Related Experiment Video
Updated: Aug 8, 2025
Emergency Undocking in Robotic Surgery: A Simulation Curriculum
Published on: May 20, 2018
An Assessment Tool to Provide Targeted Feedback to Robotic Surgical Trainees: Development and Validation of the
Taseen F Haque1, Alvin Hui1, Jonathan You1
1Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, California.
Purpose:
To create a suturing skills assessment tool that comprehensively defines criteria around relevant sub-skills of suturing and to confirm its validity.
Materials And Methods:
5 expert surgeons and an educational psychologist participated in a cognitive task analysis (CTA) to deconstruct robotic suturing into an exhaustive list of technical skill domains and sub-skill descriptions. Using the Delphi methodology, each CTA element was systematically reviewed by a multi-institutional panel of 16 surgical educators and implemented in the final product when content validity index (CVI) reached ≥0.80. In the subsequent validation phase, 3 blinded reviewers independently scored 8 training videos and 39 vesicourethral anastomoses (VUA) using EASE; 10 VUA were also scored using Robotic Anastomosis Competency Evaluation (RACE), a previously validated, but simplified suturing assessment tool. Inter-rater reliability was measured with intra-class correlation (ICC) for normally distributed values and prevalence-adjusted bias-adjusted Kappa (PABAK) for skewed distributions. Expert (≥100 prior robotic cases) and trainee (<100 cases) EASE scores from the non-training cases were compared using a generalized linear mixed model.
Results:
After two rounds of Delphi process, panelists agreed on 7 domains, 18 sub-skills, and 57 detailed sub-skill descriptions with CVI ≥ 0.80. Inter-rater reliability was moderately high (ICC median: 0.69, range: 0.51-0.97; PABAK: 0.77, 0.62-0.97). Multiple EASE sub-skill scores were able to distinguish surgeon experience. The Spearman's rho correlation between overall EASE and RACE scores was 0.635 (p=0.003).
Conclusions:
Through a rigorous CTA and Delphi process, we have developed EASE, whose suturing sub-skills can distinguish surgeon experience while maintaining rater reliability.
