| Literature DB >> 35253018 |
Sangwon Seo1, Lauren R Kennedy-Metz2, Marco A Zenati2, Julie A Shah3, Roger D Dias4, Vaibhav V Unhelkar1.
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.Entities:
Keywords: Bayesian inference; artificial intelligence; cardiac surgery; patient safety; surgical data science; teamwork
Year: 2021 PMID: 35253018 PMCID: PMC8893011 DOI: 10.1109/cogsima51574.2021.9475925
Source DB: PubMed Journal: IEEE Conf Cogn Comput Asp Situat Manag