Type of publication:
Conference abstract
Author(s):
*Kawar L.; Shakir T.; *El-sayed C.
Citation:
Colorectal Disease. Conference: Association of Coloproctology of Great Britain and Ireland Annual Meeting. Harrogate United Kingdom. 27(Supplement 2) (no pagination), 2025. Date of Publication: 01 Sep 2025.
Abstract:
Purpose: The current landscape of colorectal robotic-assisted surgery (RAS) training is marked by significant variability. In order to gather opinions, a webinar was hosted by The Dukes' Club, the UK network for colorectal surgical trainees. This seeked to understand from a panel of expert RAS surgeons with various stakeholder roles in RAS training, the optimal method of delivering standardised RAS training in the UK. Method(s): This consensus study is based on a one-hour webinar held on 4th March 2024. Panellists included robotic surgery preceptors and proctors from both CMR Surgical (UK) and Intuitive (USA) respectively; members of robotic subcommittees within speciality associations, and providers of European fellowships. A thematic analysis was conducted to systematically analyse the qualitative data. Result(s): The roundtable featured two consultant urologists and three consultant colorectal surgeons. Four main themes with relevant sub-themes emerged: (1) the current state of robotic training, (2) training components of RAS, (3) challenges in delivering training, and (4) strategies for improvement. The discussion highlighted the variability in training based on geographical location and surgical speciality. Trainer readiness was discussed, with emphasis placed on the temporary nature of this. The importance of adopting RAS skills early in training with stepwise progression, was highlighted. Essential components of a standardised curriculum were identified including e-learning, simulation, and mentorship. Conclusion(s): Standardising colorectal RAS training is vital for equitable and effective skill development. Future directions include enhancing access and resource allocation, implementing stepwise certification, and integrating artificial intelligence and machine learning.
DOI: 10.1111/codi.70177
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