title: Can we become better robot surgeons through simulator practice?
abstract: BACKGROUND
  There is significant growth in the use of the robotic surgery platform in the general surgery community. Current pre-requisites for robot surgery training include performing basic tasks on a simulator and achieving a minimum overall score for each task. However, there is limited information about these tasks related to performance and time required to become proficient. We focused on critical tasks that have the highest potential for preventing inadvertent injuries, and constructed models to predict how many attempts would be needed to master the tasks depending on the user's initial attempt.
  
  
  METHODS
  This study was conducted using de-identified data collected over 12 months from the dV-Trainers® simulator at our institution. We analyzed tasks used in institutional surgical robot credentialing that focused on camera manipulation and energy use. Data were extracted from the Camera Targeting, Energy Dissection, and Energy Switching exercises focusing on individual metrics such as Time to Complete Exercise, Economy of Motion, Misapplied Energy Time, and Blood Volume Loss. Mixed linear models looking at sequential attempts and specific performance metrics were constructed using IBM SPSS Statistics version 20.
  
  
  RESULTS
  Over 26,000 overall minutes of recorded use was logged in our simulator by more than 30 unique users across all exercises. An average of 15 users performed each of the analyzed exercises, with an average of eight attempts per exercise. Based on our models, on average most users would need four to five attempts to achieve 80 % proficiency for any given metric.
  
  
  CONCLUSIONS
  Virtual reality robotic simulators such as the dv-Trainer® can be used by general surgeons to become better robotic surgeons. Our data suggests that it can be used by a surgeon to predict how much time and effort one would need to spend on the simulator in order to become proficient with the robot, especially in critical metrics such as camera manipulation and energy application. Surgeons who require more attempts to successfully complete tasks may want to consider additional training methods, such as proctoring or hands-on laboratories, to improve robot surgery proficiency.
authors.forename: Ankit
authors.initials: A
authors.lastname: Patel
authors.forename: Meghna
authors.initials: M
authors.lastname: Patel
authors.forename: Nathaniel
authors.initials: N
authors.lastname: Lytle
authors.forename: Juan P
authors.initials: JP
authors.lastname: Toro
authors.forename: Rachel L
authors.initials: RL
authors.lastname: Medbery
authors.forename: Sheryl
authors.initials: S
authors.lastname: Bluestein
authors.forename: Sebastian D
authors.initials: SD
authors.lastname: Perez
authors.forename: John F
authors.initials: JF
authors.lastname: Sweeney
authors.forename: S Scott
authors.initials: SS
authors.lastname: Davis
authors.forename: Edward
authors.initials: E
authors.lastname: Lin
mesh_terms: D002983:Clinical Competence / Q000592:standards*; D003198:Computer Simulation; D003406:Credentialing; D004502:Education, Medical, Continuing / Q000379:methods*; D004521:Educational Measurement; D005500:Follow-Up Studies; D013502:General Surgery / Q000193:education* / Q000379:methods; D006801:Humans; D010820:Physicians / Q000592:standards*; D012371:Robotics / Q000193:education*