Towards an AI Coach to Infer Team Mental Model Alignment in Healthcare
Seo, Sangwon, Kennedy-Metz, Lauren R., Zenati, Marco A., Shah, Julie A., Dias, Roger D., Unhelkar, Vaibhav V.
–arXiv.org Artificial Intelligence
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.org Artificial Intelligence
Feb-16-2021
- Country:
- North America > United States > Massachusetts > Middlesex County > Cambridge (0.14)
- Genre:
- Research Report (0.40)
- Industry:
- Health & Medicine
- Surgery (1.00)
- Therapeutic Area > Cardiology/Vascular Diseases (0.70)
- Health & Medicine