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

2020 ◽  
Author(s):  
I-Hsuan Alan Chen ◽  
Ahmed Ghazi ◽  
Ashwin Sridhar ◽  
Danail Stoyanov ◽  
Mark Slack ◽  
...  
Keyword(s):  
Machine Learning ◽  
Robotic Surgery ◽  
Performance Feedback ◽  
New Technologies ◽  
Current Evidence ◽  
Robotic Training ◽  
Novel Technologies ◽  
Surgery Training ◽  
Consensus Statements ◽  
Robotic Surgery Training

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.

Robotic Surgery Training

2021 ◽  
Vol 48 (1) ◽  
pp. 137-146
Author(s):  
Robert S. Wang ◽  
Sapan N. Ambani
Keyword(s):  
Robotic Surgery ◽  
Surgery Training ◽  
Robotic Surgery Training

A National Survey of Robotic Surgery Training Among Otolaryngology—Head and Neck Surgery Residents

2021 ◽  
pp. 000348942199696
Author(s):  
Hilary C. McCrary ◽  
Sierra R. McLean ◽  
Abigail Luman ◽  
Patricia O’Sullivan ◽  
Brigitte Smith ◽  
...  
Keyword(s):  
Robotic Surgery ◽  
Head And Neck ◽  
National Survey ◽  
Neck Surgery ◽  
The United States ◽  
Head And Neck Surgery ◽  
Residency Programs ◽  
Surgery Training ◽  
Robotic Surgery Training ◽  
Operative Experience

Objective: The aim of this study is to describe the current state of robotic surgery training among Otolaryngology—Head and Neck Surgery (OHNS) residency programs in the United States. Methods: This is a national survey study among OHNS residents. All OHNS residency programs were identified via the Accreditation Council for Graduate Medical Education website. A total of 64/127 (50.3%) of OHNS programs were selected based on a random number generator. The main outcome measure was the number of OHNS residents with access to robotic surgery training and assessment of operative experience in robotic surgery among those residents. Results: A total of 140 OHNS residents participated in the survey, of which 59.3% (n = 83) were male. Response rate was 40.2%. Respondents came from middle 50.0% (n = 70), southern 17.8% (n = 25), western 17.8% (n = 25), and eastern sections 14.3% (n = 20). Most respondents (94.3%, n = 132) reported that their institution utilized a robot for head and neck surgery. Resident experience at the bedside increased in the junior years of training and console experience increased across the years particularly for more senior residents. However, 63.4% of residents reported no operative experience at the console. Only 11.4% of programs have a structured robotics training program. Conclusion: This survey indicated that nearly all OHNS residencies utilize robotic surgery in their clinical practice with residents receiving little formal education in robotics or experience at the console. OHNS residencies should aim to increase access to training opportunities in order to increase resident competency. Level of Evidence: IV

751 Multi-institutional validation of the ERUS robotic surgery training curriculum

2014 ◽  
Vol 13 (1) ◽  
pp. e751-e751a ◽  
Author(s):  
A. Volpe ◽  
K. Ahmed ◽  
P. Dasgupta ◽  
V. Ficarra ◽  
H. Van Der Poel ◽  
...  
Keyword(s):  
Robotic Surgery ◽  
Training Curriculum ◽  
Surgery Training ◽  
Robotic Surgery Training

Development and Implementation of a Robotic Surgery Training Curriculum

2021 ◽  
Vol 28 (11) ◽  
pp. S98
Author(s):  
M. Aioub ◽  
T. Gee ◽  
O. Mutter ◽  
K. Harmon ◽  
A. Abdo ◽  
...  
Keyword(s):  
Robotic Surgery ◽  
Training Curriculum ◽  
Surgery Training ◽  
Robotic Surgery Training

scholarly journals Training in robotic surgery, replicating the airline industry. How far have we come?

2019 ◽  
Vol 38 (7) ◽  
pp. 1645-1651 ◽  
Author(s):  
Justin William Collins ◽  
Pawel Wisz
Keyword(s):  
Robotic Surgery ◽  
Surgical Training ◽  
Airline Industry ◽  
International Standards ◽  
Current Evidence ◽  
Aviation Industry ◽  
Pilot Training ◽  
Practical Training ◽  
Robotic Training ◽  
Aviation Training

Abstract Introduction As the role of robot-assisted surgery continues to expand, development of standardised and validated training programmes is becoming increasingly important. We aim to compare current robotic training curricula with training in aviation, to evaluate current similarities and to provide insight into how healthcare can further learn from replicating initiatives in aviation training. Methods A systematic literature review of the current evidence was conducted online and relevant publications and information were identified. Evaluation and comparison between training in robotic surgery and the aviation industry was performed. Results There are significant similarities between modern robotic training curricula and pilot training. Both undergo basic training before proceeding to advanced training. Aviation training methods include classroom instruction, e-learning and practical training, in both the aircraft and flight simulation training devices. Both surgeon and pilot training include technical and procedural instruction as well as training in non-technical skills such as crisis management, decision making, leadership and communication. However, there is more regulation in aviation, with international standards for training curricula, simulation devices and instructors/trainers that are legally binding. Continuous learning with re-qualification with benchmarked high stakes tests are also mandatory throughout a pilot’s and instructor’s career. Conclusion Robotic surgeons and pilots roles have many fundamental similarities. Both work with expensive and complex technology requiring high levels of skills, within working environments with high physiological and psychological stress levels. Whilst many initiatives in aviation training have already been replicated in surgical training there remain considerable differences in regulation. Adopting established and proven aviation methods of assessment and regulation could help robotic surgical training become more efficient, more effective and ultimately safer.

scholarly journals Utilising an Accelerated Delphi Process to Develop Guidance and Protocols for Telepresence Applications in Remote Robotic Surgery Training

2020 ◽  
Vol 22 ◽  
pp. 23-33
Author(s):  
Justin W. Collins ◽  
Ahmed Ghazi ◽  
Danail Stoyanov ◽  
Andrew Hung ◽  
Mark Coleman ◽  
...  
Keyword(s):  
Robotic Surgery ◽  
Delphi Process ◽  
Surgery Training ◽  
Robotic Surgery Training
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