title: Simulator training to automaticity leads to improved skill transfer compared with traditional proficiency-based training: a randomized controlled trial.
abstract: OBJECTIVE
  We hypothesized that novices will perform better in the operating room after simulator training to automaticity compared with traditional proficiency based training (current standard training paradigm).
  
  
  BACKGROUND
  Simulator-acquired skill translates to the operating room, but the skill transfer is incomplete. Secondary task metrics reflect the ability of trainees to multitask (automaticity) and may improve performance assessment on simulators and skill transfer by indicating when learning is complete.
  
  
  METHODS
  Novices (N = 30) were enrolled in an IRB-approved, blinded, randomized, controlled trial. Participants were randomized into an intervention (n = 20) and a control (n = 10) group. The intervention group practiced on the FLS suturing task until they achieved expert levels of time and errors (proficiency), were tested on a live porcine fundoplication model, continued simulator training until they achieved expert levels on a visual spatial secondary task (automaticity) and were retested on the operating room (OR) model. The control group participated only during testing sessions. Performance scores were compared within and between groups during testing sessions.
  
  
  RESULTS
  : Intervention group participants achieved proficiency after 54 ± 14 and automaticity after additional 109 ± 57 repetitions. Participants achieved better scores in the OR after automaticity training [345 (range, 0-537)] compared with after proficiency-based training [220 (range, 0-452; P < 0.001].
  
  
  CONCLUSIONS
  Simulator training to automaticity takes more time but is superior to proficiency-based training, as it leads to improved skill acquisition and transfer. Secondary task metrics that reflect trainee automaticity should be implemented during simulator training to improve learning and skill transfer.
authors.affiliation: Carolinas Simulation Center, Carolinas Medical Center, Charlotte, NC, USA. Dimitrios.Stefanidis@carolinashealthcare.org
authors.forename: Dimitrios
authors.initials: D
authors.lastname: Stefanidis
authors.forename: Mark W
authors.initials: MW
authors.lastname: Scerbo
authors.forename: Paul N
authors.initials: PN
authors.lastname: Montero
authors.forename: Christina E
authors.initials: CE
authors.lastname: Acker
authors.forename: Warren D
authors.initials: WD
authors.lastname: Smith
mesh_terms: D000328:Adult; D000818:Animals; D001288:Attention; D001332:Automatism / Q000523:psychology*; D002983:Clinical Competence; D003198:Computer Simulation; D003479:Curriculum; D004516:Education, Premedical; D005260:Female; D006801:Humans; D010535:Laparoscopy / Q000193:education*; D059032:Learning Curve; D008297:Male; D008348:Manikins; D023421:Models, Animal; D010364:Pattern Recognition, Visual; D016037:Single-Blind Method; D013536:Suture Techniques; D013552:Swine; D014163:Transfer, Psychology; D016526:Workload / Q000523:psychology; D055815:Young Adult