Appointment Scheduling Under Patient Preference and No-Show Behavior

Jacob Feldman (), Nan Liu (), Huseyin Topaloglu () and Serhan Ziya ()
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Jacob Feldman: School of Operations Research and Information Engineering, Cornell University, Ithaca, New York 14853
Nan Liu: Department of Health Policy and Management, Mailman School of Public Health, Columbia University, New York, New York 10032
Huseyin Topaloglu: School of Operations Research and Information Engineering, Cornell University, Ithaca, New York 14853
Serhan Ziya: Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, North Carolina 27599

Operations Research, 2014, vol. 62, issue 4, 794-811

Abstract: Motivated by the rising popularity of electronic appointment booking systems, we develop appointment scheduling models that take into account the patient preferences regarding when they would like to be seen. The service provider dynamically decides which appointment days to make available for the patients. Patients arriving with appointment requests may choose one of the days offered to them or leave without an appointment. Patients with scheduled appointments may cancel or not show up for the service. The service provider collects a “revenue” from each patient who shows up and incurs a “service cost” that depends on the number of scheduled appointments. The objective is to maximize the expected net “profit” per day. We begin by developing a static model that does not consider the current state of the scheduled appointments. We give a characterization of the optimal policy under the static model and bound its optimality gap. Building on the static model, we develop a dynamic model that considers the current state of the scheduled appointments, and we propose a heuristic solution procedure. In our computational experiments, we test the performance of our models under the patient preferences estimated through a discrete choice experiment that we conduct in a large community health center. Our computational experiments reveal that the policies we propose perform well under a variety of conditions.

Keywords: appointment scheduling; healthcare management; Markov decision process; optimization (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (50)

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