目的: 基于肺恶性肿瘤内科诊断组探讨不同病例复杂度衡量模型的异同,为进一步优化DRG分组提供参考。方法: 以河南洛阳某三甲医院2019—2022年2131例肺恶性肿瘤内科诊断组为研究对象,分别采用MCC/CC表、PCCL模型和ECC模型计算出研究对象的病例复杂度,通过决策树构建分组,并对分组效果进行对比分析。结果: 肺恶性肿瘤内科诊断组按MCC/CC表分为3组,CV值分别为0.51、0.57和0.62,RIV值为0.02;按PCCL模型分为5组,CV值分别为0.57、0.57、0.50、0.76和0.70,RIV值为0.14;按ECC模型分为3组,CV值分别为0.61、0.63和0.49,RIV值为0.03。根据非参数检验结果,各基本组的组间住院费用差异有统计学意义。结论: 基于分组性能,MCC/CC表和ECC模型优于PCCL模型,不同ADRG下MCC/CC表和ECC模型分组效果有待进一步对比研究。

Objective: The paper explores the similarities and differences of different case complexity measurement models based on internal medicine diagnosis groups of pulmonary malignant tumor to provide a reference for further optimizing DRG grouping. Methods: Taking 2131 cases of pulmonary malignant tumor internal medicine diagnosis groups from a tertiary hospital in Luoyang City of Henan Province from 2019 to 2022 as the research object, the paper adopts the MCC/CC tables, PCCL model and ECC model to calculate the case complexity of the research object, constructs groups through decision trees, and compares and analyzes the effectiveness of the groups. Results: According to the MCC/CC table, the internal medicine diagnosis groups of pulmonary malignant tumor was divided into 3 groups, the CV was 0.51, 0.57 and 0.62, and the RIV was 0.02. According to the PCCL model, they were divided into 5 groups, the CV was 0.57, 0.57, 0.50, 0.76 and 0.70, and the RIV was 0.14. According to the ECC model, they were divided into 3 groups, the CV was 0.61, 0.63 and 0.49, and the RIV was 0.03. According to the results of non-parametric test, there were significant differences in hospitalization expenses among the basic groups. Conclusion: Based on the grouping performance, the MCC/CC table and ECC model are better than the PCCL model, and the grouping effect of the MCC/CC table and ECC model under different ADRG needs to be further compared and studied.