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Optimising the booking horizon in healthcare clinics considering no-shows and cancellations

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  • Gréanne Leeftink
  • Gabriela Martinez
  • Erwin W. Hans
  • Mustafa Y. Sir
  • Kalyan S. Pasupathy

Abstract

Patient no-shows and cancellations are a significant problem to healthcare clinics, as they compromise a clinic's efficiency. Therefore, it is important to account for both no-shows and cancellations into the design of appointment systems. To provide additional empirical evidence on no-show and cancellation behaviour, we assess outpatient clinic data from two healthcare providers in the USA and EU: no-show and cancellation rates increase with the scheduling interval, which is the number of days from the appointment creation to the date the appointment is scheduled for. We show the temporal cancellation behaviour for multiple scheduling intervals is bimodally distributed. To improve the efficiency of clinics at a tactical level of control, we determine the optimal booking horizon such that the impact of no-shows and cancellations through high scheduling intervals is minimised, against a cost of rejecting patients. Where the majority of the literature only includes a fixed no-show rate, we include both a cancellation rate and a time-dependent no-show rate. We propose an analytical queuing model with balking and reneging, to determine the optimal booking horizon. Simulation experiments show that the assumptions of this model are viable. Computational results demonstrate general applicability of our model by case studies of two hospitals.

Suggested Citation

  • Gréanne Leeftink & Gabriela Martinez & Erwin W. Hans & Mustafa Y. Sir & Kalyan S. Pasupathy, 2022. "Optimising the booking horizon in healthcare clinics considering no-shows and cancellations," International Journal of Production Research, Taylor & Francis Journals, vol. 60(10), pages 3201-3218, May.
    • Handle: RePEc:taf:tprsxx:v:60:y:2022:i:10:p:3201-3218
    DOI: 10.1080/00207543.2021.1913292
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    Cited by:

    1. Kılıç, Hakan & Güneş, Evrim Didem, 2024. "Patient adherence in healthcare operations: A narrative review," Socio-Economic Planning Sciences, Elsevier, vol. 91(C).
    2. Golmohammadi, Davood & Zhao, Lingyu & Dreyfus, David, 2023. "Using machine learning techniques to reduce uncertainty for outpatient appointment scheduling practices in outpatient clinics," Omega, Elsevier, vol. 120(C).
    3. Kazim Topuz & Timothy L. Urban & Robert A. Russell & Mehmet B. Yildirim, 2024. "Decision support system for appointment scheduling and overbooking under patient no-show behavior," Annals of Operations Research, Springer, vol. 342(1), pages 845-873, November.

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