Rolling horizon appointment scheduling: a simulation study.

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.
• 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.