Robotic microsurgery: validating an assessment tool and plotting the learning curve.

BACKGROUND: The surgical robot has emerged as a potentially useful tool in microsurgery. The purpose of this study was to develop a validated assessment instrument and assess the learning curve for robotic microsurgery. The authors hypothesized that subjects would demonstrate improvement across multiple domains of performance with repetition of robotic microsurgical tasks. METHODS: In part 1, a novel assessment instrument called the Structured Assessment of Robotic Microsurgical Skills was tested. Four blinded expert evaluators graded six robotic microsurgery videos and interrater reliability was determined. In part 2, a cohort of 10 participants at various skill levels performed five robotic microvascular anastomoses. All 50 sessions were subjected to blind evaluation using the assessment instrument. Primary outcome measures included changes in operative time over the five sessions, and changes in assessment scores for all skill areas. RESULTS: Interrater reliability for the Structured Assessment of Robotic Microsurgical Skills instrument was excellent for each skill area, demonstrated by Cronbach alpha scores greater than 0.9 in each category across evaluators. All skill areas improved significantly for all participants, and operative time decreased for all participants over the course of the study. The results showed an initial steep ascent in technical skill acquisition followed by more gradual improvement, and a steady decrease in operative time to as short as 9 minutes. CONCLUSIONS: The Structured Assessment of Robotic Microsurgery Skills is a valid instrument for assessing microsurgical skill. Subjects at all levels of training, ranging from minimal microsurgical experience to expert microsurgeons, gained proficiency over the course of five robotic sessions.

Robotic microsurgical training and evaluation.

Robotic surgery has expanded rapidly over the past two decades and is in widespread use among the surgical subspecialties. Clinical applications in plastic surgery have emerged gradually over the last few years. One of the promising applications is robotic-assisted microvascular anastomosis. Here the authors first describe a process by which an assessment instrument they developed called the Structured Assessment of Robotic Microsurgical Skills (SARMS) was validated. The instrument combines the previously validated Structured Assessment of Microsurgical Skills (SAMS) with other skill domains in robotic surgery. Interrater reliability for the SARMS instrument was excellent for all skill areas among four expert, blinded evaluators. They then present a process by which the learning curve for robotic-assisted microvascular anastomoses was measured and plotted. Ten study participants performed five robotic microanastomoses each that were recorded, deidentified and scored. Trends in SARMS scores were plotted. All skill areas and overall performance improved significantly for each participant over the five microanastomotic sessions, and operative time decreased for all participants. The results showed an initial steep ascent in technical skill acquisition followed by more gradual improvement, and a steady decrease in operative times for the cohort. Participants at all levels of training, ranging from minimal microsurgical experience to expert microsurgeons gained proficiency over the course of five robotic sessions.