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Matching patients and healthcare service providers: a novel two-stage method based on knowledge rules and OWA-NSGA-II algorithm

Author

Listed:
  • Xi Chen

    (Xidian University)

  • Liu Zhao

    (Xidian University)

  • Haiming Liang

    (Xidian University)

  • Kin Keung Lai

    (City University of Hong Kong)

Abstract

The matching between patients and healthcare service providers is an important issue in healthcare. Searching an appropriate matching for both patients and healthcare service providers can not only facilitate efficiency of diagnosis and treatment, but also make both of them more satisfied with the matching results. This paper proposes a two-stage method for searching the optimal matching between the patients and healthcare service providers. In the first stage, where a large number of patients are involved in the matching problem, the knowledge rules are proposed to classify the patients with similar categories of disease into the same group. In the second stage, patients in each group are compared in terms of aspiration levels and the evaluation levels of the healthcare service providers, and satisfaction degrees of patients are calculated. Then, a multi-objective optimization model is built by maximizing the satisfaction degrees of patients, maximizing the number of treated patients and balancing the workload of healthcare service providers. To solve this model, the ordinal weighting average non-dominated sorting genetic algorithm II (OWA-NSGA-II) is developed. Furthermore, a practical example of service in rehabilitation therapy is used to illustrate the feasibility of the proposed method. Additionally, several simulation experiments in different large scale problems are conducted to test the performance of OWA-NSGA-II. Simulation results show that the proposed NSGA-II algorithm has better convergence in the large scale problem, yields a more stable distribution of non-dominated solutions, as well as non-dominated solutions much faster.

Suggested Citation

  • Xi Chen & Liu Zhao & Haiming Liang & Kin Keung Lai, 2019. "Matching patients and healthcare service providers: a novel two-stage method based on knowledge rules and OWA-NSGA-II algorithm," Journal of Combinatorial Optimization, Springer, vol. 37(1), pages 221-247, January.
    • Handle: RePEc:spr:jcomop:v:37:y:2019:i:1:d:10.1007_s10878-017-0221-2
    DOI: 10.1007/s10878-017-0221-2
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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).
    2. Ling Gai & Zhiyue Peng & Jiming Zhang & Jiafu Zhang, 2021. "Emergency medical center location problem with people evacuation solved by extended TODIM and objective programming," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 1004-1029, November.
    3. Xuanzhu Fan & Jiafu Tang & Chongjun Yan & Hainan Guo & Zhongfa Cao, 2021. "Outpatient appointment scheduling problem considering patient selection behavior: data modeling and simulation optimization," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 677-699, November.
    4. Gengjun Gao & Zhen Wu & Shuyun Wang, 2022. "Study on the incentive and coordination mechanism of tumor healthcare alliance based on evolutionary game," Journal of Combinatorial Optimization, Springer, vol. 44(4), pages 2577-2597, November.
    5. Xuanzhu Fan & Jiafu Tang & Chongjun Yan & Hainan Guo & Zhongfa Cao, 0. "Outpatient appointment scheduling problem considering patient selection behavior: data modeling and simulation optimization," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-23.
    6. Ling Gai & Zhiyue Peng & Jiming Zhang & Jiafu Zhang, 0. "Emergency medical center location problem with people evacuation solved by extended TODIM and objective programming," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-26.

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