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Outpatient Appointment Block Scheduling Under Patient Heterogeneity and Patient No†Shows

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  • Seung Jun Lee
  • Gregory R. Heim
  • Chelliah Sriskandarajah
  • Yunxia Zhu

Abstract

We study outpatient appointment block scheduling policies for single providers under conditions of patient heterogeneity in service times and patient no†shows. The objective is to find daily appointment schedules that minimize a weighted sum of patients’ waiting time, the physician's idle time, and the physician's overtime. We contribute by suggesting new effective sequential block scheduling procedures motivated by actual outpatient clinic practices across the globe and grounded in the successful Toyota Production System load smoothing approach. Our block scheduling policy first assigns a sequence of different patient types within a time block. The policy then allocates repetitive blocks across a planning horizon. We start our analysis by studying the case with zero probability of no†shows. Under the setting that the physician's idle time is zero, we propose a polynomial time optimal scheduling approach for two patient types, before demonstrating that the problem with at least three patient types is NP†Hard. Various extensions to incorporate practical outpatient clinic environment dimensions are considered. We then extend our scheduling approach to incorporate reasonable patient no†show probabilities. Finally, our block scheduling approach is adapted for scenarios where outpatient clinics use an open†access scheduling environment, where patients make same†day appointments. We compare our block scheduling policies against extant scheduling policy, finding our block scheduling policies surpass the benchmark method.

Suggested Citation

  • Seung Jun Lee & Gregory R. Heim & Chelliah Sriskandarajah & Yunxia Zhu, 2018. "Outpatient Appointment Block Scheduling Under Patient Heterogeneity and Patient No†Shows," Production and Operations Management, Production and Operations Management Society, vol. 27(1), pages 28-48, January.
  • Handle: RePEc:bla:popmgt:v:27:y:2018:i:1:p:28-48
    DOI: 10.1111/poms.12791
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    Citations

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    Cited by:

    1. Murtaza Nasir & Nichalin Summerfield & Ali Dag & Asil Oztekin, 2020. "A service analytic approach to studying patient no-shows," Service Business, Springer;Pan-Pacific Business Association, vol. 14(2), pages 287-313, June.
    2. 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.
    3. Harris, Shannon L. & May, Jerrold H. & Vargas, Luis G. & Foster, Krista M., 2020. "The effect of cancelled appointments on outpatient clinic operations," European Journal of Operational Research, Elsevier, vol. 284(3), pages 847-860.
    4. Alireza F. Hesaraki & Nico P. Dellaert & Ton Kok, 2023. "Online scheduling using a fixed template: the case of outpatient chemotherapy drug administration," Health Care Management Science, Springer, vol. 26(1), pages 117-137, March.
    5. Haolin Feng & Yiwu Jia & Siyi Zhou & Hongyi Chen & Teng Huang, 2023. "A Dataset of Service Time and Related Patient Characteristics from an Outpatient Clinic," Data, MDPI, vol. 8(3), pages 1-15, February.
    6. Martin Bichler & Soeren Merting, 2021. "Randomized Scheduling Mechanisms: Assigning Course Seats in a Fair and Efficient Way," Production and Operations Management, Production and Operations Management Society, vol. 30(10), pages 3540-3559, October.
    7. Agrawal, Deepak & Pang, Guodong & Kumara, Soundar, 2023. "Preference based scheduling in a healthcare provider network," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1318-1335.
    8. Golmohammadi, Davood & Zhao, Lingyu & Dreyfus, David, 2023. "Using machine learning techniques to reduce uncertainty for outpatient appointment scheduling practices in outpatient clinics," Omega, Elsevier, vol. 120(C).

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