Intraday dynamic rescheduling under patient no-shows.

Area of Science:

• Operations Research
• Healthcare Management
• Applied Probability

Background:

• Patient no-shows create uncertainty in outpatient clinics, often managed by overbooking.
• Traditional appointment scheduling lacks flexibility, as times are fixed post-assignment.
• Advancements in communication technology enable dynamic appointment adjustments.

Purpose of the Study:

• To develop an intraday dynamic rescheduling model for outpatient appointments.
• To optimize appointment schedules by dynamically adjusting to observed no-shows.
• To reduce operational costs in clinics by minimizing patient waiting and provider idle/overtime.

Main Methods:

• Formulation as a Markov Decision Process (MDP) to determine optimal scheduling and rescheduling policies.
• Development of an alternative formulation using 'atomic' actions for efficient policy computation via shortest path algorithms.
• Numerical study utilizing parameter estimates from existing literature.

Main Results:

• The proposed intraday dynamic rescheduling model effectively adjusts appointments based on real-time no-show data.
• The MDP formulation provides an optimal pre-day schedule and an optimal policy for dynamic updates.
• The shortest path algorithm offers a more efficient method for solving the optimal policy.

Conclusions:

• Intraday dynamic rescheduling significantly reduces expected costs in outpatient clinics.
• The model offers a 15% cost reduction compared to static scheduling approaches.
• Dynamic rescheduling leverages technological advancements for improved healthcare operational efficiency.