The following is a summary of “Measuring the impact of simulation debriefing on the practices of interprofessional trauma teams using natural language processing,” published in the FEBRUARY 2023 issue of Surgery by Rosser, et al.

For a study, researchers sought to investigate the potential use of natural language processing (NLP) in automating the assessment of trauma teamwork in simulated scenarios.

The Trauma Nontechnical Skills Assessment (T-NOTECHS) was used to assess video recordings of trauma teams in simulated pre-debrief (Sim1) and post-debrief (Sim2) trauma resuscitations. NLP algorithms were developed to capture teamwork-related discourse. The study used a within-subjects pre-post design, with 150 participants, to compare changes in the teams’ T-NOTECHS scores between Sim1 and Sim2, and to identify which NLP algorithms could identify the skills assessed by the T-NOTECHS. Codes were developed through directed content analysis.

The study found that the NLP algorithms could capture changes in trauma team discourse. The analysis of automatically coded behaviors showed significant post-debrief increases in the teams’ simulation discourse related to Verbalizing Findings, Acknowledging Communication, Directed Communication, Directing Assessment and Role Assignment, and Leader as Hub for Information.

The study suggested that NLP can be used to capture changes in trauma team discourse. The findings have implications for the expedition of team assessment and innovations in real-time feedback when paired with speech-to-text technology.

Reference: https://www.americanjournalofsurgery.com/article/S0002-9610(22)00565-7/fulltext