IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v318y2022i1d10.1007_s10479-022-04850-w.html
   My bibliography  Save this article

Surgery scheduling of pelvic fracture patients with stochastic recovery time

Author

Listed:
  • Qing Li

    (Shanghai University of Political Science and Law)

  • Qiang Su

    (Tongji University)

  • Chao Xu

    (Xi’an University of Posts and Telecommunications)

Abstract

Pelvic fracture is a severe trauma and is often seen in the traffic accidents, which are associated with complications or multiple injuries. Surgery is the main treatment for patients with serious conditions, while conservative treatment is adopted for older or minor-illness patients. Surgery resources, such as doctors, nurses, and operating rooms, are shared by all pelvic fracture patients. From the perspective of patient state, this paper divides patients who require surgery into two types, convalescent patients and scheduled patients. Convalescent patients’ life states are always unstable, and they require recovery time to meet the condition of surgery. The recovery time is usually stochastic due to different patient situations. Scheduled patients have stable life states, and the pelvic fracture surgical plan is scheduled days or weeks in advance. Considering the characteristics of the two types of patients, a finite-horizon Markov decision process (MDP) model is established. With data collected from the hospital, parameters are set and experiments are designed to reveal the dynamic priority rules for receiving patients into surgery. Performances of different scenarios are compared, and the optimal policies obtained from the MDP are analyzed.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:annopr:v:318:y:2022:i:1:d:10.1007_s10479-022-04850-w
    DOI: 10.1007/s10479-022-04850-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-022-04850-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10479-022-04850-w?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ramirez-Nafarrate, Adrian & Baykal Hafizoglu, A. & Gel, Esma S. & Fowler, John W., 2014. "Optimal control policies for ambulance diversion," European Journal of Operational Research, Elsevier, vol. 236(1), pages 298-312.
    2. Guido Kaandorp & Ger Koole, 2007. "Optimal outpatient appointment scheduling," Health Care Management Science, Springer, vol. 10(3), pages 217-229, September.
    3. 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.
    4. Guanlian Xiao & Ming Dong & Jing Li & Liya Sun, 2017. "Scheduling routine and call-in clinical appointments with revisits," International Journal of Production Research, Taylor & Francis Journals, vol. 55(6), pages 1767-1779, March.
    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. Scott Carr & Izak Duenyas, 2000. "Optimal Admission Control and Sequencing in a Make-to-Stock/Make-to-Order Production System," Operations Research, INFORMS, vol. 48(5), pages 709-720, October.
    7. Linda V. Green & Sergei Savin & Ben Wang, 2006. "Managing Patient Service in a Diagnostic Medical Facility," Operations Research, INFORMS, vol. 54(1), pages 11-25, 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. Van-Anh Truong, 2015. "Optimal Advance Scheduling," Management Science, INFORMS, vol. 61(7), pages 1584-1597, July.
    2. Katsumi Morikawa & Katsuhiko Takahashi & Daisuke Hirotani, 2018. "Performance evaluation of candidate appointment schedules using clearing functions," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 509-518, March.
    3. 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.
    4. Paola Cappanera & Filippo Visintin & Carlo Banditori & Daniele Feo, 2019. "Evaluating the long-term effects of appointment scheduling policies in a magnetic resonance imaging setting," Flexible Services and Manufacturing Journal, Springer, vol. 31(1), pages 212-254, March.
    5. 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.
    6. 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.
    7. Mehmet A. Begen & Maurice Queyranne, 2011. "Appointment Scheduling with Discrete Random Durations," Mathematics of Operations Research, INFORMS, vol. 36(2), pages 240-257, May.
    8. Gang Du & Xinyue Li & Hui Hu & Xiaoling Ouyang, 2018. "Optimizing Daily Service Scheduling for Medical Diagnostic Equipment Considering Patient Satisfaction and Hospital Revenue," Sustainability, MDPI, vol. 10(9), pages 1-23, September.
    9. Pan, Xingwei & Geng, Na & Xie, Xiaolan & Wen, Jing, 2020. "Managing appointments with waiting time targets and random walk-ins," Omega, Elsevier, vol. 95(C).
    10. 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).
    11. Saied Samiedaluie & Beste Kucukyazici & Vedat Verter & Dan Zhang, 2017. "Managing Patient Admissions in a Neurology Ward," Operations Research, INFORMS, vol. 65(3), pages 635-656, June.
    12. 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.
    13. E. Lerzan Örmeci & Evrim Didem Güneş & Derya Kunduzcu, 2016. "A Modeling Framework for Control of Preventive Services," Manufacturing & Service Operations Management, INFORMS, vol. 18(2), pages 227-244, May.
    14. Yi Du & Hua Yu & Zhijun Li, 0. "Research of SVM ensembles in medical examination scheduling," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-11.
    15. 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.
    16. Lee, Hyun-Rok & Lee, Taesik, 2021. "Multi-agent reinforcement learning algorithm to solve a partially-observable multi-agent problem in disaster response," European Journal of Operational Research, Elsevier, vol. 291(1), pages 296-308.
    17. Namakshenas, Mohammad & Mazdeh, Mohammad Mahdavi & Braaksma, Aleida & Heydari, Mehdi, 2023. "Appointment scheduling for medical diagnostic centers considering time-sensitive pharmaceuticals: A dynamic robust optimization approach," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1018-1031.
    18. Dongyang Wang & Kumar Muthuraman & Douglas Morrice, 2019. "Coordinated Patient Appointment Scheduling for a Multistation Healthcare Network," Operations Research, INFORMS, vol. 67(3), pages 599-618, May.
    19. 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).
    20. Xinshang Wang & Van-Anh Truong, 2018. "Multi-Priority Online Scheduling with Cancellations," Operations Research, INFORMS, vol. 66(1), pages 104-122, 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:spr:annopr:v:318:y:2022:i:1:d:10.1007_s10479-022-04850-w. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.