基于DRG-PPS的乙肝中医诊断分组及付费方式研究
Study on Diagnosis Grouping and Payment Method of Hepatitis B in TCM Based on DRG-PPS
刘三海 1杜倩 1黄强 2陈靓 1吴春明1
作者信息
- 1. 浙江中医药大学附属温州市中医院
- 2. 南昌大学第一附属医院
- 折叠
摘要
目的 分析中医证候诊断分组模型进行慢性乙型肝炎诊断分组的适用性,探讨DRG-PPS对住院费用的影响.方法 利用文献研究法、专家访谈设计基于中医证候的诊断相关分组模型;完成分组模型的评价、验证以及住院费用影响因素分析.结果 两步聚类法在慢性乙型肝炎ADRG分组基础上得到三个病例组,三个病例组的变异系数值均小于 1;总体变异减低系数值为 0.278,Kruskal-Wallis非参数检验p<0.01,三个病例组之间的差异具有统计学意义.K-Prototypes聚类分组存在无效分组,变异系数值较大,总体变异减低系数值较小;两步聚类法对于建立中医证候诊断相关分组模型具有可行性.结论 建立基于诊断相关分组的中医证候诊断分组模型,实行诊断相关组—预付费制度是有必要的.
Abstract
Objective To analyze the applicability of traditional Chinese medicine(TCM)syndrome diagnostic grouping model in the diagnosis of chronic hepatitis B,and to explore the influence of DRG-PPS on hospitalization costs.Methods Literature review and expert interviews were used to design a diagnosis-related grouping model based on TCM syndromes.The evaluation and validation of the grouping model and the analysis of the influencing factors of hospitalization costs were completed.Results Based on the ADRG grouping of chronic hepatitis B,three case groups were obtained by two-step clustering method,and the coefficient of variation values of the three case groups were all less than 1.The total variation reduction coefficient value was 0.278,with p<0.01 in Kruskal-Wallis non-parametric test,and the differences among the three case groups were statistically significant.The K-Prototypes clustering group had invalid groups,with large value of coefficient of variation,and small value of overall coefficient of variation reduction.Two-step clustering method was feasible for establishing the grouping model related to the diagnosis of TCM syndrome.Conclusion It is necessary to establish a diagnostic grouping model based on diagnostic-related grouping for TCM syndrome and to implement a diagnostic-related group-prospective payment system.
关键词
诊断相关分组/分组原则/按病种付费/慢性乙型肝炎Key words
Diagnosis related groups/Grouping principle/Pay by disease group/Chronic hepatitis B引用本文复制引用
基金项目
温州市科技局基础性科研项目(Y2020941)
出版年
2024
NETL
NSTL
万方数据