Abstract Robotic-assisted Total Knee Arthroplasty (RA-TKA) enhances precision but is historically associated with increased operative times and workflow disruption. This study evaluates whether integrating a novel open-platform robotic effector into an established, familiar navigation workflow mitigates the learning curve and maintains total operative time compared to standard navigation. A retrospective comparative analysis was performed on 236 primary TKAs (142 NAV-TKA vs. 94 RA-TKA) performed by a single high-volume surgical team. Operative times were segmented into five phases using synchronized system logs. Educational cases were excluded. The learning curve was analyzed using Cumulative Sum (CUSUM) control charts. Multi-way analysis across independent cohorts showed a significant global variation in total skin-to-skin time ( p 0.017), avoiding an overall sustained time penalty. CUSUM analysis identified a learning curve of 58 cases. In the proficiency phase, the active robotic resection time was significantly faster than manual navigated resection (17.95 vs. 19.08 min; p = 0.009). The open-platform robotic system successfully integrated into the clinical workflow without introducing an overall sustained time penalty. Retaining a familiar interface effectively cushions the initial efficiency loss typical of closed platforms. Once proficiency is attained, active robotic assistance significantly enhances mechanical resection speed, offsetting the mandatory intra-operative planning time investment.
Maggi et al. (Fri,) studied this question.
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