Article(id=1324077812994421078, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324077716676424398, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202509003, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1756656000000, receivedDateStr=2025-09-01, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793614350793, onlineDateStr=2026-11-02, pubDate=1773072000000, pubDateStr=2026-03-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793614350793, onlineIssueDateStr=2026-11-02, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793614350793, creator=13701087609, updateTime=1793614350793, updator=13701087609, issue=Issue{id=1324077716676424398, tenantId=1146029695717560320, journalId=1227665162245664772, year='2026', volume='53', issue='5', pageStart='769', pageEnd='960', issueExtLink='null', onlineDate='null', pubDate='1773072000000', pubDateStr='2026-03-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1793614327830, creator='13701087609', updateTime=1793614418028, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1324078095132655884, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324077716676424398, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1324078095132655885, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324077716676424398, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=878, endPage=884, ext={EN=ArticleExt(id=1324077814810554713, articleId=1324077812994421078, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=DRGs grouping model of traditional Chinese medicine diseases and syndromes for dominant diseases in traditional Chinese medicine: a study of hemorrhoids, columnId=1228016567846367388, journalTitle=Modern Preventive Medicine, columnName=Health Policy and Management, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the diagnosis related group(DRG) grouping model of traditional Chinese medicine diseases and syndromes under the mode of traditional Chinese medicine disease and syndrome and Western medicine diagnosis, and provide references for relevant medical insurance departments to improve DRG of Zhejiang Province(ZJ-DRG) scheme and enhance its applicability in traditional Chinese medical institutions.

Methods

Discharged patients, from 2020 to 2023, in a tertiary TCM hospital of Zhejiang were selected, and their medical record homepage and the DRG grouping data were collected. Hemorrhoids(ICD-10 code: I84.0-I84.9) were selected as the target disease type for dominant diseases in traditional Chinese medicine. A multilayer perceptron (MLP) neural network and K-prototypes clustering algorithm were used to screen categorical node variables, and decision tree model was used to group hemorrhoids inpatients. The between-group heterogeneity and within-group homogeneity of the established model were evaluated through the coefficient of variation (CV) and the reduction in variance (RIV).

Results

The 4 663 hemorrhoids inpatients in this study were divided into 6 DRGs according to the ZJ-DRG scheme, and the heterogeneity between these groups (RIV: 1.92%) and the homogeneity within these groups (CV: 9.34%-26.29%) were moderate. Through the K-P clustering algorithm based on “Primary diagnosis-Primary symptom-Main Surgery or procedure”, 10 DRGs were formed, with the RIV increasing to 9.05% and the CV ranging between 17.14% and 22.99%. By further integrating traditional Chinese medicine specific indicators and complications, under the decision tree model (CRT algorithm), the RIV increased to 34.59%.

Conclusion

The constructed DRGs grouping model of traditional Chinese medicine diseases and syndromes has improved grouping efficiency and enhanced the heterogeneity between these groups. It can provide a reference for establishing a hierarchical and segmented disease group incentive mechanism for target dominant diseases in traditional Chinese Medicine.

, authors=Ling-ling PANG1, Ge ZHOU1, Jun-ming XIE2, Zhen-zhen SHAO3, Hua-qin SUN2, Ke DING2, Li WU4, authorsList=Ling-ling PANG, Ge ZHOU, Jun-ming XIE, Zhen-zhen SHAO, Hua-qin SUN, Ke DING, Li WU, authorCompany=null, correspAuthors=Ge ZHOU, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1324077816198869346, articleId=1324077812994421078, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=中医优势病种的中医病证DRGs分组模型研究——以痔病为例, columnId=1228016568014139551, journalTitle=现代预防医学, columnName=卫生政策与管理, runingTitle=null, highlight=null, articleAbstract=
目的

探究“中医病证+西医诊断”模式下的中医病证诊断相关分组(diagnosis related groups,DRGs)分组模型,为相关部门优化浙江省疾病诊断相关分组(diagnosis related group of zhejiang, ZJ-DRG)方案,提高其在中医医疗机构适用性提供参考。

方法

收集2020—2023年浙江省某综合性三甲中医医院出院患者的病案首页数据和DRG分组数据,选取痔病(ICD-10编码:I84.0 ~I84.9)为目标中医药治疗优势病种。采用多层感知机(multilayer perceptron, MLP)神经网络模型和K-Prototypes聚类算法筛选分类节点,并利用决策树模型进行痔病病例分组,通过变异系数(coefficient of variation, CV)和方差减少量(reduction in variance, RIV)对所建立模型进行组内同质性和组间异质性评价。

结果

本研究纳入的4 663名痔病患者按ZJ-DRG 分组,研究对象被分为6个DRGs 组,组间异质性(RIV:1.92%)和组内同质性(CV:9.34%~26.29%)均一般。在基于“主诊断_主证_主要手术操作”的 K-P聚类算法分组下,共形成10个DRGs 组,RIV值提升至9.05%,CV值在17.14%~22.99%间不等。进一步融合中医特色指标及并发症,决策树模型(CRT 算法)下,RIV 提升至34.59%。

结论

所构建的中医病证DRGs分组模型改善了分组效能,增强组间异质性,可为建立中医优势病种的分层、分区段病组激励机制提供参考。

, authors=庞玲玲1, 周歌1, 谢俊明2, 邵珍珍3, 孙华琴2, 丁科2, 吴丽4, authorsList=庞玲玲, 周歌, 谢俊明, 邵珍珍, 孙华琴, 丁科, 吴丽, authorCompany=null, correspAuthors=周歌, authorNote=

庞玲玲(1994—),女,硕士,住院医师,研究方向:医疗保障、DRG 政策

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周歌,E-mail:
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The study on payment method of inpatient medical insurance for Dominant diseases of traditional Chinese Medicine[D]. Guangzhou:Guangzhou University of Chinese Medicine, 2023. 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(In Chinese), articleTitle=Exploration and practice of Disproportionate-Share hospital payment mechanism of traditional Chinese medicine un-der the reform of medical insurance payment system, refAbstract=null)], funds=[Fund(id=1324077822033146273, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077812994421078, awardId=2024ZF080, language=CN, fundingSource=浙江省中医药科技计划项目(2024ZF080), fundOrder=null, country=null), Fund(id=1324077822096060834, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077812994421078, awardId=2024ZWJXH002, language=CN, fundingSource=浙江省卫生经济学会项目(2024ZWJXH002), fundOrder=null, country=null), Fund(id=1324077822163169699, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077812994421078, awardId=2024ZF068, language=CN, fundingSource=2024年度浙江省中医药科技计划项目(2024ZF068), fundOrder=null, country=null), Fund(id=1324077822255444388, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077812994421078, awardId=2023ZF095, language=CN, fundingSource=浙江省中医药科技计划项目(2023ZF095), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1324077816437944675, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077812994421078, xref=1., ext=[AuthorCompanyExt(id=1324077816450527588, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077812994421078, companyId=1324077816437944675, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Department of Medical Record Management, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, Zhejiang 310006, China), AuthorCompanyExt(id=1324077816458916197, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077812994421078, companyId=1324077816437944675, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.浙江中医药大学附属第一医院(浙江省中医院)病案管理科,浙江 杭州 310006)]), AuthorCompany(id=1324077816546996582, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077812994421078, xref=2., ext=[AuthorCompanyExt(id=1324077816555385191, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077812994421078, companyId=1324077816546996582, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.浙江中医药大学附属第一医院(浙江省中医院)医保物价办公室)]), AuthorCompany(id=1324077816630882664, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077812994421078, xref=3., ext=[AuthorCompanyExt(id=1324077816639271273, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077812994421078, companyId=1324077816630882664, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3.浙江中医药大学附属第一医院(浙江省中医院)中医药发展部)]), AuthorCompany(id=1324077816727351658, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077812994421078, xref=4., ext=[AuthorCompanyExt(id=1324077816735740267, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077812994421078, companyId=1324077816727351658, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=4.浙江中医药大学附属第一医院(浙江省中医院)科研部)])], figs=[ArticleFig(id=1324077821005541785, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077812994421078, language=EN, label=Figure 1, caption=Decision tree model for case-mix of hemorrhoids, figureFileSmall=rIhpTLYy7PYLf1Hgdke50g==, figureFileBig=d9wYTCar1NY1QRpa799BrA==, tableContent=null), ArticleFig(id=1324077821076844954, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077812994421078, language=CN, label=图1, caption=痔病病例组合的决策树模型, figureFileSmall=rIhpTLYy7PYLf1Hgdke50g==, figureFileBig=d9wYTCar1NY1QRpa799BrA==, tableContent=null), ArticleFig(id=1324077821294948763, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077812994421078, language=EN, label=Table 1, caption=

Grouping effect of ZJ-DRG grouper in hemorrhoid patients admitted to traditional Chinese medicine hospitals [n(%),M(P25,P75)]

, figureFileSmall=null, figureFileBig=null, tableContent=
分组编码分组名称中医组西医组合计
例数住院总费用(元)CV#(%)例数住院总费用(元)CV#(%)例数住院总费用(元)CV#(%)
GF11肛管、肛门及肛周手术,伴严重并发症与合并症47
(1.3)
10 102.44
(6 835.84, 12 644.08)
26.739
(0.8)
12 126.35
(11 317.6, 14 473.32)
22.1256
(1.2)
11 055.04
(7 297.60, 12 809.25)
26.29
GF13肛管、肛门及肛周手术,伴一般并发症与合并症1 716
(48.5)
8 822.25
(7 558.98, 10 370.7)
20.91338
(30)
8 679.67
(7 054.18, 10 405.71)
22.502 054
(44)
8 812.74
(7 449.45, 10 372.55)
21.16
GF15肛管、肛门及肛周手术,不伴并发症与合并症1 681
(47.5)
8 385.98
(7 167.04, 9 610.58)
19.68597
(53)
8 249.14
(6 422.68, 9 538.67)
19.872 278
(48.9)
8 348.82
(6 969.45, 9 593.49)
19.73
GZ11其他消化系统诊断,伴严重并发症与合并症3
(0.1)
——3
(0.3)
—12.606
(0.1)
5 276.81
(3 159.75, 7 701.08)
9.34
GZ13其他消化系统诊断,伴一般并发症与合并症37
(1.0)
2 934.24
(1 778.00, 4 497.79)
21.2757
(5.1)
3 053.61
(1 915.32, 4 740.68)
15.3794
(2.0)
3 038.75
(1 785.42, 4 689.66)
18.20
GZ15其他消化系统诊断,不伴并发症与合并症52
(1.5)
2 336.16
(1 108.02, 3 365.90)
19.24123
(10.9)
2 655.32
(888.27, 3 232.12)
8.27175
(3.8)
2 570.01
(899.28, 3 252.74)
24.84
), ArticleFig(id=1324077821416583580, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077812994421078, language=CN, label=表1, caption=

ZJ-DRG分组器在中医医院收治痔病中的分组效果[n(%),M(P25,P75)]

, figureFileSmall=null, figureFileBig=null, tableContent=
分组编码分组名称中医组西医组合计
例数住院总费用(元)CV#(%)例数住院总费用(元)CV#(%)例数住院总费用(元)CV#(%)
GF11肛管、肛门及肛周手术,伴严重并发症与合并症47
(1.3)
10 102.44
(6 835.84, 12 644.08)
26.739
(0.8)
12 126.35
(11 317.6, 14 473.32)
22.1256
(1.2)
11 055.04
(7 297.60, 12 809.25)
26.29
GF13肛管、肛门及肛周手术,伴一般并发症与合并症1 716
(48.5)
8 822.25
(7 558.98, 10 370.7)
20.91338
(30)
8 679.67
(7 054.18, 10 405.71)
22.502 054
(44)
8 812.74
(7 449.45, 10 372.55)
21.16
GF15肛管、肛门及肛周手术,不伴并发症与合并症1 681
(47.5)
8 385.98
(7 167.04, 9 610.58)
19.68597
(53)
8 249.14
(6 422.68, 9 538.67)
19.872 278
(48.9)
8 348.82
(6 969.45, 9 593.49)
19.73
GZ11其他消化系统诊断,伴严重并发症与合并症3
(0.1)
——3
(0.3)
—12.606
(0.1)
5 276.81
(3 159.75, 7 701.08)
9.34
GZ13其他消化系统诊断,伴一般并发症与合并症37
(1.0)
2 934.24
(1 778.00, 4 497.79)
21.2757
(5.1)
3 053.61
(1 915.32, 4 740.68)
15.3794
(2.0)
3 038.75
(1 785.42, 4 689.66)
18.20
GZ15其他消化系统诊断,不伴并发症与合并症52
(1.5)
2 336.16
(1 108.02, 3 365.90)
19.24123
(10.9)
2 655.32
(888.27, 3 232.12)
8.27175
(3.8)
2 570.01
(899.28, 3 252.74)
24.84
), ArticleFig(id=1324077821617910173, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077812994421078, language=EN, label=Table 2, caption=

Evaluation of K-P clustering algorithm grouping for hemorrhoids based on “Primary diagnosis-Primary symptom-Main Surgery or procedure”

, figureFileSmall=null, figureFileBig=null, tableContent=
分组例数住院总费用CV#RIV#
sM
03355 189.283 715.374 401.3719.199.05
16319 956.712 070.969 509.3017.14
21848 793.372 920.098 881.1620.74
37278 275.8210 529.938 093.5822.93
47248 987.261 803.248 767.5417.44
526310 057.122 873.9710 195.7520.73
65118 098.512 828.897 936.0722.43
71157 637.182 659.077 333.8322.99
81 1397 517.402 651.087 823.0719.50
9347 523.122 280.327 618.9617.45
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痔病“主诊断_主证_手术操作”K-P聚类算法分组评价

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分组例数住院总费用CV#RIV#
sM
03355 189.283 715.374 401.3719.199.05
16319 956.712 070.969 509.3017.14
21848 793.372 920.098 881.1620.74
37278 275.8210 529.938 093.5822.93
47248 987.261 803.248 767.5417.44
526310 057.122 873.9710 195.7520.73
65118 098.512 828.897 936.0722.43
71157 637.182 659.077 333.8322.99
81 1397 517.402 651.087 823.0719.50
9347 523.122 280.327 618.9617.45
), ArticleFig(id=1324077821836013983, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077812994421078, language=EN, label=Table 3, caption=

Case-mix grouping results of hemorrhoids (DRG)

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分组编码K-P分组住院天数分组治疗方式中治率并发症例数(%)住院总费用(元)CV#(%)
sM
DRG10.0;4.0;8.0;7.0;9.04d———524
(11.24)
8 460.561 514.488 429.815.17
DRG23.1;1.0;2.0;6.0;5.04d—≤12.37—451
(9.67)
9 763.931 693.849 553.7115.75
DRG33.1;1.0;2.0;6.0;5.04d—>12.37—93
(1.99)
8 654.31 597.658 908.7115.08
DRG41.0≤3d西医手术;中医肛肠技术;中医肛肠技术+西医手术——222(4.76)8 923.931 238.398 925.13513.88
DRG54.0≤3d西医手术;中医肛肠技术;中医肛肠技术+西医手术——238(5.1)7 766.241 238.947 793.6613.68
DRG60.0;3.0;2.0;8.0;7.0;6.0;5.0;9.0≤3d西医手术;中医肛肠技术——695
(14.9)
5 336.222 185.265 371.2916.90
DRG70.0;3.0;2.0;8.0;7.0;6.0;5.0;9.0≤3d中医肛肠技术+西医手术——969
(20.78)
7 093.482 199.177 493.0717.32
DRG8—≤3d保守治疗—无77
(1.65)
1 340.421 279.17899.28—
DRG9—≤3d保守治疗—一般;严重56
(1.2)
2 805.231 812.272 650.8511.56
DRG10—≥5d———1338
(28.69)
10 708.797 751.3610 129.5217.83
), ArticleFig(id=1324077821919900064, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324077812994421078, language=CN, label=表3, caption=

痔病的病例组合分组情况(DRG)

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分组编码K-P分组住院天数分组治疗方式中治率并发症例数(%)住院总费用(元)CV#(%)
sM
DRG10.0;4.0;8.0;7.0;9.04d———524
(11.24)
8 460.561 514.488 429.815.17
DRG23.1;1.0;2.0;6.0;5.04d—≤12.37—451
(9.67)
9 763.931 693.849 553.7115.75
DRG33.1;1.0;2.0;6.0;5.04d—>12.37—93
(1.99)
8 654.31 597.658 908.7115.08
DRG41.0≤3d西医手术;中医肛肠技术;中医肛肠技术+西医手术——222(4.76)8 923.931 238.398 925.13513.88
DRG54.0≤3d西医手术;中医肛肠技术;中医肛肠技术+西医手术——238(5.1)7 766.241 238.947 793.6613.68
DRG60.0;3.0;2.0;8.0;7.0;6.0;5.0;9.0≤3d西医手术;中医肛肠技术——695
(14.9)
5 336.222 185.265 371.2916.90
DRG70.0;3.0;2.0;8.0;7.0;6.0;5.0;9.0≤3d中医肛肠技术+西医手术——969
(20.78)
7 093.482 199.177 493.0717.32
DRG8—≤3d保守治疗—无77
(1.65)
1 340.421 279.17899.28—
DRG9—≤3d保守治疗—一般;严重56
(1.2)
2 805.231 812.272 650.8511.56
DRG10—≥5d———1338
(28.69)
10 708.797 751.3610 129.5217.83
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中医优势病种的中医病证DRGs分组模型研究——以痔病为例
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庞玲玲 1 , 周歌 1 , 谢俊明 2 , 邵珍珍 3 , 孙华琴 2 , 丁科 2 , 吴丽 4
现代预防医学 | 卫生政策与管理 2026,53(5): 878-884
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现代预防医学 |卫生政策与管理 2026 , 53 (5) : 878 -884
中医优势病种的中医病证DRGs分组模型研究——以痔病为例
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庞玲玲1, 周歌1 , 谢俊明2, 邵珍珍3, 孙华琴2, 丁科2, 吴丽4
作者信息
  • 1.浙江中医药大学附属第一医院(浙江省中医院)病案管理科,浙江 杭州 310006
  • 2.浙江中医药大学附属第一医院(浙江省中医院)医保物价办公室
  • 3.浙江中医药大学附属第一医院(浙江省中医院)中医药发展部
  • 4.浙江中医药大学附属第一医院(浙江省中医院)科研部
通讯作者:
周歌,E-mail:
作者简介:

庞玲玲(1994—),女,硕士,住院医师,研究方向:医疗保障、DRG 政策

DRGs grouping model of traditional Chinese medicine diseases and syndromes for dominant diseases in traditional Chinese medicine: a study of hemorrhoids
Ling-ling PANG1, Ge ZHOU1 , Jun-ming XIE2, Zhen-zhen SHAO3, Hua-qin SUN2, Ke DING2, Li WU4
Affiliations
  • Department of Medical Record Management, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, Zhejiang 310006, China
出版时间: 2026-03-10 doi: 10.20043/j.cnki.MPM.202509003
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目的

探究“中医病证+西医诊断”模式下的中医病证诊断相关分组(diagnosis related groups,DRGs)分组模型,为相关部门优化浙江省疾病诊断相关分组(diagnosis related group of zhejiang, ZJ-DRG)方案,提高其在中医医疗机构适用性提供参考。

方法

收集2020—2023年浙江省某综合性三甲中医医院出院患者的病案首页数据和DRG分组数据,选取痔病(ICD-10编码:I84.0 ~I84.9)为目标中医药治疗优势病种。采用多层感知机(multilayer perceptron, MLP)神经网络模型和K-Prototypes聚类算法筛选分类节点,并利用决策树模型进行痔病病例分组,通过变异系数(coefficient of variation, CV)和方差减少量(reduction in variance, RIV)对所建立模型进行组内同质性和组间异质性评价。

结果

本研究纳入的4 663名痔病患者按ZJ-DRG 分组,研究对象被分为6个DRGs 组,组间异质性(RIV:1.92%)和组内同质性(CV:9.34%~26.29%)均一般。在基于“主诊断_主证_主要手术操作”的 K-P聚类算法分组下,共形成10个DRGs 组,RIV值提升至9.05%,CV值在17.14%~22.99%间不等。进一步融合中医特色指标及并发症,决策树模型(CRT 算法)下,RIV 提升至34.59%。

结论

所构建的中医病证DRGs分组模型改善了分组效能,增强组间异质性,可为建立中医优势病种的分层、分区段病组激励机制提供参考。

痔病  /  DRGs模型  /  中医病证  /  神经网络  /  决策树
Objective

To explore the diagnosis related group(DRG) grouping model of traditional Chinese medicine diseases and syndromes under the mode of traditional Chinese medicine disease and syndrome and Western medicine diagnosis, and provide references for relevant medical insurance departments to improve DRG of Zhejiang Province(ZJ-DRG) scheme and enhance its applicability in traditional Chinese medical institutions.

Methods

Discharged patients, from 2020 to 2023, in a tertiary TCM hospital of Zhejiang were selected, and their medical record homepage and the DRG grouping data were collected. Hemorrhoids(ICD-10 code: I84.0-I84.9) were selected as the target disease type for dominant diseases in traditional Chinese medicine. A multilayer perceptron (MLP) neural network and K-prototypes clustering algorithm were used to screen categorical node variables, and decision tree model was used to group hemorrhoids inpatients. The between-group heterogeneity and within-group homogeneity of the established model were evaluated through the coefficient of variation (CV) and the reduction in variance (RIV).

Results

The 4 663 hemorrhoids inpatients in this study were divided into 6 DRGs according to the ZJ-DRG scheme, and the heterogeneity between these groups (RIV: 1.92%) and the homogeneity within these groups (CV: 9.34%-26.29%) were moderate. Through the K-P clustering algorithm based on “Primary diagnosis-Primary symptom-Main Surgery or procedure”, 10 DRGs were formed, with the RIV increasing to 9.05% and the CV ranging between 17.14% and 22.99%. By further integrating traditional Chinese medicine specific indicators and complications, under the decision tree model (CRT algorithm), the RIV increased to 34.59%.

Conclusion

The constructed DRGs grouping model of traditional Chinese medicine diseases and syndromes has improved grouping efficiency and enhanced the heterogeneity between these groups. It can provide a reference for establishing a hierarchical and segmented disease group incentive mechanism for target dominant diseases in traditional Chinese Medicine.

Hemorrhoids  /  DRGs model  /  Chinese medicine disease and syndrome  /  Neural network  /  Decision tree model
庞玲玲, 周歌, 谢俊明, 邵珍珍, 孙华琴, 丁科, 吴丽. 中医优势病种的中医病证DRGs分组模型研究——以痔病为例. 现代预防医学, 2026 , 53 (5) : 878 -884 . DOI: 10.20043/j.cnki.MPM.202509003
Ling-ling PANG, Ge ZHOU, Jun-ming XIE, Zhen-zhen SHAO, Hua-qin SUN, Ke DING, Li WU. DRGs grouping model of traditional Chinese medicine diseases and syndromes for dominant diseases in traditional Chinese medicine: a study of hemorrhoids[J]. Modern Preventive Medicine, 2026 , 53 (5) : 878 -884 . DOI: 10.20043/j.cnki.MPM.202509003
中医优势病种,是指中医诊疗具有显著疗效优势并能充分体现其辨证论治特色的疾病种类[1]。2019年,国家中医药主管部门正式遴选出95个病种纳入中医优势病种目录,并配套制定了相应的诊疗方案及临床路径[2]。中医临床路径作为标准化诊疗流程,在痔病、肛裂等纳入目录的中医优势病种的治疗中展现出显著的应用价值[3]。然而,疾病诊断相关分组(diagnosis related group, DRG)支付模式作为我国医保控费改革的重要举措,其分组规则设计与本土化应用缺乏中医诊疗的理论支撑[4],难以充分体现中医药治疗的价值。目前,针对中医病证特点的DRG模型研究尚不充分,而将理论模型构建延伸至医保支付落地的实践探索更是极为匮乏。因此,亟需构建能够充分体现中医诊疗特色的DRG模型。
本研究选取痔病为目标中医药治疗优势病种,对包含中医特色因素在内的模型因素进行重要性排序筛选,在“中医病证+西医诊断”的模式下[5],构建了具有中医分组逻辑的中医病证DRGs模型,并与浙江省疾病诊断相关分组(diagnosis related group of zhejiang, ZJ - DRG)的分组效果进行对比,为政府及医保相关部门完善优化DRGs分组方案、提高其在中医医疗机构适用性提供参考。
来源于浙江省某综合性三甲中医医院住院病案首页和医保平台反馈数据。选取痔病(ICD-10编码:I84.0 ~I84.9)作为目标中医药治疗优势病种,收集样本医院2020—2023年所有痔病出院患者的中医住院病案首页数据(基本信息、临床路径类型及入组情况、临床诊疗信息和住院费用等)、DRG分组数据。纳入标准:(1)医保反馈入组成功,且通过校验规则审核,或依据校验规则修改病案数据后通过审核者;(2)剔除单次住院天数60 d及以上者。排除标准:参与分析变量不完整及存在异常数据者。共纳入有效病例4 663例。本研究中治率公式=(中药饮片费用+中成药费用+中医非药物疗法费用)/住院总费用。
参考既往相关研究[6],按照病案首页中出院患者是否实施中、西医临床路径,将其分为中医组与西医组。中、西医临床路径的实施,均依据国家卫生行政部门发布的中医临床路径与西医临床路径相关通知[7-8]中所规定的标准表单内容执行。
采用Excel 2016对目标病种的相关数据进行筛选、整理和建立数据库。定量数据采用() 或[M(P25,P75)]表示;定性数据采用率或构成比表示。首先,运用Python 3.x进行主诊断_主证_主要手术操作病例组合的K-Prototypes(K-P)聚类算法模型构建。其次,运用IBM SPSS 24.0构建多层感知机(multilayer perceptron, MLP)神经网络模型,筛选构建DRGs分组模型因素;进一步采用分类回归树(classification and regression tree, CRT)算法进行交叉验证构建决策树模型。因住院总费用不服从正态分布,分组效果评价时,其原始值的组间比较采用Kruskal -Wallis H检验;影响因素分析和模型构建时,分别对住院总费用和住院天数进行正态得分转换,转换后经正态性检验近似正态分布。检验水准 α=0.05。
DRG分组效果通常从组内同质性和组间异质性两方面评价。变异系数(coefficient of variation, CV)用来评价组内同质性,计算公式为:CV= 标准差 / 均数。方差减少量(reduction in variance,RIV)RIV用来评价组间异质性,计算公式为:RIV =(总离均差平方和-n 个子集的总离均差平方和的总计)/ 总离均差平方和。因住院总费用不服从正态分布,本研究采用中间区间法[9-11](interquartile range, IQR)计算 CV 值和RIV值。对于分组模型,CV<80.0%,说明组内同质性较好,80%≤CV<100.0%,达可接受标准;RIV≥70.0%,说明组间异质性较好,40.0%<RIV<70.0%,达可接受标准[12]。
本研究中痔病患者共4 663例,其中男性2 149例(46.1%),女性2 514例(53.9%),年龄在3~89(39.11±14.62)岁,均次住院天数[M(P25,P75)]为4(2, 5) d,均次住院总费用[M(P25,P75)]为8 388.73(6 803.44,9 870.18)元。
根据医保平台反馈数据,4 663名痔病患者被分为6组,不同DRGs组间的住院总费用差异均具有统计学意义(H值=709.887,P<0.001),合计CV值在9.34%~26.29%间不等,其中中医组的CV值在19.24%~26.73%之间,西医组的CV值在8.27%~22.50%之间,均<80.0%,表明各组组内同质性均较好,合计RIV为1.92%,组间异质性一般。见表1。
本研究选取痔病为典型病种,尝试进行“主诊断_主证_主要手术操作”的中西医病证分组模型研究。参考南京中医DRG分组方案[13-14],利用西医诊断名称、中医证候名称及主要手术操作名称对痔病进K-P聚类,构建得到10个病组。病组0包含64个中医病证组合编码,335份病例(7.18%);病组1包含12个中医病证组合编码,631份病例(13.53%);病组2包含4个中医病证组合编码,184份病例(3.95%);病组3包含48个中医病证组合编码,727份病例(15.59%);病组4包含15个中医病证组合编码,724份病例(15.53%);病组5包含1个中医病证组合编码,263份病例(5.64%);病组6包含19个中医病证组合编码,511份病例(10.96%);病组7包含10个中医病证组合编码,115份病例(2.47%);病组8包含6个中医病证组合编码,1 139份病例(24.43%);病组9包含2个中医病证组合编码,34份病例(0.73%)。
评价K-P聚类算法的分组情况,不同分组间的住院总费用差异均具有统计学意义(χ2=743.977,P<0.001),RIV为9.05%,CV值在17.14%~22.99%不等。与未纳入中医证候编码的ZJ-DRG分组(RIV=1.92%;最大CV=26.29%)相比,最大CV值有一定程度降低,RIV值有明显升高,表明加入中医元素后,分组效果得到一定程度改善,细分组之间差异性增大。见表2。
上述聚类结果组间异质性虽较ZJ-DRG有所提高,但其RIV值仍远<40%,尚有改善空间,故本研究考虑以住院总费用为划分依据,在“主诊断_主证_主要手术操作”聚类基础上,尝试进一步引入中医特色指标、治疗方式、住院天数和并发症因素优化中医病证分组模型。以正态得分转换后的住院总费用为因变量,对性别、年龄、住院天数分组、医疗付款方式(城镇职工=1,城乡居民=2,新农合=3,自费/其他=4)、入院途径、中治率、主诊断_主证_主要手术操作、治疗方式、临床路径和并发症类型进行单因素和多重线性回归分析。结果显示,年龄、住院天数分组、中治率、主诊断_主证_主要手术操作、治疗方式、临床路径和并发症类型是住院总费用的独立影响因素(F=297.596,P<0.001)。
选取差异有统计学意义的5个项目(主诊断_主证_主要手术操作、住院天数分组、年龄、中治率、治疗方式、临床路径和并发症类型)为输入变量,以痔病患者住院总费用为输出变量,构建神经网络模型。结果显示,影响痔病患者住院总费用的重要性排序因素依次为中治率(100.0%)、治疗方式(79.2%)、住院天数分组(51.4%)、主诊断_主证_主要手术操作(50.7%)、并发症类型(39.1%)、年龄(18.2%)和临床路径(3.8%)。
参考现行的痔病ZJ-DRG分组因素,选取标准化重要性20.0%以上的主诊断_主证_主要手术操作、住院天数分组、治疗方式、中治率和并发症类型为分类节点,以正态得分转化后住院总费用为因变量,采用CRT算法构建决策树模型,见图1。4 663名痔病患者共获得10个DRGs组,见表3。经Kruskal-Wallis H检验,不同DRGs组间的住院总费用差异均具有统计学意义(χ2=2 378.269,P<0.001)。进一步计算RIV和CV值,CV值在11.56%~17.83%之间,均<80.0%,RIV为34.59%,与未纳入中医特色因素和治疗方式的ZJ-DRG(RIV=1.92%;最大CV=26.29%)相比,RIV值有明显升高、最大CV值明显降低,表明组内同质性和组间异质性均有显著提高;与“主诊断_主证_主要手术操作”K-P聚类算法分组结果(RIV= 9.05%;最大CV=22.99%)相比,分组效果亦有明显改善。
完整的中医诊断需包含中医疾病和中医证型两个部分。与西医病名相比,中医病名存在总量偏少、标准化程度不足的局限,且普遍存在“一病多名、多病同名”的现象,很难形成像西医一样精确的分组[15]。现行DRG分组框架完全依据西医诊疗路径构建,致使中医信息被系统性忽略。中医病证诊断信息的缺失,不仅会使病例入组结果偏离实际,而且会掩盖中医药“简、验、便、廉”的特色优势,难以体现中医医疗服务的价值。现有分组框架与中医注重人力经验与技术价值、弱化设备耗材投入的特性存在冲突,客观上制约了传统医学优势的发挥[16]。
因此,本研究将“证候”作为中医病证DRGs分组模型的核心分类依据,协同参照西医病名,形成“中医病证+西医诊断”结合诊断的双重诊断模式的分组基础。从结果中可以看到,与未纳入中医证候编码的ZJ-DRG分组相比,加入中医元素后的分组效果得到一定程度改善,细分组之间差异性增大(RIV=9.05%;最大CV=22.99%)。这并非诊断的简单叠加,而是希望能够构建一个能够在中西医双重维度下评估资源消耗的模型。西医诊断奠定科学基础与普遍可比性,中医病证则体现了个体状态与治疗方向,为后续融入中医特色指标提供结构化框架。
在临床中,接受中药制剂及中医诊疗技术的患者,其住院费用往往较高。这种治疗选择对住院费用存在直接及间接的双重影响。然而,以西医疾病分类为核心的DRGs分组、支付及评价体系,无法有效纳入核心中医信息,导致中医药的应用价值无法得到合理量化与偿付[17]。为突破这一瓶颈,全国多地均积极开展本土化探索尝试[18]。其中,浙江省医保局提出的“中治率”指标作为一个正向激励机制,成为具有代表性的创新实践。中治率是指中医机构住院收入里中药饮片、中医服务项目与中成药三项收入之和的占比。石连忠等[19]认为以“中治率”为特色的杭州改革模式,不仅降低了整体例均费用,减轻患者负担并节约医保基金,更带动了医院运用中医诊疗思维为疑难急危患者提供特色服务的积极性。
因此,本研究将重要的中医药指标,如中治率加入到分组模型中,进而细分组具有中医特点的DRGs,以对本研究的病证诊断相关分组模型进行优化。可以看到,这一优化与“主诊断_主证_主要手术操作”K-P聚类算法分组结果相比,进一步提高了组间异质性(RIV=34.59%;最大CV=17.83%)。它不仅保留了原始分组原则,也融入中医特色指标,能更好解释资源消耗变异、更贴合临床实际路径[20]。
综上所述,在统一框架下公平评价中西医的贡献,可为制定科学的支付政策提供重要依据。本研究所构建的模型,能更真实地反映中医特色治疗的实际成本,有效规避因关键指标缺失导致的问题。后续研究将着力于力于中医特色指标体系的持续优化与模型效能的实证检验,期望形成以中医优势病种的分层、分区段病组激励机制。然而,模型的建立与落地应用仍面临显著挑战。中医证候、中治率等核心指标的规范化采集和数据质量,是当前的主要瓶颈[21]。大量引入特色指标虽能提升模型精度,但可能显著增加其复杂性,因此需在模型的精细程度与可操作性之间寻求最优平衡点,也可融入非对称共摊理念等有益尝试[22],最终取得多方共赢改革效果。
  • 浙江省中医药科技计划项目(2024ZF080)
  • 浙江省卫生经济学会项目(2024ZWJXH002)
  • 2024年度浙江省中医药科技计划项目(2024ZF068)
  • 浙江省中医药科技计划项目(2023ZF095)
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2026年第53卷第5期
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doi: 10.20043/j.cnki.MPM.202509003
  • 接收时间:2025-09-01
  • 首发时间:2026-11-02
  • 出版时间:2026-03-10
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  • 收稿日期:2025-09-01
基金
浙江省中医药科技计划项目(2024ZF080)
浙江省卫生经济学会项目(2024ZWJXH002)
2024年度浙江省中医药科技计划项目(2024ZF068)
浙江省中医药科技计划项目(2023ZF095)
作者信息
    1.浙江中医药大学附属第一医院(浙江省中医院)病案管理科,浙江 杭州 310006
    2.浙江中医药大学附属第一医院(浙江省中医院)医保物价办公室
    3.浙江中医药大学附属第一医院(浙江省中医院)中医药发展部
    4.浙江中医药大学附属第一医院(浙江省中医院)科研部

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多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
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