Learning curve for robot-assisted knee arthroplasty; optimizing the learning curve to improve efficiency.

Abstract

The introduction of robot-assisted (RA) systems in knee arthroplasty has challenged surgeons to adopt the new technology in their customized surgical techniques, learn system controls, and adjust to automated processes. Despite the potential advantages of RA knee arthroplasty, some surgeons remain hesitant to adopt this novel technology owing to concerns regarding the cumbersome adaptation process. This narrative review addresses the learning-curve issues in RA knee arthroplasty based on the existing literature. Learning curves exist in terms of the operative time and stress level of the surgical team but not in the final implant positions. The factors that reduce the learning curve are previous experience with computer-assisted surgery (including robot or navigation systems), specialization in knee surgery, high volume of knee arthroplasty, optimization of the RA workflow, sequential implementation of RA surgery, and consistency of the surgical team. Worse clinical outcomes may occur in the early postoperative period, but not in the later period, in RA knee arthroplasty performed during the learning phase. No significant differences were observed in implant survival or complication rates between the RA knee arthroplasties performed during the learning and proficiency phases.