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A Data-Driven Model of an Appointment-Generated Arrival Process at an Outpatient Clinic

Author

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  • Song-Hee Kim

    (Data Sciences and Operations, Marshall School of Business, University of Southern California, Los Angeles, California 90089)

  • Ward Whitt

    (Industrial Engineering and Operations Research, Columbia University, New York, New York 10027)

  • Won Chul Cha

    (Department of Emergency Medicine, Samsung Medical Center, Seoul, Korea)

Abstract

We develop a high-fidelity simulation model of the patient arrival process to an endocrinology clinic by carefully examining appointment and arrival data from that clinic. The data include the time that the appointment was originally made as well as the time that the patient actually arrived, as well as if the patient did not arrive at all, in addition to the scheduled appointment time. We take a data-based approach, specifying the schedule for each day by its value at the end of the previous day. This data-based approach shows that the schedule for a given day evolves randomly over time. Indeed, in addition to three recognized sources of variability—(i) no-shows, (ii) extra unscheduled arrivals, and (iii) deviations in the actual arrival times from the scheduled times—we find that the primary source of variability in the arrival process is variability in the daily schedule itself. Even though service systems with arrivals by appointment can differ in many ways, we think that our data-based approach to modeling the clinic arrival process can be a guideline or template for constructing high-fidelity simulation models for other arrival processes generated by appointments.

Suggested Citation

  • Song-Hee Kim & Ward Whitt & Won Chul Cha, 2018. "A Data-Driven Model of an Appointment-Generated Arrival Process at an Outpatient Clinic," INFORMS Journal on Computing, INFORMS, vol. 30(1), pages 181-199, February.
    • Handle: RePEc:inm:orijoc:v:30:y:2018:i:1:p:181-199
    DOI: 10.1287/ijoc.2017.0773
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    References listed on IDEAS

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    Cited by:

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    3. Britt W. J. Mathijsen & A. J. E. M. Janssen & Johan S. H. Leeuwaarden & Bert Zwart, 2018. "Robust heavy-traffic approximations for service systems facing overdispersed demand," Queueing Systems: Theory and Applications, Springer, vol. 90(3), pages 257-289, December.
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    6. 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.
    7. Dina Bentayeb & Nadia Lahrichi & Louis-Martin Rousseau, 2019. "Patient scheduling based on a service-time prediction model: a data-driven study for a radiotherapy center," Health Care Management Science, Springer, vol. 22(4), pages 768-782, December.
    8. Azam Asanjarani & Yoni Nazarathy & Peter Taylor, 2021. "A survey of parameter and state estimation in queues," Queueing Systems: Theory and Applications, Springer, vol. 97(1), pages 39-80, February.
    9. Oualid Jouini & Saif Benjaafar & Bingnan Lu & Siqiao Li & Benjamin Legros, 2022. "Appointment-driven queueing systems with non-punctual customers," Queueing Systems: Theory and Applications, Springer, vol. 101(1), pages 1-56, June.
    10. Mor Armony & Rami Atar & Harsha Honnappa, 2019. "Asymptotically Optimal Appointment Schedules," Management Science, INFORMS, vol. 44(4), pages 1345-1380, November.
    11. Junfei Huang & Avishai Mandelbaum & Petar Momčilović, 2022. "Appointment-driven service systems with many servers," Queueing Systems: Theory and Applications, Springer, vol. 100(3), pages 529-531, April.

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