Learning Curve Analysis of Robotic-Assisted Total Hip Arthroplasty: A Retrospective Study

Background: Robotic-assisted total hip arthroplasty (RA-THA) may improve component positioning but adds workflow demands during adoption. We evaluated the learning curve for total operative and robotic workflow time during the initial implementation of a CT-based robotic system. Methods: We retrospectively analyzed 108 consecutive adults undergoing primary RA-THA between January 2024 and June 2025. We recorded skin-to-skin and robotic workflow times for each procedure. Segmented regression was fitted to individual procedure times, with model selection by Bayesian information criterion. We used CUSUM sensitivity analyses and LOESS smoothing to describe time trends. Results: For both endpoints, a one-breakpoint model was favored, with the estimated change in slope at case 12. Mean skin-to-skin time was 80.8 ± 24.9 min in cases 1–12 and 48.1 ± 12.7 min in cases 13–108. Corresponding robotic workflow times were 46.3 ± 18.8 and 28.2 ± 9.7 min. The CUSUM peak varied with the reference value, and the analysis of individual times did not support a distinct late optimization phase. Conclusions: Operative times decreased most markedly during the first approximately 12 cases and more gradually thereafter. These findings describe operative efficiency in a single-surgeon cohort; further studies should examine clinical outcomes and component-positioning accuracy alongside time trends.