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Use of Automated Performance Metrics to Measure Surgeon Performance during Robotic Vesicourethral Anastomosis and
Jian Chen1, Paul J Oh1, Nathan Cheng1
1Center for Robotic Simulation and Education, Catherine and Joseph Aresty Department of Urology, University of Southern California Institute of Urology, University of Southern California, Los Angeles, California; Medical Research, Intuitive Surgical, Inc. (AJ, LG), Norcross, Georgia.
The Journal of Urology
|May 25, 2018
Summary
Automated performance metrics effectively differentiate surgeon expertise in robotic vesicourethral anastomosis. An expert technique demonstrated superior efficiency and reduced tissue trauma, highlighting the value of standardized training for improving surgical skills.
Area of Science:
- Robotics in Surgery
- Surgical Performance Metrics
- Urologic Oncology
Background:
- Robotic-assisted radical prostatectomy (RARP) is a standard treatment for prostate cancer.
- Vesicourethral anastomosis (VUA) is a critical step in RARP, requiring precise surgical technique.
- Objective measurement of surgeon skill in VUA is needed for effective training.
Purpose of the Study:
- To develop and validate automated performance metrics for assessing VUA during RARP.
- To create a standardized training tutorial for robotic VUA based on expert techniques.
Main Methods:
- Automated performance metrics (motion tracking, system events, video) were captured during RARP.
- Non-automated metrics were manually annotated; both were compared between expert and novice surgeons.
- Task deconstruction, cognitive task analysis, and Delphi methodology were used to develop the VUA tutorial.
Main Results:
- Automated metrics showed experts outperformed novices in completion time, articulation, movement efficiency, and camera manipulation (p <0.01-0.03).
- Experts exhibited better needle handling, fewer attempts, optimal needle entry, and less tissue trauma (p <0.01).
- A 14-gesture classification revealed random gestures were linked to lower efficiency and more trauma; a 66-statement tutorial was finalized via expert consensus.
Conclusions:
- Automated performance metrics can reliably distinguish surgeon expertise in VUA.
- Expert VUA technique is characterized by enhanced efficiency and minimized tissue trauma.
- Standardized robotic VUA training, supported by a methodical tutorial, can improve surgical education and patient outcomes.
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