IDEAS home Printed from https://ideas.repec.org/a/gam/jstats/v8y2025i3p54-d1690793.html
   My bibliography  Save this article

A Data-Driven Approach of DRG-Based Medical Insurance Payment Policy Formulation in China Based on an Optimization Algorithm

Author

Listed:
  • Kun Ba

    (Department of Automation, Tsinghua University, Beijing 100084, China)

  • Biqing Huang

    (Department of Automation, Tsinghua University, Beijing 100084, China)

Abstract

The diagnosis-related group (DRG) system classifies patients into different groups in order to facilitate decisions regarding medical insurance payments. Currently, more than 600 standard DRGs exist in China. Payment details represented by DRG weights must be adjusted during decision-making. After modeling the DRG weight-determining process as a parameter-searching and optimization-solving problem, we propose a stochastic gradient tracking algorithm (SGT) and compare it with a genetic algorithm and sequential quadratic programming. We describe diagnosis-related groups in China using several statistics based on sample data from one city. We explored the influence of the SGT hyperparameters through numerous experiments and demonstrated the robustness of the best SGT hyperparameter combination. Our stochastic gradient tracking algorithm finished the parameter search in only 3.56 min when the insurance payment rate was set at 95%, which is acceptable and desirable. As the main medical insurance payment scheme in China, DRGs require quantitative evidence for policymaking. The optimization algorithm proposed in this study shows a possible scientific decision-making method for use in the DRG system, particularly with regard to DRG weights.

Suggested Citation

  • Kun Ba & Biqing Huang, 2025. "A Data-Driven Approach of DRG-Based Medical Insurance Payment Policy Formulation in China Based on an Optimization Algorithm," Stats, MDPI, vol. 8(3), pages 1-14, June.
    • Handle: RePEc:gam:jstats:v:8:y:2025:i:3:p:54-:d:1690793
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-905X/8/3/54/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2571-905X/8/3/54/
    Download Restriction: no
    ---><---

    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:gam:jstats:v:8:y:2025:i:3:p:54-:d:1690793. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.