Quantitative Objective Assessment of Preoperative Warm-up for Robotic Surgery

Abstract

Here I present the application of three established methods for quantitatively and objectively assessing robotic surgical performance as well the development and application of a fourth. These four tools are used to assess the hypothesis that a certain surgical warm-up protocol improves performance of surgeons on a da Vinci robotic surgical system. In the protocol, surgeons perform a brief warm-up task on the Mimic dV-Trainer virtual reality simulator prior to performing one of two robotic surgery practice tasks. Of the four techniques used for performance assessment, the three established techniques consist of basic measures (task time, tool path length, economy of motion and errors), algorithmic assessment (using trained Hidden Markov Model machine learning algorithms) and surgeon assessment (using the Global Evaluative Assessment of Robotics Surgery). The newly proposed technique called Crowd-Sourced Assessment of Technical Skill (C-SATS) draws on crowds of people on the Internet to assess the surgical performance. The evidence that warm-up improves surgical performance is presented as well as an analysis of the strong agreement between C-SATS and grades provided by a group of surgeons trained to assess surgical performance.