Towards an AI Coach to Infer Team Mental Model Alignment in Healthcare
Name
2102.08507.pdf
Description
Accepted version
Size
977.32 KB
Format
Adobe PDF
Checksum (MD5)
45ef59ce11dc2ce32adf4958f7d48bea
Author(s)
Seo, Sangwon
Kennedy-Metz, Lauren R
Zenati, Marco A
Shah, Julie A
Dias, Roger D
Unhelkar, Vaibhav V
Date Issued
May 14, 2021
Journal
2021 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)
Publisher
IEEE
Citation
Seo, Sangwon, Kennedy-Metz, Lauren R, Zenati, Marco A, Shah, Julie A, Dias, Roger D et al. 2021. "Towards an AI Coach to Infer Team Mental Model Alignment in Healthcare." 2021 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA), 2021.
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/cogsima51574.2021.9475925
