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Satisfied surgeon–patient matching: a model-based method

Author

Listed:
  • Yang Liu

    (Northeastern University)

  • Zhi-Ping Fan

    (Northeastern University
    Northeastern University)

  • Yan-Ping Jiang

    (Northeastern University)

Abstract

To support elective surgery scheduling, a surgery assignment plan often needs to be determined beforehand, while a good plan is actually a satisfied matching between surgeons and patients. How to match surgeons and patients considering constrains on the surgeons’ service ability and subjective preferences of the surgeons and patients is a noteworthy research issue, but the in-depth study on this issue is still lacking. The objective of this paper is to develop a method for satisfied matching between surgeons and patients. In the method, surgeons’ satisfaction degrees are calculated according to the preferences of surgeons concerning different types of surgeries, and meanwhile patients’ satisfaction degrees are calculated according to the preferences of patients concerning different surgeons. Then, given the constrains on the surgeons’ service ability, a bi-objective optimization model is constructed to maximize the surgeons’ total satisfaction degree and patients’ total satisfaction degree. Further, an algorithm for solving the model is given. By solving the model, the satisfied matching can be determined. Finally, a case study is given to illustrate the feasibility and validity of the proposed method.

Suggested Citation

  • Yang Liu & Zhi-Ping Fan & Yan-Ping Jiang, 2018. "Satisfied surgeon–patient matching: a model-based method," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(6), pages 2871-2891, November.
    • Handle: RePEc:spr:qualqt:v:52:y:2018:i:6:d:10.1007_s11135-018-0714-9
    DOI: 10.1007/s11135-018-0714-9
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    References listed on IDEAS

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    Cited by:

    1. Zhao, Meng & Wang, Yajun & Zhang, Xueyi & Xu, Chang, 2023. "Online doctor-patient dynamic stable matching model based on regret theory under incomplete information," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).

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