Static and dynamic appointment scheduling to improve patient access time.

Area of Science:

• Operations Research
• Healthcare Management
• Health Informatics

Background:

• Outpatient clinic efficiency and patient access are heavily influenced by appointment scheduling systems.
• Current static scheduling models fail to account for fluctuating patient volumes and physician availability.

Purpose of the Study:

• To optimize outpatient clinic appointment scheduling by considering variable patient arrivals and physician unavailabilities.
• To improve healthcare access times and clinic efficiency through advanced scheduling methodologies.

Main Methods:

• Formulated a stochastic mixed integer programming problem to model appointment scheduling.
• Developed two solution approaches: a mixed integer programming method for static schedules and Markov decision theory for dynamic strategies.
• Utilized discrete event simulation to evaluate the performance of both scheduling approaches in a real-world case study.

Main Results:

• A dynamic scheduling strategy derived from Markov decision theory demonstrated improved clinic performance.
• Even a minimal flexible capacity allocation (2%) led to significant performance gains.
• The study highlights the limitations of traditional static appointment schedules.

Conclusions:

• Dynamic appointment scheduling strategies offer a substantial improvement over static methods for outpatient clinics.
• Flexible capacity allocation is a key factor in enhancing clinic efficiency and patient access.
• The proposed optimization methods provide a robust framework for improving healthcare delivery operations.