ArticleFrontiers in public health2026
An empirical study of diagnosis-related group classification for hospitalization costs in lumbar disk herniation patients treated in Chinese medicine hospitals in China.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objectives: Lumbar disk herniation (LDH) is a frequent and multiple disease that imposes a heavier global economic burden. As China's diagnosis related group (DRG) payment system for Chinese medicine health insurance has entered the initial stage. Our study explored the factors influencing the hospitalization costs and established DRG cases classification scheme of LDH patients in Chinese medicine hospitals. Methods: A total of 2,857 LDH patients were hospitalized in Qingyang City Hospital of Chinese Medicine from January 2017 to June 2022. Using univariate analysis and multiple linear regression model to determine the primary factors influencing hospitalization costs for LDH patients. These factors were then utilized as classification node variables in a decision tree model to categorize LDH patients into diagnosis related groups (DRGs). The outcomes of these groupings were evaluated, and standard costs were computed for each DRG. Results: Hospitalization costs of patients with LDH were mainly affected by age, admission pathways, diagnosis and treatment based on Chinese medicine evidence, complications and comorbidities, surgery and operation, and length of stay, with the latter being the most crucial one. The decision tree model resulted in 10 DRGs with corresponding standard costs. The intra-group evaluation metric coefficient of variation (CV) was appropriate (required to be less than 1, actual range:0.23-0.91), and the inter-group evaluation metric reduction in variation (RIV) was suitable (needed to be greater than 0.4, actual value is 0.48). Significant variations exist in disease related weight (RW) and standard costs across all groups. Conclusion: The E-CHAID decision tree model grouping results are reasonably robust, showing good homogeneity within each DRG and notable heterogeneity between DRGs. Based on these groupings, scientifically formulate standard costs for each DRG and set upper limits, providing valuable insights to advance reforms in Chinese medicine DRG-based healthcare payment methods.
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