Towards an AI Coach to Infer Team Mental Model Alignment in Healthcare
arXiv:2102.08507 · doi:10.1109/CogSIMA51574.2021.9475925
Abstract
Shared mental models are critical to team success; however, in practice, team members may have misaligned models due to a variety of factors. In safety-critical domains (e.g., aviation, healthcare), lack of shared mental models can lead to preventable errors and harm. Towards the goal of mitigating such preventable errors, here, we present a Bayesian approach to infer misalignment in team members' mental models during complex healthcare task execution. As an exemplary application, we demonstrate our approach using two simulated team-based scenarios, derived from actual teamwork in cardiac surgery. In these simulated experiments, our approach inferred model misalignment with over 75% recall, thereby providing a building block for enabling computer-assisted interventions to augment human cognition in the operating room and improve teamwork.
arXiv commentSubmitted to the 2021 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)