ORIGINAL RESEARCH article
Front. Public Health
Sec. Health Economics
Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1581441
This article is part of the Research TopicPublic Health Outcomes: The Role of Social Security Systems in Improving Residents' Health WelfareView all 88 articles
Optimization of Diagnosis-Related Groups for Patients with Acute Appendicitis Using a Machine Learning Model
Provisionally accepted
- 1Teaching Management Department, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- 2Department of Research Administration Office, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- 3Department of Dean’s Offic, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- 4Department of Health Services Management, School of Health Services Management, Anhui Medical University, Hefei, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Background: The diagnosis-related groups prospective payment system (DRG-PPS) is widely implemented worldwide. Its core components include disease classification and pricing mechanisms. Developing a disease grouping and pricing approach that aligns with local conditions is essential. This study examines the factors influencing hospitalization costs for acute appendicitis (AA) patients and proposes strategies for disease grouping and pricing. Methods: Stratified random sampling was used to select research sites from provincial, municipal, and county hospitals in Hefei, China. Data were obtained from the hospitalization information systems of three hospitals from 2017 to 2019. The primary diagnosis was defined as AA. Single-factor analysis and multiple linear stepwise regression were used to identify the main factors influencing hospitalization costs. Additionally, a classification and regression tree (CART) model, based on the exhaustive chi-square automatic interaction detection (E-CHAID) algorithm, was applied to establish the DRG grouping model. Results: A total of 4,066 patients were included. Significant differences in hospitalization costs were observed based on length of stay (LOS), marital status, surgery, and hospital level (p < 0.05). By incorporating age, type of surgery, and LOS into the CART model, AA inpatients were classified into 10 DRG groups. The standardized disease cost ranged from 3,047 CNY to 15,569 CNY. Conclusion: Hospitalization costs for AA patients are primarily influenced by LOS, marital status, surgery, and hospital level. The decision tree model provides a basis for DRG grouping. Health administration departments may consider implementing precise and individualized hospitalization cost reimbursement mechanisms accordingly.
Keywords: Diagnosis-Related Groups, acute appendicitis, Classification and regression tree, Hospitalization cost, Machine learning model
Received: 22 Feb 2025; Accepted: 19 Aug 2025.
Copyright: © 2025 Gu, Li and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Heng Wang, Department of Dean’s Offic, The First Affiliated Hospital of Anhui Medical University, Hefei, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.