title: Automation Improves Schedule Quality and Increases Scheduling Efficiency for Residents.
abstract: BACKGROUND
  Medical resident scheduling is difficult due to multiple rules, competing educational goals, and ever-evolving graduate medical education requirements. Despite this, schedules are typically created manually, consuming hours of work, producing schedules of varying quality, and yielding negative consequences for resident morale and learning.
  
  
  OBJECTIVE
  To determine whether computerized decision support can improve the construction of residency schedules, saving time and improving schedule quality.
  
  
  METHODS
  The Optimized Residency Scheduling Assistant was designed by a team from the University of Michigan Department of Industrial and Operations Engineering. It was implemented in the C.S. Mott Children's Hospital Pediatric Emergency Department in the 2012-2013 academic year. The 4 metrics of schedule quality that were compared between the 2010-2011 and 2012-2013 academic years were the incidence of challenging shift transitions, the incidence of shifts following continuity clinics, the total shift inequity, and the night shift inequity.
  
  
  RESULTS
  All scheduling rules were successfully incorporated. Average schedule creation time fell from 22 to 28 hours to 4 to 6 hours per month, and 3 of 4 metrics of schedule quality significantly improved. For the implementation year, the incidence of challenging shift transitions decreased from 83 to 14 (P < .01); the incidence of postclinic shifts decreased from 72 to 32 (P < .01); and the SD of night shifts dropped by 55.6% (P < .01).
  
  
  CONCLUSIONS
  This automated shift scheduling system improves the current manual scheduling process, reducing time spent and improving schedule quality. Embracing such automated tools can benefit residency programs with shift-based scheduling needs.
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Perelstein
authors.forename: Ariella
authors.initials: A
authors.lastname: Rose
authors.forename: Young-Chae
authors.initials: YC
authors.lastname: Hong
authors.forename: Amy
authors.initials: A
authors.lastname: Cohn
authors.forename: Micah T
authors.initials: MT
authors.lastname: Long
mesh_terms: D001071:Appointments and Schedules; D001331:Automation; D003658:Decision Making, Computer-Assisted; D004503:Education, Medical, Graduate; D006801:Humans; D007396:Internship and Residency / Q000379:methods / Q000458:organization & administration*; D010561:Personnel Staffing and Scheduling / Q000458:organization & administration*; D010820:Physicians; D014940:Work Schedule Tolerance; D016526:Workload