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Related Experiment Videos

Rolling horizon appointment scheduling: a simulation study.

Thomas R Rohleder1, Kenneth J Klassen

  • 1The University of Calgary, Haskayne School of Business, AB, Canada. tom.rohleder@haskayne.ucalgary.ca

Health Care Management Science
|October 5, 2002
PubMed
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Appointment scheduling systems face conflicting demands. This study introduces overload rules (OLR) and rule delay (RD) policies to manage fluctuating demand, optimizing both patient wait times and provider utilization.

Area of Science:

  • Operations Research
  • Healthcare Management
  • Applied Mathematics

Background:

  • Patient frustration with long appointment wait times is a persistent issue in healthcare.
  • Healthcare providers face pressure to maximize resource utilization for financial viability.
  • Balancing patient satisfaction and operational efficiency in appointment scheduling presents a complex challenge.

Purpose of the Study:

  • To address the dual challenges of fluctuating demand and conflicting provider/patient needs in appointment scheduling.
  • To explore the effectiveness of overload rules (OLR) and rule delay (RD) policies in appointment scheduling systems.
  • To investigate these policies within a rolling-horizon framework and varying demand loads.

Main Methods:

  • Simulation of appointment scheduling systems under a rolling-horizon environment.

Related Experiment Videos

  • Evaluation of two management policies: overload rules (OLR) and rule delay (RD).
  • Analysis across six distinct demand patterns/loads, assessing client and server-oriented measures.

    • Main Results:

    • Optimal choices for OLR and RD policies are highly dependent on the specific performance measures prioritized (e.g., wait times vs. utilization).
    • The effectiveness of scheduling policies varies significantly based on the type of client demand pattern.
    • A decision matrix is developed to guide managers in selecting appropriate policies based on scenario-specific tradeoffs.

    Conclusions:

    • Effective appointment scheduling requires careful consideration of the tradeoffs between patient experience and operational efficiency.
    • Managers must select OLR and RD policies tailored to their specific operational context and demand characteristics.
    • The developed matrix provides a valuable tool for optimizing appointment scheduling decisions in complex healthcare environments.