DRG眼底手术治疗组分组方法研究
DRG Subgroup Study of Fundus Surgery Treatment
朱春龙 1赵英英 1侯冷晨 2朱鸿 1许冠吾 1沈怡1
作者信息
- 1. 上海市第一人民医院 上海 200080
- 2. 上海市申康医院发展中心 上海 200041
- 折叠
摘要
目的:借鉴澳大利亚病例临床复杂模型(ECC)对本市眼底手术治疗组(CB1)进行DRG分组优化研究,并拟提分组改进方案.方法:调研收集上海市21 家医院CB1 组病例数据;使用MATLAB软件构建组内病例费用关系函数,并计算治疗复杂程度值(TCL)和病例临床复杂程度值(ECCS);使用SPSS 24.0对量化的ECCS进行决策树分类;结果采用组内差异值(CV)、组间差异(RIV)、秩和检验(K-W)进行评价和检验.结果:CB1 基本组病例被划分成轻度复杂(CV 0.65)、中度复杂(CV 0.39)、重度复杂(CV 0.30),细分组RIV= 0.61,P＜0.01,符合独立分组规范要求.结论:在CB1 组的基础上,应用手术及操作为自变量的ECC模型能进一步体现治疗复杂程度,提升分组性能,更好地满足医保付费和绩效考核的需求.基于研究结果拟提的分组方案已被市医保局采纳,为地方动态调整DRG分组提供决策依据.
Abstract
Objective:To optimize the grouping of the fundus surgery treatment group(CB1)in Shanghai by drawing on the Australian episode clinical complexity(ECC)model,and to propose a grouping improvement plan.Methods:CB1 data were collected from 21 hospitals in Shanghai from survey;case cost relationship functions were constructed using MATLAB,and Treatment Complexity Level(TCL)&Episode Clinical Complexity Score(ECCS)were computed;the quantified ECCS were classified into a decision tree using SPSS 24.0;the results were evaluated and examined using the Coefficient of Variation(CV),Reductio in Variance(RIV),and Kruskal-Wallis test.Results:CB1 were categorized as mildly complex(CV 0.65),moderately complex(CV 0.39),and severely complex(CV 0.30),RIV= 0.61,P＜0.01,which was in line with the requirement of the norms of independent grouping.Conclusion:On the basis of CB1 group,applying the ECC model with operation as independent variables can further reflect episode complexity level,improve the grouping performance,and better meet the needs of health insurance payment and performance evaluation.The proposed grouping scheme based on the results of the study has been adopted by the Municipal Health Insurance Bureau,providing a decision-making basis for local dynamic adjustment of DRGs grouping.
关键词
DRG/病例临床复杂/眼底手术/MATLABKey words
Diagnosis Related Group/episode clinical complexity/fundus surgery/MATLAB引用本文复制引用
基金项目
市级医院诊疗技术推广及优化管理项目(SHDC12022622)
&&(YNGL-2023-29)
上海市医院协会医院管理研究基金(X2022180)
出版年
2024
NETL
NSTL
万方数据