Background: Natural language processing (NLP) may be a tool for automating trauma teamwork assessment in simulated scenarios.
Methods: Using the Trauma Nontechnical Skills Assessment (T-NOTECHS), raters assessed video recordings of trauma teams in simulated pre-debrief (Sim1) and post-debrief (Sim2) trauma resuscitations. We developed codes through directed content analysis and created algorithms capturing teamwork-related discourse through NLP. Using a within subjects pre-post design (n = 150), we compared changes in teams' Sim1 versus Sim2 T-NOTECHS scores and automatically coded discourse to identify which NLP algorithms could identify skills assessed by the T-NOTECHS.
Results: Automatically coded behaviors revealed significant post-debrief increases in teams' simulation discourse: Verbalizing Findings, Acknowledging Communication, Directed Communication, Directing Assessment and Role Assignment, and Leader as Hub for Information.
Conclusions: Our results suggest NLP can capture changes in trauma team discourse. These findings have implications for the expedition of team assessment and innovations in real-time feedback when paired with speech-to-text technology.
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| http://dx.doi.org/10.1016/j.amjsurg.2022.09.018 | DOI Listing |
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