UPJ INSIGHT: An Assessment Tool to Provide Targeted Feedback to Robotic Surgical Trainees: Development and Validation of the End-to-End Assessment of Suturing Expertise (EASE)

By: Taseen F. Haque, BA; Alvin Hui, BS; Jonathan You; Runzhuo Ma, MD; Jessica H. Nguyen; Xiaomeng Lei, MPH; Steven Cen, PhD; Monish Aron, MD; Justin W. Collins, MD; Hooman Djaladat, MD; Ahmed Ghazi, MD; Kenneth A. Yates, EdD; Andre L. Abreu, MD; Siamak Daneshmand, MD; Mihir M. Desai, MD; Alvin C. Goh, MD; Jim C. Hu, MD; Amir H. Lebastchi, MD; Thomas S. Lendvay, MD; James Porter, MD; Anne K. Schuckman, MD; Rene Sotelo, MD; Chandru P. Sundaram, MD; Inderbir S. Gill, MD; Andrew J. Hung, MD | Posted on: 01 Dec 2022

Haque TF, Hui A, You J, et al. An assessment tool to provide targeted feedback to robotic surgical trainees: development and validation of the End-to-End Assessment of Suturing Expertise (EASE). Urol Pract. 2022; 9(6):532-539.

Study Need and Importance

Surgical training has evolved from an apprenticeship model to one with more objective assessment. With this, there has been an emergence of validated skills assessment tools that help quantify technical skills; differing levels of technical skills have been shown to impact surgical outcomes. We propose that a granular skills assessment is needed to provide trainees with an objective evaluation of their aptitude, while also providing trainees with targeted insights. In this study, we created a suturing skills assessment tool, the End-to-End Assessment of Suturing Expertise (EASE), that exhaustively maps out and defines criteria around relevant subskills of robotic suturing, and confirmed its validity.

What We Found

Through a rigorous cognitive task analysis and Delphi process, we developed EASE. Our expert panel agreed on 7 domains, 18 subskills, and 57 detailed subskill descriptions that encompassed EASE (see Table). Inter-rater reliability was moderately high, and multiple EASE subskill scores were able to distinguish surgeon experience.

Limitations

A considerable level of effort was required to train reviewers and manually assess EASE scores. In the future, EASE will include the automation of surgical skills assessment to assist with workload. Furthermore, our study only discerned between surgeons based on 2 levels of experience and did not consider complete novices. Additionally, prospective study is needed to explore the effect of providing feedback through EASE on a trainee’s suturing skill acquisition and postoperative functional outcomes.

Interpretations for Patient Care

As the complexity of surgical approaches and technologies increases, those charged with educating the future generation of surgeons must continue to elevate the methods for surgical skills acquisition and maintenance. Providing feedback for robotic surgical trainees can improve performance and is important for widely used skills such as suturing. Through a rigorous process, we have developed EASE, whose granular suturing subskills can distinguish surgeon experience and provide the most targeted feedback for robotic suturing trainees.

Table. Summary of EASE Domains and Subskills

EASE domain	EASE subskill
Pre-planning	Field optimization
Needle handling	Needle repositions
	Hold ratio
	Hold angle
	Depth of needle hold
Needle entry	Entry angle
Needle driving	Driving smoothness
	Driving wrist rotation
	Depth of suture
Needle withdrawal	Withdrawal wrist rotation
Suture placement and management	Suture spacing
	Suture awareness
	Cinching
	Tissue approximation
Knot tying	Tie length
	Preparation
	Knot tension
	Secure/air knot
There was a significant difference between experts and trainees.