title: A quantitative experimental paradigm to optimize construction of rank order lists in the National Resident Matching Program: the ROSS-MOORE approach.
abstract: OBJECTIVE
  As part of the National Resident Matching Program, programs must submit a rank order list of desired applicants. Despite the importance of this process and the numerous manifest limitations with traditional approaches, minimal research has been conducted to examine the accuracy of different ranking strategies.
  
  
  METHODS
  The authors developed the Moore Optimized Ordinal Rank Estimator (MOORE), a novel algorithm for ranking applicants that is based on college sports ranking systems. Because it is not possible to study the Match in vivo, the authors then designed the Recruitment Outcomes Simulation System (ROSS). This program was used to simulate a series of interview seasons and to compare MOORE and traditional approaches under different conditions.
  
  
  RESULTS
  The accuracy of traditional ranking and the MOORE approach are equally and adversely affected with higher levels of intrarater variability. However, compared with traditional ranking methods, MOORE produces a more accurate rank order list as interrater variability increases.
  
  
  CONCLUSIONS
  The present data demonstrate three key findings. First, they provide proof of concept that it is possible to scientifically test the accuracy of different rank methods used in the Match. Second, they show that small amounts of variability can have a significant adverse impact on the accuracy of rank order lists. Finally, they demonstrate that an ordinal approach may lead to a more accurate rank order list in the presence of interviewer bias. The ROSS-MOORE approach offers programs a novel way to optimize the recruitment process and, potentially, to construct a more accurate rank order list.
authors.affiliation: Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511, USA. david.a.ross@yale.edu
authors.forename: David A
authors.initials: DA
authors.lastname: Ross
authors.forename: Edward Z
authors.initials: EZ
authors.lastname: Moore
mesh_terms: D000465:Algorithms; D006801:Humans; D007396:Internship and Residency / Q000458:organization & administration*; D015588:Observer Variation; D010560:Personnel Selection / Q000379:methods*; D014481:United States