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Related Experiment Video

Updated: May 17, 2025

Simulator Training for Endovascular Neurosurgery
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From communication to action: using ordered network analysis to model team performance in clinical simulation.

Vitaliy Popov1,2, Lauryn R Rochlen3,4

  • 1Department 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. vipopov@umich.edu.

BMC Medical Education
|April 2, 2025
PubMed
Summary

Ordered network analysis (ONA) effectively models team communication during malignant hyperthermia (MH) crises. High-performing teams efficiently transitioned from assessment to action, unlike low-performing teams.

Keywords:
Anesthesiology Acute Care TeamsClinical SimulationOrdered network analysisTeam communication

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Area of Science:

  • Medical simulation
  • Teamwork dynamics
  • Network analysis

Background:

  • Effective team communication is vital for managing medical emergencies like malignant hyperthermia (MH).
  • Current assessment methods lack the dynamic and temporal analysis needed for effective simulation training feedback.
  • Ordered network analysis (ONA) offers a novel approach to model communication sequences in simulated scenarios.

Purpose of the Study:

  • To demonstrate the application of ONA for analyzing team communication patterns during simulated MH events.
  • To identify communication differences between high- and low-performing teams in managing MH.
  • To provide a quantitative method for assessing teamwork in critical care simulations.

Main Methods:

  • Twenty-two anesthesiologists participated in video-recorded MH simulations.
  • Team communication was coded using the Team Reflection Behavioral Observation (TuRBO) framework.
  • ONA modeled communication sequences, comparing patterns between high- (timely dantrolene) and low-performing teams.

Main Results:

  • High-performing teams (23%) showed smoother transitions from situation assessment to planning and implementation.
  • Low-performing teams frequently cycled through assessment behaviors without progressing (p=0.04).
  • High-performers exhibited stronger links between input, assessment, planning, and implementation.

Conclusions:

  • Integrating video coding with ONA provides an innovative method for examining team behaviors.
  • ONA can reveal crucial patterns in communication timing and sequences.
  • This approach can guide targeted interventions to improve team coordination in clinical and simulated settings.