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Related Experiment Video

Updated: Jul 25, 2025

Movement Retraining using Real-time Feedback of Performance

Movement Retraining using Real-time Feedback of Performance

Published on: January 17, 2013

Development of a Classification System for Live Surgical Feedback.

Elyssa Y Wong¹, Timothy N Chu¹, Runzhuo Ma¹

¹Center for Robotic Simulation and Education, Catherine and Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles.

JAMA Network Open

|June 28, 2023

Summary

A new system reliably classifies surgical feedback, including triggers, feedback types, and trainee responses. This method can enhance surgical education strategies across various specialties and experience levels.

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Area of Science:

Medical Education
Surgical Training
Health Informatics

Background:

Live intraoperative feedback is crucial for surgical skill development.
A standardized methodology for characterizing surgical feedback is currently lacking.

Purpose of the Study:

To quantify intraoperative feedback during live surgical cases.
To propose a standardized deconstruction for analyzing surgical feedback.

Main Methods:

Qualitative study with mixed methods analysis of audio-video recorded surgeries.
Involved urological residents, fellows, and attending surgeons in robotic teaching cases.
Iterative coding of transcribed feedback data to identify recurring themes.

Main Results:

A classification system for triggers, feedback, and responses demonstrated moderate to substantial interrater reliability.
Analysis of 3711 feedback instances across 6 surgical procedures showed significant differences based on surgeon experience and surgical task.
Specific feedback types, like technical feedback with a visual component, were associated with increased trainee behavioral changes or acknowledgment.

Conclusions:

A feasible and reliable method for classifying surgical feedback across robotic procedures was developed.
The proposed system is generalizable across surgical specialties and trainee experience levels.
This classification system can inform novel surgical education strategies.