title: Improving a patient appointment call center at Mayo Clinic.
abstract: OBJECTIVE
  Contact centers for patient and referring physician are important to large medical-centers such as the Mayo Clinic's Central Appointment Office (CAO). The aim of this case study is to report the process and results of a major process improvement effort, designed to simultaneously improve service quality and efficiency.
  
  
  METHODS
  Discrete-event simulation and optimization are used and linked to significant service improvements.
  
  
  RESULTS
  The process improvement efforts led to about a 70 percent improvement in patient service performance as measured by average answering-speed (ASA) and average abandonment rate (AAR). This was achieved without adding additional staff, despite call volume increasing by 12 percent. Evaluating process improvement projects is difficult owing to the "phased" implementation of changes. Thus, there is no true control against which to compare. Additionally, the results are based on a single case study.
  
  
  CONCLUSIONS
  Evaluation of process improvement projects is difficult due to the "phased" implementation of changes. Thus, there is no true control to compare against.
  
  
  CONCLUSIONS
  Contact center data and operations research methods, such as discrete-event simulation and optimization, can be integrated with change management, which results in significant process improvements in medical call-centers.
  
  
  CONCLUSIONS
  Structured quantitative modeling of contact centers can be an important extension to traditional quality and process improvement techniques.
authors.affiliation: Process Improvement Office, Vanderbilt University Medical Center, Nashville, Tennessee, USA. rohleder@mayo.edu
authors.forename: Thomas
authors.initials: T
authors.lastname: Rohleder
authors.affiliation: Process Improvement Office, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
authors.forename: Brian
authors.initials: B
authors.lastname: Bailey
authors.affiliation: Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA.
authors.forename: Brian
authors.initials: B
authors.lastname: Crum
authors.affiliation: Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA.
authors.forename: Timothy
authors.initials: T
authors.lastname: Faber
authors.affiliation: Essentia Health, Information Services, Duluth, Minnesota, USA.
authors.forename: Brandon
authors.initials: B
authors.lastname: Johnson
authors.affiliation: Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA.
authors.forename: LeTesha
authors.initials: L
authors.lastname: Montgomery
authors.affiliation: Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA.
authors.forename: Rachel
authors.initials: R
authors.lastname: Pringnitz
mesh_terms: D001071:Appointments and Schedules; D017598:Efficiency, Organizational; D006747:Hospital Communication Systems / Q000458:organization & administration* / Q000592:standards / Q000706:statistics & numerical data; D006801:Humans; D008910:Minnesota; D019982:Organizational Case Studies; D010561:Personnel Staffing and Scheduling / Q000458:organization & administration / Q000592:standards; D015397:Program Evaluation; D058996:Quality Improvement / Q000458:organization & administration*; D012017:Referral and Consultation / Q000458:organization & administration* / Q000592:standards / Q000706:statistics & numerical data; D016526:Workload