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A stochastic appointment scheduling system on multiple resources with dynamic call-in sequence and patient no-shows for an outpatient clinic

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  • Tsai, Pei-Fang Jennifer
  • Teng, Guei-Yu

Abstract

This research focuses on the stochastic assignment system motivated by outpatient clinics, especially the physical therapy in rehabilitation service. The aim of this research is to develop a stochastic overbooking model to enhance the service quality as well as to increase the utilization of multiple resources, like therapy equipment in a physical therapy room, with the consideration of patients’ call-in sequence. The schedule for a single-service period includes a fixed number of blocks of equal length. When patients call, they are assigned to an appointment time for that block, and an existing appointment is not allowed to be changed. In each visit, a patient might require more than one resource and a probability of no-show. Two estimation methods were proposed for the expected waiting and overtime cost with multiple resources: Convolution Estimation Method and Joint Cumulative Estimation Method for the upper and lower bound value; respectively. A numerical example based on a physical therapy room was used to show that this stochastic model was able to schedule patients for better profitability compared with traditional appointment systems based on four prioritization rules. The workload in each appointment slot was more balanced albeit more patients were assigned to the first slot to fill up the empty room.

Suggested Citation

  • Tsai, Pei-Fang Jennifer & Teng, Guei-Yu, 2014. "A stochastic appointment scheduling system on multiple resources with dynamic call-in sequence and patient no-shows for an outpatient clinic," European Journal of Operational Research, Elsevier, vol. 239(2), pages 427-436.
    • Handle: RePEc:eee:ejores:v:239:y:2014:i:2:p:427-436
    DOI: 10.1016/j.ejor.2014.04.032
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    Cited by:

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    2. 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.
    3. Shnits, Boris & Bendavid, Illana & Marmor, Yariv N., 2020. "An appointment scheduling policy for healthcare systems with parallel servers and pre-determined quality of service," Omega, Elsevier, vol. 97(C).
    4. Zhou, Wenhui & Wan, Qiang & Zhang, Ren-Qian, 2017. "Choosing among hospitals in the subsidized health insurance system of China: A sequential game approach," European Journal of Operational Research, Elsevier, vol. 257(2), pages 568-585.
    5. A. G. Leeftink & I. M. H. Vliegen & E. W. Hans, 2019. "Stochastic integer programming for multi-disciplinary outpatient clinic planning," Health Care Management Science, Springer, vol. 22(1), pages 53-67, March.
    6. Yu Fu & Amarnath Banerjee, 2021. "A Stochastic Programming Model for Service Scheduling with Uncertain Demand: an Application in Open-Access Clinic Scheduling," SN Operations Research Forum, Springer, vol. 2(3), pages 1-32, September.
    7. Hyun-Jung Alvarez-Oh & Hari Balasubramanian & Ekin Koker & Ana Muriel, 2018. "Stochastic Appointment Scheduling in a Team Primary Care Practice with Two Flexible Nurses and Two Dedicated Providers," Service Science, INFORMS, vol. 10(3), pages 241-260, September.
    8. 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.
    9. Bowen Jiang & Jiafu Tang & Chongjun Yan, 2019. "A comparison of fixed and variable capacity-addition policies for outpatient capacity allocation," Journal of Combinatorial Optimization, Springer, vol. 37(1), pages 150-182, January.

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