A Training System for Surgeons to Safely Introduce Robotic Colectomy Using a Real-Time Annotation Tool and Hybrid Surgery: Learning Curve Evaluation via Cumulative Sum Analysis

Abstract

Introduction

Robotic surgery has gained worldwide popularity due to its versatility and clinical benefits. However, issues associated with high costs, safety, specialized training requirements, and trainee education remain. Despite several preclinical training strategies, it is necessary to train while performing surgery in order to improve surgical technique. Since most institutions lack dual-console systems, alternative training strategies must be developed.

Methods

To enhance surgical training, a real-time annotation tool and hybrid surgery—where assistant surgeons actively utilize laparoscopic instruments—was implemented in trainee-performed colectomies. Short-term clinical outcomes were compared between robotic colectomies performed by proctors and those performed by trainees. Robotic colectomy learning curves were assessed using cumulative sum (CUSUM) analysis.

Results

Between August 2021 and October 2024, 58 and 52 robotic colectomies were performed by proctors and trainee surgeons, respectively. Although no significant differences were observed in patient characteristics or clinical outcomes, operative time (265 vs. 343 min, p < 0.05) and console time (184 vs. 263.5 min, p < 0.05) were significantly longer in the trainee group. CUSUM analysis indicated that proctors required 16 cases to reach the learning phase and 33 cases to achieve mastery. The trainee learning curve displayed a short bimodal pattern, which was distinctly separated in April 2024, corresponding to between-batch transition. This suggests that the real-time annotation tool and hybrid surgery effectively maintained surgical quality.

Conclusion

The combination of a real-time annotation tool and hybrid surgery represents a safe and effective training strategy for trainee surgeons performing robotic colectomy in a single-console institution.