Intelligent Interruption Management System to Enhance Safety and Performance in Complex Surgical and Robotic...

Area of Science:

• Medical procedures
• Human-computer interaction
• Cognitive science

Background:

• Procedural flow disruptions during complex medical tasks increase mental workload, leading to errors and impacting patient safety.
• Interruptions are unavoidable in healthcare, necessitating effective multitasking strategies.
• Current methods for managing interruptions do not account for real-time cognitive load.

Purpose of the Study:

• To develop an intelligent system for identifying tasks with low cognitive load during complex medical procedures.
• To enable timely and appropriate management of procedural flow interruptions.
• To enhance team performance and patient safety in high-risk environments.

Main Methods:

• Utilized heart rate variability (HRV) analysis for objective, real-time, unobtrusive measurement of cognitive load in surgical teams (surgeon, anesthesiologist, perfusionist).
• Integrated HRV data with audio-video recordings, behavioral coding, and a hierarchical surgical process model.
• Developed an interactive surgical dashboard to visualize cognitive load and identify specific tasks and substeps.

Main Results:

• Successfully identified tasks imposing low cognitive load during real-life cardiac surgeries.
• Demonstrated the ability to detect cognitive load fluctuations over time and in specific error-prone situations.
• The system provides objective data to guide an interruption management system.

Conclusions:

• The novel approach using HRV and integrated data sources can accurately identify low cognitive load periods during surgery.
• This facilitates the development of intelligent systems to manage interruptions proactively, mitigating risks associated with cognitive overload.
• Understanding cognitive load dynamics is crucial for developing error-preventing cognitive support systems in complex procedures.