Towards an AI Coach to Infer Team Mental Model Alignment in Healthcare.

Journal: IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)
Published Date: Jul 9, 2021

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.

Authors

  • Sangwon Seo
    Department of Computer Science, Rice University, Houston, TX, USA.
  • Lauren R Kennedy-Metz
    Harvard Medical School and U.S. Dept. of Veterans Affairs, Boston, MA, USA.
  • Marco A Zenati
    Harvard Medical School and U.S. Dept. of Veterans Affairs, Boston, MA, USA.
  • Julie A Shah
    Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Roger D Dias
    Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
  • Vaibhav V Unhelkar
    Department of Computer Science, Rice University, Houston, TX, USA.

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