Learning Curves during Implementation of Robotic Stereotactic Surgery

Abstract

Introduction: Adoption of robotic techniques is increasing for neurosurgical applications. Common cranial applications include stereoelectroencephalography (sEEG) and deep brain stimulation (DBS). For surgeons to implement robotic techniques in these procedures, realistic learning curves must be anticipated for surgeons to overcome the challenges of integrating new techniques into surgical workflow. One such way of quantifying learning curves in surgery is cumulative sum (CUSUM) analysis. Methods: Here, the authors present retrospective review of stereotactic cases to perform a CUSUM analysis of operative time for robotic cases at a single institution performed by 2 surgeons. The authors demonstrate learning phase durations of 20 and 16 cases in DBS and sEEG, respectively. Results: After plateauing of operative time, mastery phases started at cases 132 and 72 in DBS and sEEG. A total of 273 cases (188 DBS and 85 sEEG) were included in the study. The authors observed a learning plateau concordant with change of location of surgery after exiting the learning phase. Conclusion: This study demonstrates the learning curve of 2 stereotactic workflows when integrating robotics as well as being the first study to examine the robotic learning curve in DBS via CUSUM analysis. This work provides data on what surgeons may expect when integrating this technology into their practice for cranial applications.