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Conception and prospective multicentric validation of a Robotic Surgery Training Curriculum (RoSTraC) for surgical residents: from simulation via laboratory training to integration into the operation room.

Journal of robotic surgery

Authors: Michael Thomaschewski, Markus Kist, Markus Zimmermann, Claudia Benecke, Jörg C Kalff, Colin M Krüger, Benno Mann, Andreas Türler, Tobias Keck, Richard Hummel

There is a lack of training curricula and educational concepts for robotic-assisted surgery (RAS). It remains unclear how surgical residents can be trained in this new technology and how robotics can be integrated into surgical residency training. The conception of a training curriculum for RAS addressing surgical residents resulted in a three-step training curriculum including multimodal learning contents: basics and simulation training of RAS (step 1), laboratory training on the institutional robotic system (step 2) and structured on-patient training in the operating room (step 3). For all three steps, learning content and video tutorials are provided via cloud-based access to allow self-contained training of the trainees. A prospective multicentric validation study was conducted including seven surgical residents. Transferability of acquired skills to a RAS procedure were analyzed using the GEARS score. All participants successfully completed RoSTraC within 1 year. Transferability of acquired RAS skills could be demonstrated using a RAS gastroenterostomy on a synthetic biological organ model. GEARS scores concerning this procedure improved significantly after completion of RoSTraC (17.1 (±5.8) vs. 23.1 (±4.9), p < 0.001). In step 3 of RoSTraC, all participants performed a median of 12 (range 5-21) RAS procedures on the console in the operation room. RoSTraC provides a highly standardized and comprehensive training curriculum for RAS for surgical residents. We could demonstrate that participating surgical residents acquired fundamental and advanced RAS skills. Finally, we could confirm that all surgical residents were successfully and safely embedded into the local RAS team.

© 2024. The Author(s).

PMID: 38280113

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