Popov Vitaliy, Rochlen Lauryn R
Department of Learning Health Sciences, University of Michigan Medical School, University of Michigan, School of Information, Victor Vaughan, 217, 1111 Catherine St, Ann Arbor, MI, 48109, USA.
School of Information, Ann Arbor, MI, USA.
BMC Med Educ. 2025 Apr 3;25(1):479. doi: 10.1186/s12909-025-07062-5.
Effective team communication is crucial for managing medical emergencies like malignant hyperthermia (MH), but current assessment methods fail to capture the dynamic and temporal nature of teamwork processes. The lack of reliable measures to inform feedback to teams is likely limiting the overall effectiveness of simulation training. This study demonstrates the application of ordered network analysis (ONA) to model communication sequences during the simulated MH scenario.
Twenty-two anesthesiologists participated in video-recorded MH simulations. Each scenario involved one participant as the primary anesthesiologist with confederates in supporting roles. Team communication was coded using the Team Reflection Behavioral Observation (TuRBO) framework, capturing behaviors related to information gathering, evaluation, planning, and implementation. ONA modeled the sequences of these coded behaviors as dynamic networks. Teams were classified as high- or low-performing based on timely dantrolene administration and appropriate MH treatment actions. Network visualizations and statistical tests compared communication patterns between groups.
Five of 22 teams (23%) were high-performing. ONA revealed high-performers transitioned more effectively from situation assessment (information seeking/evaluation) to planning and implementation, while low-performers cycled between assessment behaviors without progressing (p = 0.04, Cohen's d = 1.72). High-performers demonstrated stronger associations between invited input, explicitly assessing the situation, stating plans, and implementation.
Integrating video coding with ONA provides an innovative approach for examining team behaviors. Leveraging ONA can uncover patterns in communication timing and sequences, guiding targeted interventions to improve team coordination in various real-world clinical and simulated settings (e.g., operating room, EMS, ICU).
有效的团队沟通对于处理诸如恶性高热(MH)等医疗紧急情况至关重要,但当前的评估方法未能捕捉到团队协作过程的动态性和时效性。缺乏可靠的措施为团队提供反馈可能限制了模拟培训的整体效果。本研究展示了有序网络分析(ONA)在模拟MH场景中对沟通序列进行建模的应用。
22名麻醉医生参与了视频记录的MH模拟。每个场景中,一名参与者作为主麻醉医生,其他人员扮演辅助角色。团队沟通使用团队反思行为观察(TuRBO)框架进行编码,捕捉与信息收集、评估、计划和实施相关的行为。ONA将这些编码行为的序列建模为动态网络。根据丹曲林的及时给药和适当的MH治疗行动,将团队分为高绩效或低绩效组。通过网络可视化和统计测试比较两组之间的沟通模式。
22个团队中有5个(23%)为高绩效团队。ONA显示,高绩效团队能更有效地从情况评估(信息寻求/评估)过渡到计划和实施,而低绩效团队在评估行为之间循环,没有进展(p = 0.04,科恩d值 = 1.72)。高绩效团队在邀请输入、明确评估情况、陈述计划和实施之间表现出更强的关联。
将视频编码与ONA相结合为检查团队行为提供了一种创新方法。利用ONA可以揭示沟通时间和序列中的模式,指导有针对性的干预措施,以改善各种实际临床和模拟环境(如手术室、急救医疗服务、重症监护病房)中的团队协作。