肺恶性肿瘤内科诊断组DRG分组方案研究  

Study on DRG Grouping Scheme of Internal Medicine Diagnosis Groups of Pulmonary Malignant Tumors

作　　者：张亚楠 刘玲 孙建勋[2] 

机构地区：[1]联勤保障部队第九八九医院,洛阳471000 [2]河南大学药学院,开封475000

出　　处：《中国医疗保险》2024年第9期79-84,共6页China Health Insurance

摘　　要：目的:分析肺恶性肿瘤内科诊断组住院费用的影响因素,设计DRG分组方案,为研究对象分组方案优化提供案例分析和参考。方法:收集洛阳市某三甲医院2019—2022年肺恶性肿瘤内科诊断组的患者住院信息,采用K-means聚类和支持向量机分析住院费用的影响因素,通过CHAID算法构建DRG分组方案。结果:治疗方式、住院天数被纳入分组模型,最终生成6个DRG组,各DRG组的组内一致性好,组间差异性显著,分组效果好。结论:对于肺恶性肿瘤内科诊断组,住院天数分组效果好,但不适合作为分组节点;治疗方式有助于完善研究对象的DRG分组,但其划分方案有待进一步研究。Objective:The paper analyzes the influencing factors of hospitalization expenses in the internal medicine diagnosis groups of pulmonary malignant tumors,designs DRG grouping scheme,and provides case studies and references for the optimization of grouping scheme.Methods:The hospitalization information of patients belonging to internal medicine diagnosis groups of pulmonary malignant tumors in a Class A hospital in Luoyang City from 2019 to 2022 was collected.K-means clustering and support vector machine was used to analyze the influencing factors of hospitalization expenses,and CHAID algorithm was used to construct DRG grouping scheme.Results:Treatment methods and length of hospital stay were included in the grouping model,and 6 DRG groups were finally generated.The consistency of each DRG with the group was good,and the difference between the groups was significant,and the grouping effect was good.Conclusions:For internal medicine diagnosis groups of pulmonary malignant tumor,the grouping effect of hospitalization days is good,but it is not suitable as a grouping node.The treatment method can help to improve the DRG grouping of the subjects,but the division scheme needs to be studied.

关 键 词：肺恶性肿瘤 内科诊断组 疾病诊断相关分组 聚类 支持向量机 决策树 

分 类 号：F840.684[经济管理—保险] C913.7[社会学]