Outlier experienced surgeon's performances impact on benchmark for technical surgical skills training

Training in medicine must move to an outcome-based approach. A proficiency-based progression outcome approach to training relies on a quantitative estimation of experienced operator performance. We aimed to develop a method for dealing with atypical expert performances in the quantitative definition of surgical proficiency.

In study one, 100 experienced laparoscopic surgeons' performances on virtual reality and box-trainer simulators were assessed for two similar laparoscopic tasks. In study two, 15 experienced surgeons and 16 trainee colorectal surgeons performed one simulated hand-assisted laparoscopic colorectal procedure. Performance scores of experienced surgeons in both studies were standardized (i.e. Z-scores) using the mean and standard deviations (SDs). Performances >1.96 SDs from the mean were excluded in proficiency definitions.

In study one, 1-5% of surgeons' performances were excluded having performed significantly below their colleagues. Excluded surgeons made significantly fewer correct incisions (mean = 7 (SD = 2) versus 19.42 (SD = 4.6), P < 0.0001) and a greater proportion of incorrect incisions (mean = 45.71 (SD = 10.48) versus 5.25 (SD = 6.6), P < 0.0001). In study two, one experienced colorectal surgeon performance was >4 SDs for time to complete the procedure and >6 SDs for path length. After their exclusions, experienced surgeons' performances were significantly better than trainees for path length: P = 0.031 and for time: P = 0.002.

Objectively assessed atypical expert performances were few. Z-score standardization identified them and produced a more robust quantitative definition of proficiency.