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

Area of Science:

• Orthopedic Surgery
• Robotics in Medicine
• Surgical Technology

Background:

• Robot-assisted (RA) systems are increasingly used in knee arthroplasty.
• Surgeons face challenges adapting to RA technology, including system controls and automated processes.
• Hesitancy exists due to concerns about the adaptation process, despite potential benefits.

Purpose of the Study:

• To review the existing literature on the learning curve associated with robot-assisted knee arthroplasty.
• To identify factors influencing the learning curve in RA knee arthroplasty.
• To evaluate the impact of the learning phase on clinical outcomes and implant survival.

Main Methods:

• This study is a narrative review of existing literature.
• The review focuses on learning-curve issues in robot-assisted knee arthroplasty.
• Analysis of factors influencing the learning curve and outcomes during the learning phase.

Main Results:

• Learning curves are evident in operative time and surgical team stress, but not in final implant positioning.
• Factors reducing the learning curve include prior computer-assisted surgery experience, knee surgery specialization, high arthroplasty volume, optimized workflow, sequential implementation, and team consistency.
• Worse early postoperative outcomes may occur during the learning phase, but no significant differences in implant survival or complication rates are observed between learning and proficiency phases.

Conclusions:

• The learning curve in robot-assisted knee arthroplasty primarily impacts process metrics like operative time and stress, not surgical accuracy.
• Experience, workflow optimization, and team stability are key to mitigating the learning curve.
• Robot-assisted knee arthroplasty demonstrates comparable long-term implant survival and complication rates regardless of the surgeon's learning phase.