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Updated: Jul 5, 2025
Emergency Undocking in Robotic Surgery: A Simulation Curriculum
Published on: May 20, 2018
A dedicated robotic bedside physician assistant significantly enhances trainee console operating time in general
Benjamin T Jones1, Jinny S Ha2, Chuck Lawrence2
1Department of Surgery, The Johns Hopkins Medical Institutions, Baltimore, Md.
Objective:
As trainees rotate through thoracic subspecialties within their curricula, a crucial portion of their robotic training consists of actual console operating time. The more time spent on the surgeon console, the greater the development will be through the course of their training. Implementing a physician assistant at the bedside may increase the operative console time for the trainee and develop robotic skills in a more expeditious rate. The objective was to evaluate the impact a designated robotic physician assistant can have on trainee console learning opportunity.
Methods:
Operating room data collected consisted of all robotic general thoracic surgical cases that trainees participated in with and without a physician assistant present. Metrics regarding case efficiency included anesthesia ready-to-incision, incision-to-console, and raw resident console times. By using PRISM software, a nonparametric t test was used to analyze each averaged data group compared between when a physician assistant was present and not present.
Results:
The mean resident console time without and with a physician assistant assist was 45.8 minutes and 80.9 minutes, respectively (P < .0001). The average portion of a case performed by a trainee similarly without and with a physician assistant present was 28.0% and 77.1%, respectively (P < .0001). Case efficiency metrics between physician assistant presence cohorts showed no difference.
Conclusions:
Thoracic surgical trainees have increased opportunity for robotic skill development within a fellowship or resident program curriculum when a designated robotic physician assistant is present in the operating room. These findings are significant for the improvement of residency and fellowship robotic training models moving forward by incorporating robotic-specialized physician assistants in academic institutions.
