Using epistemic network analysis to identify targets for educational interventions in trauma team communication

Abstract

Background: Epistemic Network Analysis (ENA) is a technique for modeling and comparing the structure of connections between elements in coded data. We hypothesized that connections among team discourse elements as modeled by ENA would predict the quality of team performance in trauma simulation.

Methods: The Modified Non-technical Skills Scale for Trauma (T-NOTECHS) was used to score a simulation-based trauma team resuscitation. Sixteen teams of 5 trainees participated. Dialogue was coded using Verbal Response Modes (VRM), a speech classification system. ENA was used to model the connections between VRM codes. ENA models of teams with lesser T-NOTECHS scores (n = 9, mean = 16.98, standard deviation [SD] = 1.45) were compared with models of teams with greater T-NOTECHS scores (n = 7, mean = 21.02, SD = 1.09).

Results: Teams had different patterns of connections among VRM speech form codes with regard to connections among questions and edifications (meanHIGH = 0.115, meanLOW = -0.089; t = 2.21; P = .046, Cohen d = 1.021). Greater-scoring groups had stronger connections between stating information and providing acknowledgments, confirmation, or advising. Lesser-scoring groups had a stronger connection between asking questions and stating information. Discourse data suggest that this pattern reflected increased uncertainty. Lesser-scoring groups also had stronger connections from edifications to disclosures (revealing thoughts, feelings, and intentions) and interpretations (explaining, judging, and evaluating the behavior of others).

Conclusion: ENA is a novel and valid method to assess communication among trauma teams. Differences in communication among higher- and lower-performing teams appear to result from the ways teams use questions. ENA allowed us to identify targets for improvement related to the use of questions and stating information by team members.