IDEAS home Printed from https://ideas.repec.org/a/inm/orijoc/v35y2023i4p844-868.html
   My bibliography  Save this article

A Prediction-Based Approach for Online Dynamic Appointment Scheduling: A Case Study in Radiotherapy Treatment

Author

Listed:
  • Tu San Pham

    (Polytechnique Montréal, Montréal, Québec H3T 1J4, Canada; CIRRELT, Montréal, Québec H3T 1J4, Canada)

  • Antoine Legrain

    (Polytechnique Montréal, Montréal, Québec H3T 1J4, Canada; CIRRELT, Montréal, Québec H3T 1J4, Canada; GERAD, Montréal, Québec H3T 2A7, Canada)

  • Patrick De Causmaecker

    (KU Leuven, 8500 Kortrijk, Belgium)

  • Louis-Martin Rousseau

    (Polytechnique Montréal, Montréal, Québec H3T 1J4, Canada; CIRRELT, Montréal, Québec H3T 1J4, Canada)

Abstract

Patient scheduling is a difficult task involving stochastic factors, such as the unknown arrival times of patients. Similarly, the scheduling of radiotherapy for cancer treatments needs to handle patients with different urgency levels when allocating resources. High-priority patients may arrive at any time, and there must be resources available to accommodate them. A common solution is to reserve a flat percentage of treatment capacity for emergency patients. However, this solution can result in overdue treatments for urgent patients, a failure to fully exploit treatment capacity, and delayed treatments for low-priority patients. This problem is especially severe in large and crowded hospitals. In this paper, we propose a prediction-based approach for online dynamic radiotherapy scheduling that dynamically adapts the present scheduling decision based on each incoming patient and the current allocation of resources. Our approach is based on a regression model trained to recognize the links between patients’ arrival patterns and their ideal waiting time in optimal off-line solutions when all future arrivals are known in advance. When our prediction-based approach is compared with flat-reservation policies, it does a better job of preventing overdue treatments for emergency patients and also maintains comparable waiting times for the other patients. We also demonstrate how our proposed approach supports explainability and interpretability in scheduling decisions using Shapley additive explanation values.

Suggested Citation

  • Tu San Pham & Antoine Legrain & Patrick De Causmaecker & Louis-Martin Rousseau, 2023. "A Prediction-Based Approach for Online Dynamic Appointment Scheduling: A Case Study in Radiotherapy Treatment," INFORMS Journal on Computing, INFORMS, vol. 35(4), pages 844-868, July.
  • Handle: RePEc:inm:orijoc:v:35:y:2023:i:4:p:844-868
    DOI: 10.1287/ijoc.2023.1289
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/ijoc.2023.1289
    Download Restriction: no

    File URL: https://libkey.io/10.1287/ijoc.2023.1289?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Schütz, Hans-Jörg & Kolisch, Rainer, 2012. "Approximate dynamic programming for capacity allocation in the service industry," European Journal of Operational Research, Elsevier, vol. 218(1), pages 239-250.
    2. Sauré, Antoine & Begen, Mehmet A. & Patrick, Jonathan, 2020. "Dynamic multi-priority, multi-class patient scheduling with stochastic service times," European Journal of Operational Research, Elsevier, vol. 280(1), pages 254-265.
    3. Marynissen, Joren & Demeulemeester, Erik, 2019. "Literature review on multi-appointment scheduling problems in hospitals," European Journal of Operational Research, Elsevier, vol. 272(2), pages 407-419.
    4. Sauré, Antoine & Patrick, Jonathan & Tyldesley, Scott & Puterman, Martin L., 2012. "Dynamic multi-appointment patient scheduling for radiation therapy," European Journal of Operational Research, Elsevier, vol. 223(2), pages 573-584.
    5. Jonathan Patrick & Martin L. Puterman & Maurice Queyranne, 2008. "Dynamic Multipriority Patient Scheduling for a Diagnostic Resource," Operations Research, INFORMS, vol. 56(6), pages 1507-1525, December.
    6. Petra Vogl & Roland Braune & Karl F. Doerner, 2019. "Scheduling recurring radiotherapy appointments in an ion beam facility," Journal of Scheduling, Springer, vol. 22(2), pages 137-154, April.
    7. Lamiri, Mehdi & Xie, Xiaolan & Dolgui, Alexandre & Grimaud, Frederic, 2008. "A stochastic model for operating room planning with elective and emergency demand for surgery," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1026-1037, March.
    8. Yigal Gerchak & Diwakar Gupta & Mordechai Henig, 1996. "Reservation Planning for Elective Surgery Under Uncertain Demand for Emergency Surgery," Management Science, INFORMS, vol. 42(3), pages 321-334, March.
    9. Tu-San Pham & Louis-Martin Rousseau & Patrick Causmaecker, 2022. "A two-phase approach for the Radiotherapy Scheduling Problem," Health Care Management Science, Springer, vol. 25(2), pages 191-207, June.
    10. Conforti, D. & Guerriero, F. & Guido, R., 2010. "Non-block scheduling with priority for radiotherapy treatments," European Journal of Operational Research, Elsevier, vol. 201(1), pages 289-296, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sauré, Antoine & Patrick, Jonathan & Tyldesley, Scott & Puterman, Martin L., 2012. "Dynamic multi-appointment patient scheduling for radiation therapy," European Journal of Operational Research, Elsevier, vol. 223(2), pages 573-584.
    2. Yasin Gocgun & Martin Puterman, 2014. "Dynamic scheduling with due dates and time windows: an application to chemotherapy patient appointment booking," Health Care Management Science, Springer, vol. 17(1), pages 60-76, March.
    3. 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.
    4. Camila Ramos & Alejandro Cataldo & Juan–Carlos Ferrer, 2020. "Appointment and patient scheduling in chemotherapy: a case study in Chilean hospitals," Annals of Operations Research, Springer, vol. 286(1), pages 411-439, March.
    5. Gartner, Daniel & Kolisch, Rainer, 2014. "Scheduling the hospital-wide flow of elective patients," European Journal of Operational Research, Elsevier, vol. 233(3), pages 689-699.
    6. Bruno Vieira & Derya Demirtas & Jeroen B. Kamer & Erwin W. Hans & Louis-Martin Rousseau & Nadia Lahrichi & Wim H. Harten, 2020. "Radiotherapy treatment scheduling considering time window preferences," Health Care Management Science, Springer, vol. 23(4), pages 520-534, December.
    7. Kaining Shao & Wenjuan Fan & Zishu Yang & Shanlin Yang & Panos M. Pardalos, 2022. "A column generation approach for patient scheduling with setup time and deteriorating treatment duration," Operational Research, Springer, vol. 22(3), pages 2555-2586, July.
    8. Gökalp, E. & Gülpınar, N. & Doan, X.V., 2023. "Dynamic surgery management under uncertainty," European Journal of Operational Research, Elsevier, vol. 309(2), pages 832-844.
    9. Tu-San Pham & Louis-Martin Rousseau & Patrick Causmaecker, 2022. "A two-phase approach for the Radiotherapy Scheduling Problem," Health Care Management Science, Springer, vol. 25(2), pages 191-207, June.
    10. Silva, Thiago A.O. & de Souza, Mauricio C., 2020. "Surgical scheduling under uncertainty by approximate dynamic programming," Omega, Elsevier, vol. 95(C).
    11. Ridvan Gedik & Shengfan Zhang & Chase Rainwater, 2017. "Strategic level proton therapy patient admission planning: a Markov decision process modeling approach," Health Care Management Science, Springer, vol. 20(2), pages 286-302, June.
    12. Yongbo Xiao & Yan Zhu, 2016. "Value management of diagnostic equipment with cancelation, no‐show, and emergency patients," Naval Research Logistics (NRL), John Wiley & Sons, vol. 63(4), pages 287-304, June.
    13. Hans-Jörg Schütz & Rainer Kolisch, 2013. "Capacity allocation for demand of different customer-product-combinations with cancellations, no-shows, and overbooking when there is a sequential delivery of service," Annals of Operations Research, Springer, vol. 206(1), pages 401-423, July.
    14. Astaraky, Davood & Patrick, Jonathan, 2015. "A simulation based approximate dynamic programming approach to multi-class, multi-resource surgical scheduling," European Journal of Operational Research, Elsevier, vol. 245(1), pages 309-319.
    15. Xiang Ma & Antoine Sauré & Martin L. Puterman & Marianne Taylor & Scott Tyldesley, 2016. "Capacity planning and appointment scheduling for new patient oncology consults," Health Care Management Science, Springer, vol. 19(4), pages 347-361, December.
    16. Yasin Gocgun, 2018. "Simulation-based approximate policy iteration for dynamic patient scheduling for radiation therapy," Health Care Management Science, Springer, vol. 21(3), pages 317-325, September.
    17. Antoine Sauré & Jonathan Patrick & Martin L. Puterman, 2015. "Simulation-Based Approximate Policy Iteration with Generalized Logistic Functions," INFORMS Journal on Computing, INFORMS, vol. 27(3), pages 579-595, August.
    18. Marquinez, José Tomás & Sauré, Antoine & Cataldo, Alejandro & Ferrer, Juan-Carlos, 2021. "Identifying proactive ICU patient admission, transfer and diversion policies in a public-private hospital network," European Journal of Operational Research, Elsevier, vol. 295(1), pages 306-320.
    19. Shuwan Zhu & Wenjuan Fan & Shanlin Yang & Jun Pei & Panos M. Pardalos, 2019. "Operating room planning and surgical case scheduling: a review of literature," Journal of Combinatorial Optimization, Springer, vol. 37(3), pages 757-805, April.
    20. Liping Zhou & Na Geng & Zhibin Jiang & Shan Jiang, 2022. "Integrated Multiresource Capacity Planning and Multitype Patient Scheduling," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 129-149, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:orijoc:v:35:y:2023:i:4:p:844-868. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.