Towards an AI Coach to Infer Team Mental Model Alignment in Healthcare
This addresses preventable errors in safety-critical healthcare teams, though it is incremental as it builds on existing concepts with a new method.
The paper tackled the problem of misaligned team mental models in healthcare by developing a Bayesian approach to infer misalignment during complex tasks, achieving over 75% recall in simulated cardiac surgery scenarios.
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.