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Appointment Scheduling Under Patient Preference and No-Show Behavior

Author

Listed:
  • 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)

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.

Suggested Citation

  • Jacob Feldman & Nan Liu & Huseyin Topaloglu & Serhan Ziya, 2014. "Appointment Scheduling Under Patient Preference and No-Show Behavior," Operations Research, INFORMS, vol. 62(4), pages 794-811, August.
  • Handle: RePEc:inm:oropre:v:62:y:2014:i:4:p:794-811
    DOI: 10.1287/opre.2014.1286
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    References listed on IDEAS

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    1. Huseyin Topaloglu & S. Ilker Birbil & J. B. G. Frenk & Nilay Noyan, 2012. "Tractable Open Loop Policies for Joint Overbooking and Capacity Control Over a Single Flight Leg with Multiple Fare Classes," Transportation Science, INFORMS, vol. 46(4), pages 460-481, November.
    2. Guillermo Gallego & Garrett van Ryzin, 1997. "A Multiproduct Dynamic Pricing Problem and Its Applications to Network Yield Management," Operations Research, INFORMS, vol. 45(1), pages 24-41, February.
    3. Guillermo Gallego & Garrett van Ryzin, 1994. "Optimal Dynamic Pricing of Inventories with Stochastic Demand over Finite Horizons," Management Science, INFORMS, vol. 40(8), pages 999-1020, August.
    4. Juan José Miranda Bront & Isabel Méndez-Díaz & Gustavo Vulcano, 2009. "A Column Generation Algorithm for Choice-Based Network Revenue Management," Operations Research, INFORMS, vol. 57(3), pages 769-784, June.
    5. Jonathan Patrick & Martin L. Puterman & Maurice Queyranne, 2008. "Dynamic Multipriority Patient Scheduling for a Diagnostic Resource," Operations Research, INFORMS, vol. 56(6), pages 1507-1525, December.
    6. Wen-Ya Wang & Diwakar Gupta, 2011. "Adaptive Appointment Systems with Patient Preferences," Manufacturing & Service Operations Management, INFORMS, vol. 13(3), pages 373-389, July.
    7. Kalyan Talluri & Garrett van Ryzin, 2004. "Revenue Management Under a General Discrete Choice Model of Consumer Behavior," Management Science, INFORMS, vol. 50(1), pages 15-33, January.
    8. Diwakar Gupta & Lei Wang, 2008. "Revenue Management for a Primary-Care Clinic in the Presence of Patient Choice," Operations Research, INFORMS, vol. 56(3), pages 576-592, June.
    9. Retsef Levi & Ana Radovanović, 2010. "Provably Near-Optimal LP-Based Policies for Revenue Management in Systems with Reusable Resources," Operations Research, INFORMS, vol. 58(2), pages 503-507, April.
    10. Hole, Arne Risa, 2008. "Modelling heterogeneity in patients' preferences for the attributes of a general practitioner appointment," Journal of Health Economics, Elsevier, vol. 27(4), pages 1078-1094, July.
    11. Woonghee Tim Huh & Nan Liu & Van-Anh Truong, 2013. "Multiresource Allocation Scheduling in Dynamic Environments," Manufacturing & Service Operations Management, INFORMS, vol. 15(2), pages 280-291, May.
    12. Sumit Kunnumkal & Huseyin Topaloglu, 2008. "A refined deterministic linear program for the network revenue management problem with customer choice behavior," Naval Research Logistics (NRL), John Wiley & Sons, vol. 55(6), pages 563-580, September.
    13. Qian Liu & Garrett van Ryzin, 2008. "On the Choice-Based Linear Programming Model for Network Revenue Management," Manufacturing & Service Operations Management, INFORMS, vol. 10(2), pages 288-310, October.
    14. Yigal Gerchak & Diwakar Gupta & Mordechai Henig, 1996. "Reservation Planning for Elective Surgery Under Uncertain Demand for Emergency Surgery," Management Science, INFORMS, vol. 42(3), pages 321-334, March.
    15. Dan Zhang & Daniel Adelman, 2009. "An Approximate Dynamic Programming Approach to Network Revenue Management with Customer Choice," Transportation Science, INFORMS, vol. 43(3), pages 381-394, August.
    16. Rohleder, Thomas R. & Klassen, Kenneth J., 2000. "Using client-variance information to improve dynamic appointment scheduling performance," Omega, Elsevier, vol. 28(3), pages 293-302, June.
    17. Nan Liu & Serhan Ziya & Vidyadhar G. Kulkarni, 2010. "Dynamic Scheduling of Outpatient Appointments Under Patient No-Shows and Cancellations," Manufacturing & Service Operations Management, INFORMS, vol. 12(2), pages 347-364, September.
    18. Linda V. Green & Sergei Savin, 2008. "Reducing Delays for Medical Appointments: A Queueing Approach," Operations Research, INFORMS, vol. 56(6), pages 1526-1538, December.
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