[1]杜 飒,郭志武,吴志强,等.中医优势病种DIP支付辅助目录调整机制研究[J].卫生经济研究,2026,43(09):81-83,89.
　DU Sa,GUO Zhiwu,WU Zhiqiang,et al.Research on the Adjustment Mechanism of Auxiliary Payment Catalog for Dominant Traditional Chinese Medicine Disease Groups under DIP Payment[J].Journal Press of Health Economics Research,2026,43(09):81-83,89.
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中医优势病种DIP支付辅助目录调整机制研究

卫生经济研究[ISSN:1004-7778/CN:33-1056/F]

卷:
43
期数:
2026年09期
页码:
81-83,89
栏目:
医疗保障
出版日期:
2026-09-02

文章信息/Info

Title:
Research on the Adjustment Mechanism of Auxiliary Payment Catalog for Dominant Traditional Chinese Medicine Disease Groups under DIP Payment
作者:
杜 飒1; 郭志武1; 吴志强1; 钟晓玲1; 郝琳慧2; 艾 莉3; 金贤德4; 梁土亮1; 徐明珍1
1.广州中医药大学第四临床医学院，广东 深圳 518033
2.深圳市宝安区中医院，广东 深圳 518100
3.深圳市罗湖区中医院，广东 深圳 518001
4.北京中医药大学深圳医院，广东 深圳 518100
Author(s):
DU Sa;  GUO Zhiwu;  WU Zhiqiang;  ZHONG Xiaoling;  HAO Linhui;  AI Li;  JIN Xiande;  LIANG Tuliang;  XU Mingzhen
The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen Guangdong 518033, China
关键词:
DIP支付; 中医优势病种; 变异系数; 住院费用
Keywords:
DIP payment;  dominant traditional Chinese medicine disease groups;  coefficient of variation;  hospitalization costs
分类号:
R19
文献标志码:
A
摘要:
目的：分析在DIP支付下，中医优势病种的资源消耗差异。方法：收集S市4家三甲综合中医医院2023年中医优势病种病例36 042例，涵盖132个中医优势病种；采用SPSS 25.0软件进行数据分析，以变异系数（CV）评价病种分布特征；对典型病种进行多元线性回归，分析住院费用的影响因素。结果：132个病种中，28个病种CV>0.60，提示同一病种的病例同质性偏低，“同病异耗”现象突出；多元回归分析显示，合并急性呼吸衰竭、合并感染性休克、抢救、转科、住院天数等因素会显著推高住院费用（P<0.05）。结论：建立CV阈值触发的中医优势病种辅助目录动态修订机制，对高离散度病种的医保支付实施差异化校正；基于“疾病严重程度—治疗复杂度”动态调整中医优势病种权重系数，实现医保对中医优势病种的精准支付；引入中医特色变量并完善数据接口，提高中医优势病种支付权重与真实资源消耗的匹配度。
Abstract:
Objective To analyze the resource consumption variations among dominant traditional Chinese medicine (TCM) disease groups under DIP payment. Methods A total of 36 042 cases from four tertiary TCM hospitals in S City in 2023 were collected, covering 132 dominant TCM disease groups. SPSS 25.0 was used for data analysis, with the coefficient of variation(CV) applied to evaluate the homogeneity of disease groups. Multiple linear regression was performed on typical disease groups to identify factors influencing hospitalization costs. Results Among the 132 disease groups, 28 had a CV greater than 0.60, indicating low within-group homogeneity and prominent "same disease, different consumption" patterns. Results of multiple regression analysis showed that factors such as acute respiratory failure, septic shock, rescue, transfer, and length of hospital stay significantly increased hospitalization costs(P<0.05). Conclusion It is recommended to establish a dynamic revision mechanism for the auxiliary catalog triggered by CV thresholds, implementing differentiated adjustments for high-discrepancy disease groups. Based on disease severity and treatment complexity, the weight coefficients of dominant TCM disease groups should be dynamically adjusted to achieve precise payment. TCM-specific variables should be introduced and data interfaces improved to enhance the alignment between payment weights and actual resource consumption.

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更新日期/Last Update: 2026-09-02