Using cumulative summation analysis for the learning curve of robotic docking time in radical prostatectomy with the HUGO RAS System

摘要

Minimally invasive surgery like robotic surgery is known to yield better outcomes in terms of blood loss, blood transfusion, and length of stay, and robot-assisted radical prostatectomy provides a clear example compared to open surgery. It is still constrained by issues related to platform availability and cost-effectiveness. Introducing new robotic platforms, such as the HUGO™ Robot-Assisted Surgery (RAS) System, could lead to longer operating times caused by the surgeon’s learning curve, system configuration, adjustment of robotic devices, and robotic docking. Several studies have assessed the influence of resident physicians on outcomes in urological surgeries. Our main objective was to evaluate the learning curve of the docking time for 195 radical prostatectomies performed in our hospital. The results of our research indicate that the setup and docking process with the HUGO RAS system can be accomplished with ease, and the learning curve for robotic docking is consistent with the available data for other robotic platforms. Our training facilitated a rapid docking process and seamless completion of the surgery.