Implementation and learning curve of an advanced imageless navigation system in total hip arthroplasty

Background Understanding the learning curve for technology-assisted total hip arthroplasty (THA) is critical for healthcare systems investing in robotic platforms and for ensuring patient safety during the learning phase. The aim of this study was to define the learning curve of operative time metrics, postoperative precision in limb length discrepancy (LLD) and offset discrepancy, and computer system complications during implementation of an advanced navigation system implemented with dynamic spinopelvic parameters. Hypothesis We hypothesized that surgeons adopting TA-THA would demonstrate progressive gains in operative efficiency, reaching proficiency within a quantifiable case volume identifiable through statistical analysis. Patients and methods We reviewed consecutive patients who underwent primary THA performed using the RI.HIP NAVIGATION system (Smith & Nephew), combined with preoperative planning based on spinopelvic parameters through the MODELER software (Smith & Nephew), at a single institution between 2023 and 2024. Adults aged ≥18 years treated for hip osteoarthritis or femoral head osteonecrosis were included. Data on skin-to-skin and computer-assisted system time and limb length discrepancy and offset discrepancy were collected. Primary outcomes were learning curves for operative time metrics and case volume required for proficiency. Secondary outcomes were associations between surgical precision metrics and surgeon experience. Statistical analyses included segmented regression, CUSUM analysis, rolling window analysis, and multivariable linear regression. Results 111 consecutive cases were analyzed for time-based metrics; 109 cases (98.2%) had complete femoral measurement data. CUSUM analysis identified initial proficiency at case 15 for computer registration time and case 17 for overall surgery time, with sustained proficiency achieved at case 38 and case 43, respectively. After sustained proficiency achievement, computer registration time improved 21.3% (19.7 vs 15.5 min, P < 0.001) and overall surgery time improved 8.2% (86.0 vs 78.9 min, P < 0.001). Both LLD precision (R-squared = 0.6, P < 0.001) and offset precision (R-squared = 0.3, P < 0.001) improved significantly over time. Computer system bugs accelerated learning through significant case number interactions (β = −0.49 min per case, P < 0.001). Discussion Proficiency in advanced navigation THA requires 15–17 cases, substantially exceeding previously reported thresholds and challenging current proficiency assessment methodologies. Level of evidence III.