Implementation of the Hugo Robotic System: Early Outcomes and Learning Curves in Hysterectomy by Surgeons With and Without Prior Robotic Experience

PURPOSE: To characterize early learning curves for two gynecologic oncologists and their first assistants using the Hugo robotic-assisted surgery system for hysterectomy in benign uterine disease or FIGO stage IA endometrial cancer. METHODS: We retrospectively examined the first 43 Hugo hysterectomies performed at our center by two surgeons: Surgeon A (experienced with da Vinci) and Surgeon B (robotics-naïve), assisted by three primary assistants (A, B, and C). We analyzed baseline patient characteristics, perioperative outcomes (operative time, docking time, console time, blood loss, complications, length of stay), and plotted learning curves using operative time trends and CUSUM analysis. Surgeon and assistant group comparisons used t-test or Kruskal-Wallis and chi-square as appropriate, with p < 0.05 considered significant. Complications graded ≥ Clavien-Dindo II were considered notable. RESULTS: Patient demographics were similar between groups. Surgeon A achieved significantly shorter operative times (128.6 ± 23.7 vs. 149.8 ± 19.6 min, p = 0.003) and console times (90.9 ± 20.4 vs. 115.6 ± 18.9 min, p < 0.001) versus Surgeon B. Docking times did not differ significantly. No conversions occurred, and complication rates were low and comparable (4% vs. 11%, p = 0.56). CUSUM analysis revealed that Surgeon A's operative times stabilized by case 5, while Surgeon B required approximately 15 cases to reach comparable proficiency. Assistants demonstrated decreasing docking times, with no significant differences among groups. CONCLUSIONS: In early Hugo RAS adoption, prior robotic experience led to a shorter learning curve, but robotics-naïve surgeons achieved proficiency within ~15 cases without compromising safety. Assistants also rapidly mastered docking. These findings support safe and efficient implementation of new robotic platforms with structured training.