Speech recognition technology for assessing team debriefing communication and interaction patterns: An algorithmic toolkit for healthcare simulation educators.

Autor: Brutschi R; D-MAVT, ETH Zurich, Leonhardstrasse, Zurich, 8092, Zurich, Switzerland., Wang R; D-MAVT, ETH Zurich, Leonhardstrasse, Zurich, 8092, Zurich, Switzerland. ruiwang46@ethz.ch., Kolbe M; Simulation Center USZ, Universitätsspital Zürich, Huttenstrasse 46, Zurich, 8091, Zurich, Switzerland., Weiss K; D-MAVT, ETH Zurich, Leonhardstrasse, Zurich, 8092, Zurich, Switzerland., Lohmeyer Q; D-MAVT, ETH Zurich, Leonhardstrasse, Zurich, 8092, Zurich, Switzerland., Meboldt M; D-MAVT, ETH Zurich, Leonhardstrasse, Zurich, 8092, Zurich, Switzerland.
Jazyk: angličtina
Zdroj: Advances in simulation (London, England) [Adv Simul (Lond)] 2024 Oct 09; Vol. 9 (1), pp. 42. Date of Electronic Publication: 2024 Oct 09.
DOI: 10.1186/s41077-024-00315-1
Abstrakt: Background: Debriefings are central to effective learning in simulation-based medical education. However, educators often face challenges when conducting debriefings, which are further compounded by the lack of empirically derived knowledge on optimal debriefing processes. The goal of this study was to explore the technical feasibility of audio-based speaker diarization for automatically, objectively, and reliably measuring debriefing interaction patterns among debriefers and participants. Additionally, it aimed to investigate the ability to automatically create statistical analyses and visualizations, such as sociograms, solely from the audio recordings of debriefings among debriefers and participants.
Methods: We used a microphone to record the audio of debriefings conducted during simulation-based team training with third-year medical students. The debriefings were led by two healthcare simulation instructors. We processed the recorded audio file using speaker diarization machine learning algorithms and validated the results manually to showcase its accuracy. We selected two debriefings to compare the speaker diarization results between different sessions, aiming to demonstrate similarities and differences in interaction patterns.
Results: Ten debriefings were analyzed, each lasting about 30 min. After data processing, the recorded data enabled speaker diarization, which in turn facilitated the automatic creation of visualized interaction patterns, such as sociograms. The findings and data visualizations demonstrated the technical feasibility of implementing audio-based visualizations of interaction patterns, with an average accuracy of 97.78%.We further analyzed two different debriefing cases to uncover similarities and differences between the sessions. By quantifying the response rate from participants, we were able to determine and quantify the level of interaction patterns triggered by instructors in each debriefing session. In one session, the debriefers triggered 28% of the feedback from students, while in the other session, this percentage increased to 36%.
Conclusion: Our results indicate that speaker diarization technology can be applied accurately and automatically to provide visualizations of debriefing interactions. This application can be beneficial for the development of simulation educator faculty. These visualizations can support instructors in facilitating and assessing debriefing sessions, ultimately enhancing learning outcomes in simulation-based healthcare education.
(© 2024. The Author(s).)
Databáze: MEDLINE
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