Key result
External benchmarking shows robotic right hemicolectomy requires ~35 to 55 cases to reach perioperative proficiency.
Why the study?
Current learning curve analyses in robotic right hemicolectomy rely on internal benchmarks that introduce circularity and may not reflect true surgical proficiency or clinical outcomes.
Does an external benchmark approach alter the assessment of learning curves in robotic right hemicolectomy compared to standard methods?
Cohort (n=229)
No
Does an external benchmark approach alter the assessment of learning curves in robotic right hemicolectomy compared to standard methods?
Using an external benchmark for learning curve analysis in robotic right hemicolectomy reveals a longer initial learning phase than previously recognized, suggesting a need for clinically driven proficiency assessments.
External benchmarking may indicate longer learning curves than internal CUSUM estimates; challenges prior consensus and leaves open prospective validation.
Learning curves in robotic right hemicolectomy (RRH) are assessed using cumulative sum (CUSUM) analysis. CUSUM was originally designed for industrial processes using external benchmarks. Medical applications rely on internal benchmarks, typically the cohort-specific median. This introduces circularity, obscures early deviations and distorts inflection points. In addition, phase definitions are based on procedural metrics with limited linkage to clinical outcomes. Current models may not reflect surgical proficiency. Data were drawn from a prospectively maintained institutional database covering RRH procedures performed between 2010 and 2025; after exclusions, 229 cases remained for analysis. Cases were split into a training group (TG; n = 164) and a control group (CG; n = 65), the latter comprising procedures by two surgeons already in their mastery phase and used as an external benchmark (EB). Learning-curve analysis of RRH referenced against this EB formed the primary endpoint, while RA-CUSUM analysis and perioperative outcome parameters were assessed as secondary endpoints. Application of the external benchmark approach revealed a prolonged initial learning phase (approximate transition zone 35-55 cases). Major complications, conversion rate, blood loss, and length of stay showed the steepest changes initially, with stabilization thereafter, while lymph node yield reached an early plateau. Operative time demonstrated a continuous increase throughout. Compared with previously reported analyses, learning phases were extended and showed different patterns when clinical outcomes were considered. The prolonged first learning phase for RRH indicates delayed perioperative proficiency. Learning curve analysis should shift to a clinically driven approach with external benchmark methodology. Benchmark values are institution-specific; the methodology may serve as a conceptual framework for future analyses.
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Wilk et al. (2026) conducted a cohort in Right-sided colon cancer (n=229). Robotic right hemicolectomy learning curve vs. External benchmark (mastery phase surgeons) was evaluated on Learning-curve analysis of RRH applying the external benchmark approach with correlation to clinical outcomes. Application of an external benchmark approach to robotic right hemicolectomy revealed a prolonged initial learning phase, with an approximate transition zone of 35 to 55 cases required to attain perioperative proficiency.
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