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Automated performance metrics, learning curve and robotic colorectal surgery.
Shing Wai Wong1,2, Philip Crowe1,2
1Department of General Surgery, Prince of Wales Hospital, Sydney, New South Wales, Australia.
Summary
Automated Performance Metrics (APMs) showed limited value in assessing the robotic colorectal surgery learning curve. Surgeon console time (SCT) alone effectively identified learning phases, with APMs offering no significant additional insight.
Area of Science:
- Robotic Surgery
- Surgical Education
- Performance Metrics
Background:
- Assessing surgical skill acquisition is crucial for patient safety and optimizing training.
- Automated Performance Metrics (APMs) are emerging tools for objective skill evaluation.
Purpose of the Study:
- To evaluate the utility of APMs in characterizing the learning curve for robotic colorectal surgery.
- To compare the information provided by APMs with traditional metrics like surgeon console time (SCT).
Main Methods:
- Retrospective analysis of 85 robotic colorectal surgeries.
- Utilized Cumulative Summation (CUSUM) technique to analyze learning curves.
- Key metrics included SCT, fourth arm use, clutch activation, instrument off-screen events, and electrocautery activation.
Main Results:
- CUSUM analysis identified two distinct learning phases based on SCT (50 and 35 cases).
- Significant difference in SCT between phases (176 min vs. 251 min, p < 0.002).
- No significant differences in APMs were observed between the identified learning phases after adjusting for SCT.
Conclusions:
- Surgeon console time (SCT) alone is a sufficient metric for assessing the learning curve in robotic colorectal surgery.
- Most APMs do not provide additional valuable information beyond SCT for learning curve assessment in this context.
