ABSTRACT
OBJECTIVE: Our institution recently implemented a virtual reality (VR) skills curriculum for general surgery residents using the SimNow simulator. Based on a content alignment study, we revised the curriculum to include only 20 of 33 VR tasks and we added 3 previously validated inanimate tasks. The purpose of this study was to establish expert-derived proficiency levels for all tasks and to evaluate the validity of the scoring for the VR tasks. DESIGN: Two expert robotic surgeons performed 5 repetitions of each VR and inanimate task. The trimmed mean (lowest scoring attempt and outliers [>2 standard deviations] were eliminated) was defined as the expert level for each task. For the VR tasks, expert levels were compared to resident performance to evaluate validity. SETTING: This study was conducted at the University of Texas Southwestern Medical Center (Dallas, TX), a tertiary care academic teaching hospital. PARTICIPANTS: Two expert robotic surgeons participated in this study. The data from 42 residents (PGY2-4) who completed the original curriculum was used to represent novice performance. RESULTS: Comparison of expert levels and resident performance was statistically significant for 15 VR tasks (supporting validity) and approached significance (pâ¯=â¯0.06, 0.09) for 2 VR tasks; expert levels were designated as proficiency levels for these 17 tasks. Group comparisons were clearly not significant (pâ¯=â¯0.2-0.8) for 3 VR tasks; 2 of these 3 tasks were retained as introductory exercises (with 3 repetitions required) and 1 was excluded. For the 3 inanimate tasks, expert levels minus 2 standard deviations were designated as proficiency levels. CONCLUSIONS: This analysis generated validity evidence for 15 VR tasks and established expert-derived proficiency levels for 17 VR tasks and 3 inanimate tasks. Our proposed curriculum now consists of 19 VR and 3 inanimate tasks using the selected proficiency levels. We anticipate that this design will maximize curriculum efficiency and effectiveness.
