Sidney I Roberts1, Steven Y Cen2, Jessica H Nguyen1, Laura C Perez1, Luis G Medina1, Runzhuo Ma1, Sandra Marshall3, Rafal Kocielnik4, Anima Anandkumar4,5, Andrew J Hung1. 1. 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. 2. Departments of Radiology and Neurology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA. 3. EyeTracking, Inc., Solana Beach, California, USA. 4. Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, California, USA. 5. Nvidia Corporation, Santa Clara, California, USA.
Abstract
Purpose: We attempt to understand the relationship between surgeon technical skills, cognitive workload, and errors during a simulated robotic dissection task. Materials and Methods: 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. Results: 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 p < 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). Conclusions: 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.
Purpose: We attempt to understand the relationship between surgeon technical skills, cognitive workload, and errors during a simulated robotic dissection task. Materials and Methods: 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. Results: 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 p < 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). Conclusions: 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: Monty A Aghazadeh; Isuru S Jayaratna; Andrew J Hung; Michael M Pan; Mihir M Desai; Inderbir S Gill; Alvin C Goh Journal: Surg Endosc Date: 2015-01-22 Impact factor: 4.584
Authors: Oliver A Varban; Jyothi R Thumma; Arthur M Carlin; Amir A Ghaferi; Justin B Dimick; Jonathan F Finks Journal: Ann Surg Date: 2020-11-12 Impact factor: 13.787
Authors: Loc Trinh; Samuel Mingo; Erik B Vanstrum; Daniel I Sanford; Runzhuo Ma; Jessica H Nguyen; Yan Liu; Andrew J Hung Journal: Eur Urol Focus Date: 2021-04-12