Development and evaluation of a training module for the clinical introduction of the da Vinci robotic system in visceral and vascular surgery

Abstract

Background: With the increasing use of the surgical robotic system in the clinical arena, appropriate training programs and assessment systems need to be established for mastery of this new technology. The authors aimed to design and evaluate a clinic-like training program for the clinical introduction of the da Vinci robotic system in visceral and vascular surgery.

Methods: Four trainees with different surgical levels of experience participated in this study using the da Vinci telemanipulator. Each participant started with an initial evaluation stage composed of standardized visceral and vascular operations (cholecystectomy, gastrotomy, anastomosis of the small intestine, and anastomosis of the aorta) in a porcine model. Then the participants went on to the training stage with the rat model, performing standardized visceral and vascular operations (gastrotomy, anastomosis of the large and small intestines, and anastomosis of the aorta) four times in four rats. The final evaluation stage was again identical to the initial stage. The operative times, the number of complications, and the performance quality of the participants were compared between the two evaluation stages to assess the impact of the training stage on the results.

Results: The operative times in the final evaluation stage were considerably shorter than in the initial evaluation stage and, except for cholecystectomies, all the differences reached statistical significance. Also, significantly fewer complications and improved quality for each operation in the final evaluation stage were documented, as compared with their counterparts in the initial evaluation stage. These improvements were recorded at each level of experience.

Conclusions: The presented experimental small and large animal model is a standardized and reproducible training method for robotic surgery that allows evaluation of the surgical performance while shortening and optimizing the learning-curve.