The Relationship Between Technical Skills, Cognitive Workload, and Errors During Robotic Surgical Exercises.

Journal: Journal of endourology
Published Date: May 1, 2022

Abstract

We attempt to understand the relationship between surgeon technical skills, cognitive workload, and errors during a simulated robotic dissection task. Participant surgeons performed a robotic surgery dissection exercise. Participants were grouped based on surgical experience. Technical skills were evaluated utilizing the validated Global Evaluative Assessment of Robotic Skills (GEARS) assessment tool. The dissection task was evaluated for errors during active dissection or passive retraction maneuvers. We quantified cognitive workload of surgeon participants as an index of cognitive activity (ICA), derived from task-evoked pupillary response metrics; ICA ranged 0 to 1, with 1 representing maximum ICA. Generalized estimating equation (GEE) was used for all modelings to establish relationships between surgeon technical skills, cognitive workload, and errors. We found a strong association between technical skills as measured by multiple GEARS domains (depth perception, force sensitivity, and robotic control) and passive errors, with higher GEARS scores associated with a lower relative risk of errors (all  < 0.01). For novice surgeons, as average GEARS scores increased, the average estimated ICA decreased. In contrast, as average GEARS increased for expert surgeons, the average estimated ICA increased. When exhibiting optimal technical skill (maximal GEARS scores), novices and experts reached a similar range of ICA scores (ICA: 0.47 and 0.42, respectively). This study found that there is an optimal cognitive workload level for surgeons of all experience levels during our robotic surgical exercise. Select technical skill domains were strong predictors of errors. Future research will explore whether an ideal cognitive workload range truly optimizes surgical training and reduces surgical errors.

Authors

  • Sidney I Roberts
    Catherine and Joseph Aresty Department of Urology, Center for Robotic Simulation and Education, USC Institute of Urology, University of Southern California, Los Angeles, California, USA.
  • Steven Y Cen
    Departments of Radiology and Neurology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
  • Jessica H Nguyen
    Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA.
  • Laura C Perez
    Catherine and Joseph Aresty Department of Urology, Center for Robotic Simulation and Education, USC Institute of Urology, University of Southern California, Los Angeles, California, USA.
  • Luis G Medina
    USC Institute of Urology, University of Southern California, 1441 Eastlake Ave., Suite 7416, Los Angeles, CA, 90089-9178, USA.
  • Runzhuo Ma
    Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, California, USA.
  • Sandra Marshall
    EyeTracking, Inc., Solana Beach, California, USA.
  • Rafal Kocielnik
    University of Washington, WA.
  • Anima Anandkumar
    Department of Computing and Mathematical Science, California Institute of Technology, Pasadena, California; NVIDIA Corporation, Santa Clara, California.
  • Andrew J Hung
    Center for Robotic Simulation & Education, Catherine & Joseph Aresty Department of Urology, University of Southern California Institute of Urology, Los Angeles, California. Electronic address: Andrew.Hung@med.usc.edu.

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