Use performance thresholds that meaningfully measure surgeon performance alongside standardized terminology when assessing surgical learning curves¶

Strategy · All strategies
Evidence · 2 claims (2 for) · 1 study (1 review), q2 · 0 of 1 report an effect size · 2 claims rest on one study

Description¶

The article recommends that learning curve analyses adopt thresholds providing meaningful measures of surgeon performance together with standardized terminology, because current simplistic thresholds and inconsistent use of terms such as proficiency and competency impede interpretation. It states: "Using thresholds that provide meaningful measures of surgeon performance alongside standardized terminology seems vital to realize the full potential of learning curve analyses for optimization of surgical training programmes." It further calls for enhanced reporting of surgeon baseline characteristics, experience and procedure complexity.

Design Implications¶

Context¶

Requirements¶

• Studies must report surgeon baseline characteristics, previous experience and procedure complexity to permit comparisons between studies, as the article states is required.

Constraints¶

• The article notes there is currently no widely accepted and validated method for quantitatively defining a plateau, and some studies used visual fit alone.

Target Learners¶

• surgeons learning robot-assisted procedures
• surgical trainees

Target Learning Goals¶

• acquisition of robot-assisted surgical skills
• design of surgical training programmes

Claims¶

• Methods and terminology used to define the point at which the robot-assisted learning curve is overcome are inconsistent and often lack statistical validation [+W]
• A plateau in performance does not necessarily equate to high-quality performance, so 'overcome' thresholds may misrepresent surgeon mastery [+W]

Related Strategies¶

• Use risk-adjusted cumulative sum (RA-CUSUM) analysis to account for case-mix heterogeneity in learning curve assessment
• Combine objective metrics with validated subjective tools and audio-visual recording to contextualize interpretation of surgical performance data

Examples¶

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Key Sources¶

• Soomro N. A., Hashimoto D. A., Porteous A. J., Ridley C. J. A., Marsh W. J., Ditto R. and Roy S. (2020). Systematic review of learning curves in robot‐assisted surgery. BJS Open. https://doi.org/10.1002/bjs5.50235