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Optimal Choice for Appointment Scheduling Window under Patient No-Show Behavior

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  • Nan Liu

Abstract

type="main" xml:id="poms12401-abs-0001"> Observing that patients with longer appointment delays tend to have higher no-show rates, many providers place a limit on how far into the future that an appointment can be scheduled. This article studies how the choice of appointment scheduling window affects a provider's operational efficiency. We use a single server queue to model the registered appointments in a provider's work schedule, and the capacity of the queue serves as a proxy of the size of the appointment window. The provider chooses a common appointment window for all patients to maximize her long-run average net reward, which depends on the rewards collected from patients served and the “penalty” paid for those who cannot be scheduled. Using a stylized M/M/1/K queueing model, we provide an analytical characterization for the optimal appointment queue capacity K, and study how it should be adjusted in response to changes in other model parameters. In particular, we find that simply increasing appointment window could be counterproductive when patients become more likely to show up. Patient sensitivity to incremental delays, rather than the magnitudes of no-show probabilities, plays a more important role in determining the optimal appointment window. Via extensive numerical experiments, we confirm that our analytical results obtained under the M/M/1/K model continue to hold in more realistic settings. Our numerical study also reveals substantial efficiency gains resulted from adopting an optimal appointment scheduling window when the provider has no other operational levers available to deal with patient no-shows. However, when the provider can adjust panel size and overbooking level, limiting the appointment window serves more as a substitute strategy, rather than a complement.

Suggested Citation

  • Nan Liu, 2016. "Optimal Choice for Appointment Scheduling Window under Patient No-Show Behavior," Production and Operations Management, Production and Operations Management Society, vol. 25(1), pages 128-142, January.
  • Handle: RePEc:bla:popmgt:v:25:y:2016:i:1:p:128-142
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    File URL: http://hdl.handle.net/10.1111/poms.2016.25.issue-1
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    Citations

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    Cited by:

    1. Murtaza Nasir & Nichalin Summerfield & Ali Dag & Asil Oztekin, 2020. "A service analytic approach to studying patient no-shows," Service Business, Springer;Pan-Pacific Business Association, vol. 14(2), pages 287-313, June.
    2. Long Gao & Jim (Junmin) Shi & Michael F. Gorman & Ting Luo, 2020. "Business Analytics for Intermodal Capacity Management," Manufacturing & Service Operations Management, INFORMS, vol. 22(2), pages 310-329, March.
    3. Seokjun Youn & H. Neil Geismar & Michael Pinedo, 2022. "Planning and scheduling in healthcare for better care coordination: Current understanding, trending topics, and future opportunities," Production and Operations Management, Production and Operations Management Society, vol. 31(12), pages 4407-4423, December.
    4. Christos Zacharias & Tallys Yunes, 2020. "Multimodularity in the Stochastic Appointment Scheduling Problem with Discrete Arrival Epochs," Management Science, INFORMS, vol. 66(2), pages 744-763, February.
    5. Pan, Xingwei & Geng, Na & Xie, Xiaolan & Wen, Jing, 2020. "Managing appointments with waiting time targets and random walk-ins," Omega, Elsevier, vol. 95(C).
    6. Baş, Seda & Carello, Giuliana & Lanzarone, Ettore & Yalçındağ, Semih, 2018. "An appointment scheduling framework to balance the production of blood units from donation," European Journal of Operational Research, Elsevier, vol. 265(3), pages 1124-1143.
    7. Ruiwei Jiang & Siqian Shen & Yiling Zhang, 2017. "Integer Programming Approaches for Appointment Scheduling with Random No-Shows and Service Durations," Operations Research, INFORMS, vol. 65(6), pages 1638-1656, December.
    8. Shan Wang & Nan Liu & Guohua Wan, 2020. "Managing Appointment-Based Services in the Presence of Walk-in Customers," Management Science, INFORMS, vol. 66(2), pages 667-686, February.
    9. Ni Huang & Zhijun Yan & Haonan Yin, 2021. "Effects of Online–Offline Service Integration on e‐Healthcare Providers: A Quasi‐Natural Experiment," Production and Operations Management, Production and Operations Management Society, vol. 30(8), pages 2359-2378, August.
    10. Hessam Bavafa & Sergei Savin & Christian Terwiesch, 2021. "Customizing Primary Care Delivery Using E‐Visits," Production and Operations Management, Production and Operations Management Society, vol. 30(11), pages 4306-4327, November.
    11. Li Luo & Ying Zhou & Bernard T. Han & Jialing Li, 2019. "An optimization model to determine appointment scheduling window for an outpatient clinic with patient no-shows," Health Care Management Science, Springer, vol. 22(1), pages 68-84, March.
    12. Vusal Babashov & Antoine Sauré & Onur Ozturk & Jonathan Patrick, 2023. "Setting wait time targets in a multi‐priority patient setting," Production and Operations Management, Production and Operations Management Society, vol. 32(6), pages 1958-1974, June.
    13. Yong-Hong Kuo & Hari Balasubramanian & Yan Chen, 2020. "Medical appointment overbooking and optimal scheduling: tradeoffs between schedule efficiency and accessibility to service," Flexible Services and Manufacturing Journal, Springer, vol. 32(1), pages 72-101, March.
    14. Namakshenas, Mohammad & Mazdeh, Mohammad Mahdavi & Braaksma, Aleida & Heydari, Mehdi, 2023. "Appointment scheduling for medical diagnostic centers considering time-sensitive pharmaceuticals: A dynamic robust optimization approach," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1018-1031.
    15. Ahmadi-Javid, Amir & Jalali, Zahra & Klassen, Kenneth J, 2017. "Outpatient appointment systems in healthcare: A review of optimization studies," European Journal of Operational Research, Elsevier, vol. 258(1), pages 3-34.
    16. Xi Chen & Liu Zhao & Haiming Liang & Kin Keung Lai, 2019. "Matching patients and healthcare service providers: a novel two-stage method based on knowledge rules and OWA-NSGA-II algorithm," Journal of Combinatorial Optimization, Springer, vol. 37(1), pages 221-247, January.
    17. Jiang, Bowen & Tang, Jiafu & Yan, Chongjun, 2019. "A stochastic programming model for outpatient appointment scheduling considering unpunctuality," Omega, Elsevier, vol. 82(C), pages 70-82.
    18. Nguyen, Thu Ba T. & Sivakumar, Appa Iyer & Graves, Stephen C., 2018. "Capacity planning with demand uncertainty for outpatient clinics," European Journal of Operational Research, Elsevier, vol. 267(1), pages 338-348.

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