Related Experiment Video
Updated: Jan 3, 2026
Emergency Undocking in Robotic Surgery: A Simulation Curriculum
Published on: May 20, 2018
A simulation-based selection process for trying to identify medical students who will become outstanding general
Yazan AlJamal1, Nicholas Prabhakar2, Mariela Rivera2
1Mayo Clinic Multidisciplinary Simulation Center, Mayo Clinic College of Medicine and Science, Rochester, MN, USA; Department of General Surgery, Mayo Clinic, Rochester, MN, USA; Department of Cardiothoracic Surgery, Mayo Clinic, Rochester, MN, USA.
Background:
Selecting the right applicants for general surgery training is critical and difficult. We refined our selection process by using a pre-interview preparation package and simulation-based assessments.
Methods:
Sixty applicants invited for categorical general-surgery residency interview were mailed an educational package which included a link to instructional videos, surgical instruments and low-cost models for suturing, open knot tying, and adrenal anatomy knowledge. During the interview day, applicants participated in a 48-min simulation-based assessment consisting of same tasks included in the package. Performance scores were used to assist in ranking applicants. The matched 2018class was compared to our previous intern classes on several objective assessments (Surgical-Olympics and ABSITE score).
Results:
Students scored >50tile moved high in our final rank-list. The 2018 class scored significantly higher in 10 of 15 Surgical-Olympic stations compared to our 2015-17class with no significant difference on ABSITE score.
Conclusions:
By mailing out a pre-interview welcome package and adding a simulation based assessment to our General Surgery categorical interview process, we believe early objective data suggests we positively influenced our 2018 NRMP match.
