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.

本文介绍展示了一种贝叶斯方法，用于在复杂的医疗任务执行期间推断团队成员心智模型的失调，并通过两个模拟的基于团队的情景来说明这种方法，从中我们可以得出 75% 以上的召回率，从而为实现计算机辅助干预以增强手术室人类认知和改善团队协作提供了基本的建设性组成部分。