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A Universal Appointment Rule in the Presence of No‐Shows and Walk‐Ins

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  • Tugba Cayirli
  • Kum Khiong Yang
  • Ser Aik Quek

Abstract

This study introduces a universal “Dome” appointment rule that can be parameterized through a planning constant for different clinics characterized by the environmental factors—no‐shows, walk‐ins, number of appointments per session, variability of service times, and cost of doctor's time to patients’ time. Simulation and nonlinear regression are used to derive an equation to predict the planning constant as a function of the environmental factors. We also introduce an adjustment procedure for appointment systems to explicitly minimize the disruptive effects of no‐shows and walk‐ins. The procedure adjusts the mean and standard deviation of service times based on the expected probabilities of no‐shows and walk‐ins for a given target number of patients to be served, and it is thus relevant for any appointment rule that uses the mean and standard deviation of service times to construct an appointment schedule. The results show that our Dome rule with the adjustment procedure performs better than the traditional rules in the literature, with a lower total system cost calculated as a weighted sum of patients’ waiting time, doctor's idle time, and doctor's overtime. An open‐source decision‐support tool is also provided so that healthcare managers can easily develop appointment schedules for their clinical environment.

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  • Tugba Cayirli & Kum Khiong Yang & Ser Aik Quek, 2012. "A Universal Appointment Rule in the Presence of No‐Shows and Walk‐Ins," Production and Operations Management, Production and Operations Management Society, vol. 21(4), pages 682-697, July.
    • Handle: RePEc:bla:popmgt:v:21:y:2012:i:4:p:682-697
    DOI: 10.1111/j.1937-5956.2011.01297.x
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    References listed on IDEAS

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    Cited by:

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    2. Matthias Grot & Simon Kugai & Lukas Degen & Isabel Wiemer & Brigitte Werners & Birgitta M. Weltermann, 2023. "Small Changes in Patient Arrival and Consultation Times Have Large Effects on Patients’ Waiting Times: Simulation Analyses for Primary Care," IJERPH, MDPI, vol. 20(3), pages 1-11, January.
    3. 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.
    4. Kuiper, Alex & de Mast, Jeroen & Mandjes, Michel, 2021. "The problem of appointment scheduling in outpatient clinics: A multiple case study of clinical practice," Omega, Elsevier, vol. 98(C).
    5. Shenghai Zhou & Yichuan Ding & Woonghee Tim Huh & Guohua Wan, 2021. "Constant Job‐Allowance Policies for Appointment Scheduling: Performance Bounds and Numerical Analysis," Production and Operations Management, Production and Operations Management Society, vol. 30(7), pages 2211-2231, July.
    6. Zhou, Shenghai & Li, Debiao & Yin, Yong, 2021. "Coordinated appointment scheduling with multiple providers and patient-and-physician matching cost in specialty care," Omega, Elsevier, vol. 101(C).
    7. Wu, Xueqi & Zhou, Shenghai, 2022. "Sequencing and scheduling appointments on multiple servers with stochastic service durations and customer arrivals," Omega, Elsevier, vol. 106(C).
    8. Creemers, Stefan & Lambrecht, Marc R. & Beliën, Jeroen & Van den Broeke, Maud, 2021. "Evaluation of appointment scheduling rules: A multi-performance measurement approach," Omega, Elsevier, vol. 100(C).
    9. Qing Li & Qiang Su & Chao Xu, 2022. "Surgery scheduling of pelvic fracture patients with stochastic recovery time," Annals of Operations Research, Springer, vol. 318(1), pages 277-321, November.

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