Factors affecting the learning curve in robotic colorectal surgery

Abstract

Learning related to robotic colorectal surgery can be measured by surgical process (such as time or adequacy of resection) or patient outcome (such as morbidity or quality of life). Time based metrics are the most commonly used variables to assess the learning curve because of ease of analysis. With analysis of the learning curve, there are factors which need to be considered because they may have a direct impact on operative times or may be surrogate markers of clinical effectiveness (unrelated to times). Variables which may impact on operation time include surgery case mix, hybrid technique, laparoscopic and open colorectal surgery experience, robotic surgical simulator training, technology, operating room team, and case complexity. Multidimensional analysis can address multiple indicators of surgical performance and include variables such as conversion rate, complications, oncological outcome and functional outcome. Analysis of patient outcome and/or global assessment of robotic skills may be the most reliable methods to assess the learning curve.

Colorectal cancer surgery: is further research necessary?

“…Robotic surgery offers the advantages of 3D vision, precision, improved ergonomics, reduced infections (due to smaller incisions), less scarring and potentially shorter hospital stays [21]. Disadvantages include the operator learning curve [22], longer operating times and the fact that it is expensive.…”

Training and assessment for colorectal surgery and appendicectomy– a systematic review

“…Often, case numbers or time taken to perform a procedure are used to evaluate competency. However, this approach is too simplistic as all trainees learn at different paces [10‐12]. Mastery learning relies on a strategy where every trainee undergoes competency assessment and continues training to proficiency.…”