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Establishing validity evidence for device-assisted advanced laparoscopic suturing tasks using simulation.

Elif Bilgic^1,2,3,4, Allan Okrainec^5,6,7, John Paige⁸

¹Steinberg Centre for Simulation and Interactive Learning, McGill University, Montreal, QC, Canada.

Surgical Endoscopy

|June 21, 2022

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This summary is machine-generated.

Advanced laparoscopic suturing tasks were validated using an automated device. Experienced surgeons significantly outperformed novices, demonstrating the tasks' potential to improve surgical training and skills.

Area of Science:

Surgical Education
Minimally Invasive Surgery
Medical Simulation

Background:

Laparoscopic suturing (LS) is a critical yet challenging skill for surgical trainees.
Advanced LS tasks were developed using an automated suturing device to address training needs.

Purpose of the Study:

To design and collect validity evidence for advanced laparoscopic suturing tasks.
To evaluate the perceived educational value of these novel training tasks.

Main Methods:

A multicentre prospective study involving novice (NS) and experienced surgeons (ES).
Participants performed suturing under tension (UT) and continuous suturing (CS) tasks.
Assessment included completion time, error rates, Global Operative Assessment of Laparoscopic Skills (GOALS), and Formative Feedback Tools (FFT).

Keywords:

Advanced laparoscopic suturing Assessment Device-assisted laparoscopic suturing Simulation

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Main Results:

Experienced surgeons showed significantly higher task scores and GOALS scores for both UT and CS tasks compared to novice surgeons (p<0.001).
Experienced surgeons also achieved significantly higher FFT scores, indicating better performance and feedback potential.
Data confirmed preliminary validity evidence for the designed advanced laparoscopic suturing tasks.

Conclusions:

The developed advanced laparoscopic suturing tasks demonstrate preliminary validity.
Incorporating these tasks into surgical curricula can enhance trainee skills.
These tasks offer a promising solution for improving advanced suturing training models.