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.

中文翻译：

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迈向AI教练以推断医疗保健团队的心理模型一致性

共享的思维模式对于团队成功至关重要。但是，实际上，由于各种因素，团队成员的模型可能不一致。在对安全至关重要的领域（例如，航空，医疗保健），缺乏共享的心理模型可能导致可预防的错误和伤害。为了减轻此类可预防的错误，在此，我们提出一种贝叶斯方法来推断复杂医疗任务执行过程中团队成员心理模型的失调。作为示例应用程序，我们演示了使用两个基于团队的模拟场景的方法，这些场景是从心脏手术中的实际团队协作得出的。在这些模拟实验中，我们的方法可以推断出75％以上的召回率导致模型失调，从而为使计算机辅助干预能够增强手术室中的人类认知并改善团队合作提供了基础。