Robotic Olympics: A novel robotic surgical training experience for residents in an obstetrics and gynecology residency program

Autor: Dennis Y. S. Kuo, Eric C Liberman, Malte Renz, Brian R. Daniels, Sara Isani, Nicole S. Nevadunsky
Jazyk: angličtina
Rok vydání: 2018
Předmět:
DOI: 10.7287/peerj.preprints.3510v2
Popis: Background: Resident experience and opinions regarding robotic surgical training as part of the formal obstetrics and gynecology curriculum has not been reported. Objective: To evaluate residents’ experience with the newly introduced Robotic Olympics and a robotic surgical trainings curriculum in general, especially in correlation with future career goals. Methods: All residents of the Obstetrics and Gynecology Residency Program at the Montefiore Medical Center, who participated in the Robotic Olympics 2014, a team-based simulation competition, completed a de-identified pre- and post-Olympics survey. Results: For the participating 31 residents, the mean number of bedside-assistant robotic and console cases was 8 (0-50) and 4 (0-30), respectively. Both were positively associated with postgraduate level. The majority of residents (89%) reported that they were best trained in open surgery. Only 52% anticipated using robotic surgery in their future practice. Anticipated use of the robot and interest in robotic training were correlated with surgical subspecialty career goals. 100% of residents aspiring a career in gynecologic oncology and none interested in maternofetal medicine anticipated future use of robotic surgery. However, all residents desired the Robotic Olympics to be integral part of resident education. Conclusions: The majority of residents welcomed the addition of the Robotic Olympics to the robotic-surgical curriculum. However, the residents’ interest in robotic surgical training in general was disparate and correlated with the anticipated use of the robot in the residents’ future career. This data suggests the need for directed robotic surgical training for residents interested in surgical sub-specialties to focus resources early on.
Databáze: OpenAIRE