Learning Curve for Robotic Colorectal Surgery

Simple Summary Robotic surgery is increasingly being adopted in the field of minimally invasive colorectal surgery and has the ability to overcome the inherent drawbacks of conventional laparoscopic surgery. The adoption of new technology comes with the need to first overcome its learning curve in a safe manner without compromising on patient care. Understanding the learning curve and the factors that affect it can help institutions develop strategies to shorten it and acquire this new skill safely and efficiently. Prior experience in laparoscopic colorectal surgery, robotic surgical simulation, spending time as a bedside first assistant, along with a structured training program with proctorship were found to accelerate the process of skill acquisition. Aspiring robotic surgeons would require as little as 12 cases to attain operative efficiency and 15 cases to reduce complications.Abstract With the increasing adoption of robotic surgery in clinical practice, institutions intending to adopt this technology should understand the learning curve in order to develop strategies to help its surgeons and operating theater teams overcome it in a safe manner without compromising on patient care. Various statistical methods exist for the analysis of learning curves, of which a cumulative sum (CUSUM) analysis is more commonly described in the literature. Variables used for analysis can be classified into measures of the surgical process (e.g., operative time and pathological quality) and measures of patient outcome (e.g., postoperative complications). Heterogeneity exists in how performance thresholds are defined during the interpretation of learning curves. Factors that influence the learning curve include prior surgical experience in colorectal surgery, being in a mature robotic surgical unit, case mix and case complexity, robotic surgical simulation, spending time as a bedside first assistant, and being in a structured training program with proctorship.