Minimizing total cost in scheduling outpatient appointments
Management Science
BAYES AND EMPIRICAL BAYES METHODS FOR DATA ANALYSIS
Statistics and Computing
Scheduling Arrivals to Queues: A Single-Server Model with No-Shows
Management Science
Reducing Delays for Medical Appointments: A Queueing Approach
Operations Research
Revenue Management for a Primary-Care Clinic in the Presence of Patient Choice
Operations Research
A Comparison of Traditional and Open-Access Policies for Appointment Scheduling
Manufacturing & Service Operations Management
Dynamic Scheduling of Outpatient Appointments Under Patient No-Shows and Cancellations
Manufacturing & Service Operations Management
Estimating the Implied Value of the Customer's Waiting Time
Manufacturing & Service Operations Management
A Stochastic Mathematical Appointment Overbooking Model for Healthcare Providers to Improve Profits
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Agent-Based System Design for Service Process Scheduling: Challenges, Approaches and Opportunities
Journal of Integrated Design & Process Science
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Patients' satisfaction with an appointment system when they attempt to book a nonurgent appointment is affected by their ability to book with a doctor of choice and to book an appointment at a convenient time of day. For medical conditions requiring urgent attention, patients want quick access to a familiar physician. For such instances, it is important for clinics to have open slots that allow same-day (urgent) access. A major challenge when designing outpatient appointment systems is the difficulty of matching randomly arriving patients' booking requests with physicians' available slots in a manner that maximizes patients' satisfaction as well as clinics' revenues. What makes this problem difficult is that booking preferences are not tracked, may differ from one patient to another, and may change over time. This paper describes a framework for the design of the next generation of appointment systems that dynamically learn and update patients' preferences and use this information to improve booking decisions. Analytical results leading to a partial characterization of an optimal booking policy are presented. Examples show that heuristic decision rules, based on this characterization, perform well and reveal insights about trade-offs among a variety of performance metrics important to clinic managers.