A Markov decision model for determining optimal outpatient scheduling.

Area of Science:

• Operations Research
• Healthcare Management
• Health Systems Engineering

Background:

• Outpatient clinics face challenges with patient no-shows and long appointment lead times, impacting efficiency and resource utilization.
• Open access scheduling, aiming to "do today's demand today," is a proposed solution to mitigate wasted capacity from missed appointments.

Purpose of the Study:

• To develop and evaluate a Markov Decision Process (MDP) model for outpatient clinic scheduling.
• To compare the performance of an MDP-derived short booking window policy against open access scheduling.
• To analyze the trade-offs between patient access (lead times) and clinic operational efficiency (revenue, idle time, overtime).

Main Methods:

• Development of a Markov Decision Process (MDP) model to optimize appointment scheduling.
• Simulation analysis across various clinic scenarios to assess scheduling policy performance.
• Evaluation of patient-related metrics (lead times) and system-related metrics (costs, revenue, utilization).

Main Results:

• The MDP-based short booking window policy significantly outperforms open access in managing clinic efficiency.
• The MDP policy demonstrates superior or comparable results in minimizing costs and maximizing profits.
• Consistent patient throughput is achieved with the MDP policy across diverse operational scenarios.

Conclusions:

• A short booking window, optimized via an MDP model, offers a more effective strategy than open access for outpatient clinic management.
• This approach balances patient access needs with system-level financial and operational performance.
• The MDP model provides a robust framework for optimizing clinic scheduling to improve efficiency and patient flow.