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

TL;DR

This paper introduces a Bayesian method to detect misaligned team mental models in healthcare, aiming to reduce errors by improving team coordination during complex medical procedures.

Contribution

It presents a novel Bayesian approach for inferring mental model misalignment in healthcare teams, demonstrated through simulated cardiac surgery scenarios.

Findings

Achieved over 75% recall in detecting model misalignment.

Validated approach using simulated team-based healthcare scenarios.

Provides a foundation for computer-assisted interventions in operating rooms.

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.