An automated content-based measure of closed loop communication among critical care air transport teams.

Abstract

Successful teamwork is essential to ensure critical care air transport (CCAT) patients receive effective care. Despite the importance of team performance, current training methods rely on subjective performance assessments and do not evaluate performance at the team level. Researchers have developed the Team Dynamics Measurement System (TDMS) to provide real-time, objective measures of team coordination to assist trainers in providing CCAT aircrew with feedback to improve performance. The first iteration of TDMS relied exclusively on communication flow patterns (i.e., who was speaking and when) to identify instances of various communication types such as closed loop communication (CLC). The research presented in this paper significantly advances the TDMS project by incorporating natural language processing (NLP) to identify CLC. The addition of NLP to the existing TDMS resulted in greater accuracy and fewer false alarms in identifying instances of CLC compared to the previous flow-based implementation. We discuss ways in which these improvements will facilitate instructor feedback and support further refinement of the TDMS.