National learning curves among robotic thoracic surgeons in the United States: Quantifying the impact of procedural experience on efficiency and productivity gains

Objective: This study aims to characterize the aggregate learning curves of US surgeons for robotic thoracic procedures and to quantify the impact on productivity. Methods: National average console times relative to cumulative case number were extracted from the My Intuitive application (Version 1.7.0). Intuitive da Vinci robotic system data for 56,668 lung resections performed by 870 individual surgeons between 2021 and 2022 were reviewed. Console time and hourly productivity (work relative value units/hour) were analyzed using linear regression models. Results: Average console times improved for all robotic procedures with cumulative case experience (P = .003). Segmentectomy and thymectomy had the steepest initial learning curves with a 33% and 34% reduction of the average console time for proficient (51-100 cases) relative to novice surgeons (1-10 cases), respectively. The hourly productivity increase for proficient surgeons ranged from 11.4 work relative value units/hour (+26%) for lobectomy to 17.0 work relative value units/hour (+50%) for segmentectomy. At the expert level (101+ cases), average console times continued to decrease significantly for esophagectomy (-18%) and lobectomy (-23%), but only minimally for wedge resections (-1%) (P = .003). The work relative value units/hour increase at the expert level reached 50% for lobectomy and 40% for esophagectomy. Surgeon experience level, dual console use, system model, and robotic stapler use were factors independently associated with console time for robotic lobectomy. Conclusions: The aggregate learning curve for robotic thoracic surgeons in the United States varies significantly by procedure type and demonstrate continued improvements in efficiency beyond 100 cases for lobectomy and esophagectomy. Improvements in efficiency with growing experiences translate to substantial productivity gains.