Evolving robotic surgery training and improving patient safety, with the integration of novel technologies.


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Authors
Ghazi, Ahmed 
Sridhar, Ashwin 
Stoyanov, Danail 
Slack, Mark 
Abstract

INTRODUCTION: Robot-assisted surgery is becoming increasingly adopted by multiple surgical specialties. There is evidence of inherent risks of utilising new technologies that are unfamiliar early in the learning curve. The development of standardised and validated training programmes is crucial to deliver safe introduction. In this review, we aim to evaluate the current evidence and opportunities to integrate novel technologies into modern digitalised robotic training curricula. METHODS: A systematic literature review of the current evidence for novel technologies in surgical training was conducted online and relevant publications and information were identified. Evaluation was made on how these technologies could further enable digitalisation of training. RESULTS: Overall, the quality of available studies was found to be low with current available evidence consisting largely of expert opinion, consensus statements and small qualitative studies. The review identified that there are several novel technologies already being utilised in robotic surgery training. There is also a trend towards standardised validated robotic training curricula. Currently, the majority of the validated curricula do not incorporate novel technologies and training is delivered with more traditional methods that includes centralisation of training services with wet laboratories that have access to cadavers and dedicated training robots. CONCLUSIONS: Improvements to training standards and understanding performance data have good potential to significantly lower complications in patients. Digitalisation automates data collection and brings data together for analysis. Machine learning has potential to develop automated performance feedback for trainees. Digitalised training aims to build on the current gold standards and to further improve the 'continuum of training' by integrating PBP training, 3D-printed models, telementoring, telemetry and machine learning.

Description

Funder: University College London (UCL)

Keywords
3D printed models, Eye tracking, Machine learning, Patient safety, Proficiency-based progression, Robotic-assisted surgery, Surgical education, Telementoring, Training, Education, Education, Distance, General Surgery, Humans, Inventions, Mentoring, Models, Anatomic, Patient Safety, Robotic Surgical Procedures, Urology
Journal Title
World J Urol
Conference Name
Journal ISSN
0724-4983
1433-8726
Volume Title
39
Publisher
Springer Science and Business Media LLC