canada.
Thunder Bay Regional Health Sciences Centre Department of Critical Care Medicine Northern Ontario School of Medicine Thunder Bay Ontario Canada.
AEM Educ Train. 2021 Jul 1;5(3):e10605. doi: 10.1002/aet2.10605. eCollection 2021 Jul.
In resuscitation medicine, effectively managing cognitive load in high-stakes environments has important implications for education and expertise development. There exists the potential to tailor educational experiences to an individual's cognitive processes via real-time physiologic measurement of cognitive load in simulation environments.
The goal of this research was to test a novel simulation platform that utilized artificial intelligence to deliver a medical simulation that was adaptable to a participant's measured cognitive load.
The research was conducted in 2019. Two board-certified emergency physicians and two medical students participated in a 10-minute pilot trial of a novel simulation platform. The system utilized artificial intelligence algorithms to measure cognitive load in real time via electrocardiography and galvanic skin response. In turn, modulation of simulation difficulty, determined by a participant's cognitive load, was facilitated through symptom severity changes of an augmented reality (AR) patient. A postsimulation survey assessed the participants' experience.
Participants completed a simulation that successfully measured cognitive load in real time through physiological signals. The simulation difficulty was adapted to the participant's cognitive load, which was reflected in changes in the AR patient's symptoms. Participants found the novel adaptive simulation platform to be valuable in supporting their learning.
Our research team created a simulation platform that adapts to a participant's cognitive load in real-time. The ability to customize a medical simulation to a participant's cognitive state has potential implications for the development of expertise in resuscitation medicine.
在复苏医学中，在高风险环境中有效管理认知负荷对教育和专业技能发展具有重要意义。通过在模拟环境中对认知负荷进行实时生理测量，有可能根据个体的认知过程量身定制教育体验。
本研究的目的是测试一个新颖的模拟平台，该平台利用人工智能提供一种能适应参与者测量到的认知负荷的医学模拟。
该研究于2019年进行。两名获得董事会认证的急诊医生和两名医学生参与了一个新颖模拟平台的10分钟试点试验。该系统利用人工智能算法通过心电图和皮肤电反应实时测量认知负荷。反过来，根据参与者的认知负荷确定的模拟难度调制，通过增强现实（AR）患者的症状严重程度变化来实现。模拟后调查评估了参与者的体验。
参与者完成了一个模拟，该模拟通过生理信号成功实时测量了认知负荷。模拟难度适应了参与者的认知负荷，这反映在AR患者症状的变化上。参与者发现这个新颖的自适应模拟平台对支持他们的学习很有价值。
我们的研究团队创建了一个能实时适应参与者认知负荷的模拟平台。根据参与者的认知状态定制医学模拟的能力对复苏医学专业技能的发展具有潜在意义。
