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Validation of the Simbionix Hysterectomy Procedural Modules for the Xi Robotic System
Purpose: To gather validity evidence for Simbionix (3D) Hysterectomy Modules for the DaVinci Xi console simulation system.
Background: Simbionix (3D) Hysterectomy Modules for the DaVinci Xi simulation system became available for use in 2017, offering new procedural training opportunities. Study of this tool is needed to inform surgical educators on how, when, and for whom these simulations would be effective.
Methods: In this multi-center prospective cohort, residents, fellows, and faculty in Obstetrics and Gynecology completed 4 simulator modules (ureter identification, bladder flap, colpotomy, and complete hysterectomy). Participants were categorized by experience level: less than 10 hysterectomies (novice), 10 to 49 hysterectomies (experienced), and 50 or greater hysterectomies (expert). Recordings were reviewed in duplicate by educators in minimally invasive gynecologic surgery using the Modified Global Evaluative Assessment of Robotic Skills (GEARS) tool.
Results: A total of 10 novice, 10 experienced, and 14 expert participants were recruited from 3 different academic medical centers. Simulator-generated metrics correlated with GEARS performance for bladder flap and ureter identification modules in multiple domains including total movements and total time for completion. GEARS performance for the bladder flap module correlated with experience level (novice vs experienced/expert) in depth perception (p=0.001), bimanual dexterity (p=0.008), efficiency (p=0.02), force sensitivity (p=0.031), total score (p=0.026), and overall score (p=0.016), but did not consistently correlate for the other procedural modules.
Discussions: Individual simulation modules within this new training tool may better discriminate between novice and experienced users, and in turn be more appropriate for the development of competency benchmarks than simulator performance as a whole.
Topics:
CREOG & APGO Annual Meeting, 2021, Resident, Faculty, Residency Director, Practice-Based Learning & Improvement, GME, Simulation, Minimally Invasive Surgery,
