Surgeon experience and patient selection influence the learning curve in robotic inguinal hernia repair.

Abstract

Robotic-assisted laparoscopic inguinal hernia repair offers ergonomic and technical advantages over conventional approaches, but its adoption requires a learning process influenced by multiple factors. While prior surgical experience is a known determinant of performance, the impact of patient selection and hernia complexity on the learning trajectory remains insufficiently explored. This retrospective single-center study evaluated the learning curves of three surgeons with varying levels of prior open, laparoscopic, and robotic experience, who performed robotic-assisted transabdominal preperitoneal (rTAPP) inguinal hernia repairs between September 2018 and May 2023. Operative time was plotted against sequential case number to identify distinct phases in each surgeon's learning curve. Associations between clinical variables and curve phases were analyzed statistically. Additional descriptive analyses were performed for unilateral and bilateral hernias. A total of 110 consecutive patients were included. Surgeons A and B (more experienced) demonstrated two-phase learning curves, while Surgeon C (less experienced) exhibited a four-phase curve with greater variability in operative time. Differences in patient and hernia complexity across phases were most notable in Surgeon C's cases. The learning curve for rTAPP is modulated not only by prior surgical experience, but also by the evolving complexity of patient selection during training. These findings support the implementation of structured training programs that prioritize progressive case selection, beginning with less complex hernias, to ensure safe and efficient skill acquisition.