Active control time: an objective performance metric for trainee participation in robotic surgery

Julie M. Clanahan, Andrew Yee, Michael M. Awad

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Trainee participation and progression in robotic general surgery remain poorly defined. Computer-assisted technology offers the potential to provide and track objective performance metrics. In this study, we aimed to validate the use of a novel metric—active control time (ACT)—for assessing trainee participation in robotic-assisted cases. Performance data from da Vinci Surgical Systems was retrospectively analyzed for all robotic cases involving trainees with a single minimally invasive surgeon over 10 months. The primary outcome metric was percent ACT—the amount of trainee console time spent in active system manipulations over total active time from both consoles. Kruskal–Wallis and Mann–Whitney U statistical tests were applied in analyses. A total of 123 robotic cases with 18 general surgery residents and 1 fellow were included. Of these, 56 were categorized as complex. Median %ACT was statistically different between trainee levels for all case types taken in aggregate (PGY1s 3.0% [IQR 2–14%], PGY3s 32% [IQR 27–66%], PGY4s 42% [IQR 26–52%], PGY5s 50% [IQR 28–70%], and fellow 61% [IQR 41–85%], p = < 0.0001). When stratified by complexity, median %ACT was higher in standard versus complex cases for PGY5 (60% vs. 36%, p = 0.0002) and fellow groups (74% vs. 47%, p = 0.0045). In this study, we demonstrated an increase in %ACT with trainee level and with standard versus complex robotic cases. These findings are consistent with hypotheses, providing validity evidence for ACT as an objective measurement of trainee participation in robotic-assisted cases. Future studies will aim to define task-specific ACT to guide further robotic training and performance assessments.

Original languageEnglish
Pages (from-to)2117-2123
Number of pages7
JournalJournal of Robotic Surgery
Issue number5
StatePublished - Oct 2023


  • Automated performance metrics
  • Objective performance metrics
  • Robotic education
  • Robotic surgery
  • Robotic training


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