Stochastic integer programming for multi-disciplinary outpatient clinic planning

A.G. Leeftink* (Corresponding Author), I.M.H. Vliegen, E.W. Hans

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

7 Citations (Scopus)
83 Downloads (Pure)

Abstract

Scheduling appointments in a multi-disciplinary clinic is complex, since coordination between disciplines is required. The design of a blueprint schedule for a multi-disciplinary clinic with open access requirements requires an integrated optimization approach, in which all appointment schedules are jointly optimized. As this currently is an open question in the literature, our research is the first to address this problem. This research is motivated by a Dutch hospital, which uses a multi-disciplinary cancer clinic to communicate the diagnosis and to explain the treatment plan to their patients. Furthermore, also regular patients are seen by the clinicians. All involved clinicians therefore require a blueprint schedule, in which multiple patient types can be scheduled. We design these blueprint schedules by optimizing the patient waiting time, clinician idle time, and clinician overtime. As scheduling decisions at multiple time intervals are involved, and patient routing is stochastic, we model this system as a stochastic integer program. The stochastic integer program is adapted for and solved with a sample average approximation approach. Numerical experiments evaluate the performance of the sample average approximation approach. We test the suitability of the approach for the hospital’s problem at hand, compare our results with the current hospital schedules, and present the associated savings. Using this approach, robust blueprint schedules can be found for a multi-disciplinary clinic of the Dutch hospital.

Original languageEnglish
Pages (from-to)53-67
Number of pages15
JournalHealth care management science
Volume22
Issue number1
Early online date9 Nov 2017
DOIs
Publication statusPublished - 15 Mar 2019

Keywords

  • UT-Hybrid-D
  • Multi-disciplinary planning
  • Sample average approximation
  • Stochastic processes
  • Appointment scheduling

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