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Appointment Capacity Planning in Specialty Clinics: A Queueing Approach

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  • Navid Izady

    (School of Mathematical Sciences/Southampton Business School, University of Southampton SO17 1BJ, United Kingdom)

Abstract

Specialty clinics provide specialized care for patients referred by primary care physicians, emergency departments, or other specialists. Urgent patients must often be seen on the referral day, whereas nonurgent referrals are typically booked an appointment for the future. To deliver a balanced performance, the clinics must know how much “appointment capacity” is needed for achieving a reasonably quick access for nonurgent patients. To help identify the capacity that leads to the desired performance, we model the dynamics of appointment backlog as novel discrete-time bulk service queues and develop numerical methods for efficient computation of corresponding performance metrics. Realistic features such as arbitrary referral and clinic appointment cancellation distributions, delay-dependent no-show behaviour, and rescheduling of no-shows are explicitly captured in our models. The accuracy of the models in predicting performance as well as their usefulness in appointment capacity planning is demonstrated using real data. We also show the application of our models in capacity planning in clinics where patient panel size, rather than appointment capacity, is the major decision variable.

Suggested Citation

  • Navid Izady, 2015. "Appointment Capacity Planning in Specialty Clinics: A Queueing Approach," Operations Research, INFORMS, vol. 63(4), pages 916-930, August.
  • Handle: RePEc:inm:oropre:v:63:y:2015:i:4:p:916-930
    DOI: 10.1287/opre.2015.1391
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    References listed on IDEAS

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    Cited by:

    1. Zander, Anne & Nickel, Stefan & Vanberkel, Peter, 2021. "Managing the intake of new patients into a physician panel over time," European Journal of Operational Research, Elsevier, vol. 294(1), pages 391-403.
    2. Izady, Navid, 2019. "An integrated approach to demand and capacity planning in outpatient clinics," European Journal of Operational Research, Elsevier, vol. 279(2), pages 645-656.
    3. 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.
    4. 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.
    5. Kılıç, Hakan & Güneş, Evrim Didem, 2024. "Patient adherence in healthcare operations: A narrative review," Socio-Economic Planning Sciences, Elsevier, vol. 91(C).
    6. Tinglong Dai & Sridhar Tayur, 2020. "OM Forum—Healthcare Operations Management: A Snapshot of Emerging Research," Manufacturing & Service Operations Management, INFORMS, vol. 22(5), pages 869-887, September.
    7. Bowen Jiang & Jiafu Tang & Chongjun Yan, 2019. "A comparison of fixed and variable capacity-addition policies for outpatient capacity allocation," Journal of Combinatorial Optimization, Springer, vol. 37(1), pages 150-182, January.

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