The learning curve associated with robotic-assisted shoulder arthroplasty: A feasibility study.

INTRODUCTION: Robotic-assisted reverse shoulder arthroplasty is a new technology which could lead to more accurate implant positioning and potentially improved longevity and outcomes. Cognitive workload and learning curve must be considered when introducing new technologies into the operating theatre.

MATERIALS AND METHODS: The first 20 patients who received a MAKO robotic shoulder arthroplasty performed by a single surgeon during limited release access to this technology were evaluated. The surgeon learning curve was assessed in the domains of operative time and via the NASA Task Load Index (NASA-TLX) score.

RESULTS: Nineteen cases were available for analysis (one case was performed for fracture and thus excluded). There was no correlation between operative time and NASA-TLX score as the cases progressed (p = .42, with poor correlation ρ = -0.198). The NASA-TLX score showed the learning curve breakpoint between three and five cases across the six domains and composite score. There was one intraoperative technical complication, where the robot had to be re-started midway through the operation.

DISCUSSION: The NASA-TLX score is a more reliable judge of learning curve than operative time for MAKO robotic-assisted shoulder arthroplasties, showing a learning curve breakpoint after three to five cases across different cognitive domains.

LEVEL OF EVIDENCE: Level IV - prospective observational study.