AUA 2018: Skill Acquisition and Cognitive Load, Simulation

San Francisco, CA (UroToday.com) Ahmed Ghazi, MD, from the University of Rochester Medical Center, presented data on robot skills training evaluations. The acquisition of technical surgical skills by trainees requires didactic instruction, practice and feedback. However, this requires a considerable amount of faculty teaching time. Many medical educators are now advocating the uses of teaching method that utilize a computer-based training and web-based feedback. Many believe the benefits of these methods can decrease time demands on faculty and create opportunities for trainees to learn, practice, and acquire technical skills without clinical pressures. For this study, Ahmed Ghazi and team aimed to evaluate instructional methods not requiring expert in-person attendance during VR robotic simulation training, remote expert feedback, and self-training using expert videos were compared to standard in-person expert feedback.

For this study, 15 medical students were recruited. The students were randomized into one of three groups and all were asked to complete 4 VR robot simulation tasks. The three groups are described below.

Group 1: Students self-study by watching expert instructional videos performing the same 4 tasks, then do the task themselves.
Group 2: Students recorded themselves performing the 4 tasks then upload them online for remote expert feedback.
Group 3: Students practice and perform tasks with expert instructor present and receive in-person feedback.

For each of tasks, the number of repetitions to reach a pre-identified proficiency level and the cognitive workload during training (NASA-TLX) were recorded.

The results showed that for simple tasks there was a significant difference between the three groups. However, for complex tasks, group 2 and 3 required significantly fewer repetitions compared to group 1. For cognitive workload, scores were lowest in group 1 and highest in group 2.

Ghazi concluded that the findings suggest that expert remote feedback was as effective as standard expert feedback in complex robotic simulation training tasks.

Presented by: Ahmed Ghazi, MD, FEBU University of Rochester
Co-Authors: Prabhakar Mithal, Brett Teplitz, Yongsoo Joo, Noorullah Maqsoodi, Karen Chong, Katherine Stewart, Henry Keenan, Stephen Hassig, Hongyi Kang, Scott Echternacht, Changyong Feng, Ahmed Ghazi
Author Affiliation: University of Rochester Medical Center

Written by: Renai Yoon, Department of Urology, University of California-Irvine, medical writer for UroToday.com at the 2018 AUA Annual Meeting - May 18 - 21, 2018 – San Francisco, CA USA

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