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Article(id=1241759336003862628, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241759317016252418, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202403099, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1709654400000, receivedDateStr=2024-03-06, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773988096040, onlineDateStr=2026-03-20, pubDate=1727193600000, pubDateStr=2024-09-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773988096040, onlineIssueDateStr=2026-03-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773988096040, creator=13701087609, updateTime=1773988096040, updator=13701087609, issue=Issue{id=1241759317016252418, tenantId=1146029695717560320, journalId=1227665162245664772, year='2024', volume='51', issue='18', pageStart='3265', pageEnd='3456', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773988091898, creator=13701087609, updateTime=1773991617194, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241774103024174010, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241759317016252418, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241774103024174011, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241759317016252418, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=3358, endPage=3362, ext={EN=ArticleExt(id=1241759338876960973, articleId=1241759336003862628, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Research on medical quality evaluation of public traditional Chinese medicine hospitals in Shandong province based on the fuzzy combination of entropy-weighted TOPSIS method and RSR method, columnId=null, journalTitle=Modern Preventive Medicine, columnName=null, runingTitle=null, highlight=null, articleAbstract=
Objective

To provide a reference for the health department and to improve the medical quality by analyzing and evaluating the medical quality of public traditional Chinese medicine hospitals in Shandong Province from 2019 to 2022 comprehensively.

Methods

Based on the survey data of public TCM hospitals participating in performance appraisal in Shandong Province, the medical quality of public TCM hospitals in Shandong Province was comprehensively evaluated by using the entropy-weighted TOPSIS method, RSR method and the fuzzy combination method of the two.

Results

According to the entropy-weighted TOPSIS method, the evaluation results of medical quality were the best in 2019, followed by 2022, with C values of 0.594 4 and 0.548 6, respectively, the evaluation results of RSR method were the best in 2022, followed by 2021, with RSR values of 0.750 0 and 0.688 0, respectively, and the best evaluation results of the two fuzzy joint methods were in 2022 and followed by 2021, and the three evaluation methods showed that the evaluation results of medical quality in 2020 were the worst.

Conclusion

Although the evaluation results of the entropy-weighted TOPSIS and RSR methods and the fuzzy joint method are slightly different, the trend is basically the same, and the medical quality of public TCM hospitals in Shandong Province has been improving year by year from 2019 to 2022. The fuzzy combination method of the two can reflect the evaluation results more scientifically and reasonably, and has certain reference significance for the improvement of medical quality level and management standards.

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目的

综合分析评价2019—2022年山东省公立中医医院医疗质量，为卫生部门提升医疗质量提供参考。

方法

基于山东省参加绩效考核的公立中医医院调查数据，运用熵权TOPSIS法、RSR法以及二者模糊联合方法对山东省公立中医医院的医疗质量进行综合评价。

结果

依据熵权TOPSIS法医疗质量评价结果2019年最好，2022年次之，C值分别为0.594 4，0.548 6；RSR法评价结果为2022年最好，2021年次之，RSR值分别为0.750 0，0.688 0；二者模糊联合法评价结果最好年份为2022年，2021年次之；三种评价方法均显示2020年的医疗质量评价结果最差。

结论

熵权TOPSIS、RSR法及二者模糊联合法评价结果虽略有不同，但趋势基本一致，2019—2022年山东省公立中医医院医疗质量呈逐年提升趋势。采用二者模糊联合的方法能够更科学合理反映其评价结果，对医疗质量水平提升及管理规范具有一定参考借鉴意义。

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元虹，E-mail:；
井淇，E-mail:
, copyrightStatement=本刊刊出的所有文章不代表中华预防医学会和本刊编委会的观点，除非特别声明。, copyrightOwner=中华预防医学会和四川大学华西公共卫生学院, extLink=null, articleAbsUrl=null, sourceXml=icsLQgLJwSyqJ+ir59e7jA==, magXml=ByT2OkgO3SSUVVzGFSdRFw==, pdfUrl=null, pdf=//M5U5nL8WX9HTJCU+m4Sg==, pdfFileSize=622828, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=de3aBaAfNPDQQUhHKgQfAw==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=TdK3qOeGF1wDgOAmaTvQcg==, mapNumber=null, authorCompany=null, fund=null, authors=

石志学（1998—），男，硕士在读，研究方向：卫生政策与管理、卫生健康资源配置

, authorsList=石志学, 张艳秋, 谢抒宸, 孙经杰, 杨小兰, 杜金曼, 刘宝松, 董志伟, 元虹, 井淇)}, authors=[Author(id=1241759351564731085, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759336003862628, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=null, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1241759351929635549, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759336003862628, authorId=1241759351564731085, language=EN, stringName=Zhi-xue SHI, firstName=Zhi-xue, middleName=null, lastName=SHI, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1, address=School of Management, Shandong Second Medical University, Weifang, Shandong 261053, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1241759352097407723, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759336003862628, authorId=1241759351564731085, language=CN, stringName=石志学, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1, address=1.山东第二医科大学管理学院，山东 潍坊 261053, bio={"content":"

石志学（1998—），男，硕士在读，研究方向：卫生政策与管理、卫生健康资源配置

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石志学（1998—），男，硕士在读，研究方向：卫生政策与管理、卫生健康资源配置

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Chinese Health Economics, 2015, 34(10): 40-42.（In Chinese), articleTitle=Study on Chinese health resource allocation after new round medical reform based on index of resource distribution, refAbstract=null)], funds=[Fund(id=1241759367784104255, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759336003862628, awardId=2022RKY07004, language=CN, fundingSource=山东省重点研发计划（软科学）(2022RKY07004), fundOrder=null, country=null), Fund(id=1241759367884767556, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759336003862628, awardId=2021Z004, language=CN, fundingSource=山东省中医药科技项目(2021Z004), fundOrder=null, country=null), Fund(id=1241759367964459334, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759336003862628, awardId=WZY202360, language=CN, fundingSource=山东省卫生健康政策研究课题(WZY202360), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241759349643739818, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759336003862628, xref=1., ext=[AuthorCompanyExt(id=1241759349652128427, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759336003862628, companyId=1241759349643739818, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=School of Management, Shandong Second Medical University, Weifang, Shandong 261053, China), AuthorCompanyExt(id=1241759349660517036, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759336003862628, companyId=1241759349643739818, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.山东第二医科大学管理学院，山东 潍坊 261053)]), AuthorCompany(id=1241759351132717747, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759336003862628, xref=2., ext=[AuthorCompanyExt(id=1241759351183049401, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759336003862628, companyId=1241759351132717747, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.山东省卫生健康委员会医疗管理服务中心)]), AuthorCompany(id=1241759351396958914, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759336003862628, xref=3., ext=[AuthorCompanyExt(id=1241759351417930437, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759336003862628, companyId=1241759351396958914, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3.山东省烟台业达医院)])], figs=[ArticleFig(id=1241759364223140073, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759336003862628, language=EN, label=Fig.1, caption=Medical quality evaluation and analysis indicators, figureFileSmall=8MzMLJv/FqnhOCa2RyfZKw==, figureFileBig=de3aBaAfNPDQQUhHKgQfAw==, tableContent=null), ArticleFig(id=1241759364319609075, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759336003862628, language=CN, label=图1, caption=医疗质量评价与分析指标, figureFileSmall=8MzMLJv/FqnhOCa2RyfZKw==, figureFileBig=de3aBaAfNPDQQUhHKgQfAw==, tableContent=null), ArticleFig(id=1241759364571267331, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759336003862628, language=EN, label=Table 1, caption=

Original data table of medical quality indicators of public traditional Chinese medicine hospitals in Shandong Province from 2019 to 2022

, figureFileSmall=null, figureFileBig=null, tableContent=
年份X1X2X3X4X5X6X7X8
201924 124 9391 611 95384.5931.519.320.792 30.051 90.176 9
202021 564 6771 518 94974.5527.689.550.812 10.029 40.305 6
202127 768 7261 855 72478.3629.329.590.816 80.015 60.206 3
202228 104 6661 886 47772.3128.399.250.832 40.020 60.430 6
), ArticleFig(id=1241759364667736331, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759336003862628, language=CN, label=表1, caption=

2019—2022年山东省公立中医医院医疗质量指标原始数据表

, figureFileSmall=null, figureFileBig=null, tableContent=
年份X1X2X3X4X5X6X7X8
201924 124 9391 611 95384.5931.519.320.792 30.051 90.176 9
202021 564 6771 518 94974.5527.689.550.812 10.029 40.305 6
202127 768 7261 855 72478.3629.329.590.816 80.015 60.206 3
202228 104 6661 886 47772.3128.399.250.832 40.020 60.430 6
), ArticleFig(id=1241759364747428113, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759336003862628, language=EN, label=Table 2, caption=

The entropy method calculates the index weight results

, figureFileSmall=null, figureFileBig=null, tableContent=
评价指标信息熵值e信息效用值d权重（%）
X5 出院患者平均住院日0.643 50.356 515.45
X4 病床周转次数0.647 00.353 015.30
X3 病床使用率0.656 30.343 714.90
X2 出院人次数0.701 30.298 712.95
X1 门诊人次数0.742 20.257 811.18
X6医院病例组合指数0.759 80.240 210.41
X7 低风险组病例死亡率0.763 70.236 310.24
X8 手术并发症发生率0.779 40.220 69.56
), ArticleFig(id=1241759364881645849, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759336003862628, language=CN, label=表2, caption=

熵值法计算指标权重结果

, figureFileSmall=null, figureFileBig=null, tableContent=
评价指标信息熵值e信息效用值d权重（%）
X5 出院患者平均住院日0.643 50.356 515.45
X4 病床周转次数0.647 00.353 015.30
X3 病床使用率0.656 30.343 714.90
X2 出院人次数0.701 30.298 712.95
X1 门诊人次数0.742 20.257 811.18
X6医院病例组合指数0.759 80.240 210.41
X7 低风险组病例死亡率0.763 70.236 310.24
X8 手术并发症发生率0.779 40.220 69.56
), ArticleFig(id=1241759366861357341, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759336003862628, language=EN, label=Table 3, caption=

Entropy-weighted TOPSIS method C value and ranking results

, figureFileSmall=null, figureFileBig=null, tableContent=
年份D+D-C排序
20190.186 60.273 50.594 41
20200.303 70.099 40.246 74
20210.198 00.237 40.545 33
20220.220 10.267 50.548 62
), ArticleFig(id=1241759366995575074, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759336003862628, language=CN, label=表3, caption=

熵权TOPSIS法C值及排序结果

, figureFileSmall=null, figureFileBig=null, tableContent=
年份D+D-C排序
20190.186 60.273 50.594 41
20200.303 70.099 40.246 74
20210.198 00.237 40.545 33
20220.220 10.267 50.548 62
), ArticleFig(id=1241759367188513064, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759336003862628, language=EN, label=Table 4, caption=

The indicators of the RSR method are ranked and ranked

, figureFileSmall=null, figureFileBig=null, tableContent=
年份R1R2R3R4R5R6R7R8RSR排序
2019224131140.563 03
2020112422220.500 04
2021333213430.688 02
2022441344310.750 01
), ArticleFig(id=1241759367373062447, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759336003862628, language=CN, label=表4, caption=

RSR法各项指标编秩及排序

, figureFileSmall=null, figureFileBig=null, tableContent=
年份R1R2R3R4R5R6R7R8RSR排序
2019224131140.563 03
2020112422220.500 04
2021333213430.688 02
2022441344310.750 01
), ArticleFig(id=1241759367507280181, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759336003862628, language=EN, label=Table 5, caption=

Fuzzy joint evaluation and ranking of entropy weight TOPSIS and RSR method

, figureFileSmall=null, figureFileBig=null, tableContent=
年份C值排序RSR值排序0.1C+0.9RSR排序0.5C+0.5RSR排序0.9C+0.1RSR排序
20190.594 410.563 030.566 130.578 730.591 31
20200.246 740.500 040.474 740.373 340.272 04
20210.545 330.688 020.673 720.616 720.559 63
20220.548 620.750 010.729 910.649 310.568 72
), ArticleFig(id=1241759367624720698, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759336003862628, language=CN, label=表5, caption=

熵权TOPSIS与RSR法模糊联合评价及排序

, figureFileSmall=null, figureFileBig=null, tableContent=
年份C值排序RSR值排序0.1C+0.9RSR排序0.5C+0.5RSR排序0.9C+0.1RSR排序
20190.594 410.563 030.566 130.578 730.591 31
20200.246 740.500 040.474 740.373 340.272 04
20210.545 330.688 020.673 720.616 720.559 63
20220.548 620.750 010.729 910.649 310.568 72
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基于熵权TOPSIS法与RSR法模糊联合的山东省公立中医医院医疗质量评价研究
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石志学 1 , 张艳秋 1 , 谢抒宸 1 , 孙经杰 2 , 杨小兰 1 , 杜金曼 1 , 刘宝松 3 , 董志伟 1 , 元虹 1 , 井淇 1
现代预防医学 | 卫生政策与管理 2024,51(18): 3358-3362
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现代预防医学 | 卫生政策与管理 2024, 51(18): 3358-3362
基于熵权TOPSIS法与RSR法模糊联合的山东省公立中医医院医疗质量评价研究
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石志学1, 张艳秋1, 谢抒宸1, 孙经杰2, 杨小兰1, 杜金曼1, 刘宝松3, 董志伟1, 元虹1 , 井淇1
作者信息
  • 1.山东第二医科大学管理学院，山东 潍坊 261053
  • 2.山东省卫生健康委员会医疗管理服务中心
  • 3.山东省烟台业达医院

    • 石志学（1998—），男，硕士在读，研究方向：卫生政策与管理、卫生健康资源配置

通讯作者:

元虹，E-mail:；
井淇，E-mail:
Research on medical quality evaluation of public traditional Chinese medicine hospitals in Shandong province based on the fuzzy combination of entropy-weighted TOPSIS method and RSR method
Zhi-xue SHI1, Yan-qiu ZHANG1, Shu-chen XIE1, Jing-jie SUN2, Xiao-lan YANG1, Jin-man DU1, Bao-song LIU3, Zhi-wei DONG1, Hong YUAN1 , Qi JING1
Affiliations
  • School of Management, Shandong Second Medical University, Weifang, Shandong 261053, China
出版时间: 2024-09-25 doi: 10.20043/j.cnki.MPM.202403099
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摘要
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目的

综合分析评价2019—2022年山东省公立中医医院医疗质量，为卫生部门提升医疗质量提供参考。

方法

基于山东省参加绩效考核的公立中医医院调查数据，运用熵权TOPSIS法、RSR法以及二者模糊联合方法对山东省公立中医医院的医疗质量进行综合评价。

结果

依据熵权TOPSIS法医疗质量评价结果2019年最好，2022年次之，C值分别为0.594 4，0.548 6；RSR法评价结果为2022年最好，2021年次之，RSR值分别为0.750 0，0.688 0；二者模糊联合法评价结果最好年份为2022年，2021年次之；三种评价方法均显示2020年的医疗质量评价结果最差。

结论

熵权TOPSIS、RSR法及二者模糊联合法评价结果虽略有不同，但趋势基本一致，2019—2022年山东省公立中医医院医疗质量呈逐年提升趋势。采用二者模糊联合的方法能够更科学合理反映其评价结果，对医疗质量水平提升及管理规范具有一定参考借鉴意义。

公立中医医院  /  医疗质量  /  熵权TOPSIS法  /  RSR法  /  模糊联合
Objective

To provide a reference for the health department and to improve the medical quality by analyzing and evaluating the medical quality of public traditional Chinese medicine hospitals in Shandong Province from 2019 to 2022 comprehensively.

Methods

Based on the survey data of public TCM hospitals participating in performance appraisal in Shandong Province, the medical quality of public TCM hospitals in Shandong Province was comprehensively evaluated by using the entropy-weighted TOPSIS method, RSR method and the fuzzy combination method of the two.

Results

According to the entropy-weighted TOPSIS method, the evaluation results of medical quality were the best in 2019, followed by 2022, with C values of 0.594 4 and 0.548 6, respectively, the evaluation results of RSR method were the best in 2022, followed by 2021, with RSR values of 0.750 0 and 0.688 0, respectively, and the best evaluation results of the two fuzzy joint methods were in 2022 and followed by 2021, and the three evaluation methods showed that the evaluation results of medical quality in 2020 were the worst.

Conclusion

Although the evaluation results of the entropy-weighted TOPSIS and RSR methods and the fuzzy joint method are slightly different, the trend is basically the same, and the medical quality of public TCM hospitals in Shandong Province has been improving year by year from 2019 to 2022. The fuzzy combination method of the two can reflect the evaluation results more scientifically and reasonably, and has certain reference significance for the improvement of medical quality level and management standards.

Public Traditional Chinese Medicine Hospital  /  Medical quality  /  Entropy-weighted TOPSIS method  /  RSR method  /  Fuzzy combination
石志学, 张艳秋, 谢抒宸, 孙经杰, 杨小兰, 杜金曼, 刘宝松, 董志伟, 元虹, 井淇. 基于熵权TOPSIS法与RSR法模糊联合的山东省公立中医医院医疗质量评价研究. 现代预防医学, 2024 , 51 (18) : 3358 -3362 . DOI: 10.20043/j.cnki.MPM.202403099
Zhi-xue SHI, Yan-qiu ZHANG, Shu-chen XIE, Jing-jie SUN, Xiao-lan YANG, Jin-man DU, Bao-song LIU, Zhi-wei DONG, Hong YUAN, Qi JING. Research on medical quality evaluation of public traditional Chinese medicine hospitals in Shandong province based on the fuzzy combination of entropy-weighted TOPSIS method and RSR method[J]. Modern Preventive Medicine, 2024 , 51 (18) : 3358 -3362 . DOI: 10.20043/j.cnki.MPM.202403099
正文
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在“十四五”时期，我国经济社会发展的主旨是实现高质量发展，公立医院的运营和发展直接关系到经济社会的发展质量，而医疗质量是医院发展的生命线，也是医院管理的持久焦点[1]。医疗质量是指医疗服务达到人群与个体所期望的健康结果程度，以及医疗服务与现有专业知识的一致程度[2]。自2009年发布《关于深化医药卫生体制改革的意见》以来，医疗质量便受到了广泛关注和重视。2021年，国务院办公厅印发《关于推动公立医院高质量发展的意见》，这标志着公立医院进入高质量发展的新阶段。在当前“国考”下，医疗质量这一指标在三级公立医院绩效考核指标体系中占据了重要地位，其总分占比高达43%，更凸显其重要性[3]。
在新的发展阶段下，中医药在治未病和慢性病等领域的优势逐渐显现，人民群众对中医药服务的需求持续增加。与此同时，随着人民群众生活品质和健康管理意识的不断提高，对医疗质量也有了更高的期待和要求[4]。公立中医医院作为提供中医药服务的重要机构，在医疗服务中发挥着重要作用。在高质量发展的背景下，更需要进一步提高公立中医医院的医疗质量和安全水平，以助力医疗卫生事业高质量发展。本研究运用了熵权TOPSIS法、RSR法以及二者模糊联合评价的方法，对2019—2022年山东省公立中医医院的医疗质量进行分析评价，旨在为改善公立中医医院医疗质量提供工作思路和参考借鉴。
1　资料与方法
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1.1　数据来源
本研究数据主要来源于山东省参加绩效考核的公立中医医院相关统计数据，鉴于数据可比性和可得性，纳入研究时间截点为2019—2022年。在这四年中，涵盖在评价中的公立中医医院数量分别为83、92、121和124所。
1.2　评价指标选取
依据近年国家陆续印发的国务院办公厅《关于加强三级公立医院绩效考核工作的意见》、《关于推动公立医院高质量发展的意见》及2022年版的《三级医院评审标准》等政策文件内有关医疗质量指标，参考文献查阅结果[5-8]和专家建议，综合考虑医疗质量指标的重要性、可操作性和研究的科学性等原则，结合高质量发展背景下提高医疗服务效率和降低医疗事故发生率的医疗质量要求，选取如图1所示的医疗服务量、工作效率和医疗安全等3个维度8项医疗质量评价指标，分别用X1—X8表示。
1.3　研究方法
1.3.1　熵权法
本研究运用熵权法对各指标进行客观确定权重，包括以下运算步骤：（1）数据标准化处理。在确定各指标属性基础上，构建原始数据矩阵X=(xij)m×n，（i=1,2，…，m；j=1,2，…，n），其中m为纳入年份数，n为评价指标数。采用极值法对原始数据进行无量纲化和同趋势化处理，中性指标的标准化处理方法同高优指标一致。高优指标表示数值越高越好，公式为，而低优指标则反之，公式为。（2）计算各指标熵值，其中，。（3）差异性系数gj=1－hj。（4）计算指标权重。
1.3.2　TOPSIS法
在熵权法计算基础之上继续进行分析，进行归一化Z转换得到正负理想解，然后依据公式；分别得到各评价对象与正负理想解的距离值Di+和Di—，最后运用公式求得理想接近程度C值并进行优劣排次[9]。
1.3.3　RSR秩和比分析法
RSR法是一种综合评价方法[10]，在医院医疗质量评价领域多有应用，其基本思想是构建被评价对象和评价指标的n×m矩阵，首先是对数据矩阵求秩，然后计算出RSR值并进行排序和分档处理[6]。
1.3.4　熵权TOPSIS和RSR法的模糊联合
在运用熵权TOPSIS和RSR法的基础上，采用FUZZYSET思想[11]使用二者模糊联合方法进行综合评价，以确保研究的科学性。
2　结果
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2.1　2019—2022年山东省公立中医医院医疗质量现况
在研究时间段内，针对研究对象进行了相关数据的收集与分析。由表1可知，2019—2022年山东省公立中医医院的门诊人次数X1、出院人次数X2除2020年呈下降趋势外，其余年份均为上升趋势；病床使用率X3、病床周转次数X4和出院患者平均住院日X5为波动式下降；CMI值X6近年来均呈上升态势；低风险组病例死亡率X7和手术并发症发生率X8变化趋势较为稳定，波动起伏不明显。由此进一步看出，尽管病床使用率和病床周转次数为下降趋势，但门诊人次数和出院人次数在近两年逐年增加的同时，出院患者平均住院日呈现下降趋势，CMI值逐年上升，而低风险组病例死亡率和手术并发症发生率这两个指标变化不大，一直维持于发生率极低的状态。
2.2　熵权TOPSIS法评价结果
2.2.1　熵权法确定指标权重
经对各指标进行标准化处理和相关运算，表2显示医疗质量评价指标的权重分配情况。医疗质量评价指标3个维度的权重大小依次为：工作效率（45.65%）>医疗安全（30.21%）>医疗服务量（24.13%）。具体而言，出院患者平均住院日X5和病床周转次数X4占据较大权重，说明这两项指标的提高将显著改善医疗质量；相反，低风险组病例死亡率X7和手术并发症发生率X8所占权重较小。
2.2.2　TOPSIS法评价结果
依据研究对象与最优方案接近程度的C值，可知评价结果中C值范围为0.246 7~0.594 4，其中2019年C值最大，代表在这四年中，该年份的医疗质量为最优水平，其评价结果最好；而2020年C值最低，表明该年份的评价结果相对较差。见表3。
2.3　RSR法评价结果
首先，对表1中的指标原始数据进行处理。高优指标按照从小到大的原则编秩，低优指标则从大到小编秩。然后依据公式计算RSR值，并根据其值大小进行排序。RSR值∈（0,1），其值越接近于1表示结果越好，反之越差。编秩处理和RSR值见表4。
2.4　模糊联合评价结果
运用FUZZYSET理论使用熵权TOPSIS和RSR法二者模糊联合法综合评价2019—2022年山东省公立中医医院医疗质量，赋予C值和RSR值权重，设权重比W1和W2并分以下5档，1:0、0:1、0.1:0.9、0.5:0.5、0.9:0.1，即求W1C×W2RSR值并排序，W1C×W2RSR值∈（0,1），越接近于1表示结果越好[11]，结果详见表5。
3　讨论与展望
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3.1　运用熵权TOPSIS与RSR模糊联合法的科学合理性
熵权TOPSIS法和RSR法在卫生管理领域应用广泛。然而，单独使用任何一种评价方法进行研究分析均存在一定的局限性[12]，可能对评价结果造成一定的负面影响。熵权法客观确定指标权重可以有效避免人为因素造成的主观偏差[13]，TOPSIS法能够敏感反映原始数据的差异，获得各指标值与最优方案的相对接近程度C值并进行优劣排序，但此方法结果易受异常值的影响，且无法反映各指标的作用大小[14]。RSR法是一种方法简单易于操作的非参数评价方法，但在编秩处理中会导致指标信息缺失而无法有效利用数据。
为了弥补单一使用TOPSIS法或RSR法的局限，本研究采用熵权TOPSIS与RSR模糊联合法进行综合评价，充分利用FUZZYSET理论的独特优势。该方法依据C值和RSR值分别赋予比值权重，分为5档进行研究分析，并采用“择多原则”得出评价结果[15]，以保障对2019—2022年山东省公立中医医院医疗质量评价结果的客观准确性与科学合理性。
3.2　2019—2022年山东省公立中医医院医疗质量优化提升
近年来，山东省公立中医医院医疗质量态势为逐步上升。根据熵权TOPSIS法评价结果显示，2019年的医疗质量评价最好，2022年紧随其后，其评价结果较好，而2020年的医疗质量最差。另一方面，依据RSR法的评价结果显示，2020年的医疗质量评价仍然最差，而2022年的评价结果最好，与TOPSIS法的评价结果稍有不同。经过二者模糊联合法，通过对比可以看出，在不同权重比下评价结果存在一致性，其中权重比W1和W2为0:1、0.1:0.9、0.5:0.5三档时其评价结果一致，为1:0、0.9:0.1两档时结果一致，根据“择多原则”[14]，得出2022年医疗质量最好，其次为2021年、2019年、2020年的结论。因此，可进一步说明2019—2022年山东省公立中医医院的医疗质量正在逐步优化提升。
自2009年新医改后，医院管理进一步规范化，质量管理意识得到进一步提升。2016年颁布并贯彻落实《医疗质量管理办法》，更有力地保障了医疗质量提高及管理优化。然而，突发卫生事件对我省公立中医医院医疗服务的效率和质量、医疗资源合理配置产生了负面影响，导致医疗质量不可避免地出现下降。国家卫生健康委员会于2023年启动全面提升医疗质量行动计划，并开启了为期三年的新行动，这一举措使得公立医院高质量发展呈现全新姿态，不仅提升了医院内部管理，也对整个医疗体系的质量提升和医疗流程优化产生了积极影响。医院管理者已认识到医疗质量和患者安全对医院管理至关重要，成为医院提升医疗质量的重要因素。
3.3　研究启示及展望
本研究发现，低风险组病例死亡率和手术并发症发生率这两个指标在医疗质量评价中所占权重较小，且其发生率非常低。但考虑到研究样本范围仅限于公立中医医院，因此对于以上提到的两个指标的外推性尚待商榷。尽管这些指标已被纳入三级公立医院绩效考核医疗质量评价指标、三级医院评审标准医疗质量评价指标及DRG医疗质量评价指标[3]，但其在其他医疗机构和不同环境中的适用性仍需进一步验证。值得注意的是，过分关注这类极小概率的指标可能会造成医疗资源的浪费，因此在未来的研究中需要更多的权衡分析，这与我国研究学者针对此类问题的研究结论保持一致[16]，未来研究在探讨这些指标的价值和适用性方面还有待深入。此外，医疗机构在医疗质量方面还面临医疗质量安全基础不稳固、理念不牢固、管理不到位等诸多挑战。在未来的研究中，应持续关注国家政策文件对指标的规定，重视医院内部管理和流程的优化[17]。同时，将医疗质量视为保证医院高质量发展的重要衡量标准[7]，拓展研究样本范围，以提高结果的普适性和可靠性，确保医院质量评价的科学性、全面性和准确性。这样才能更全面、客观地评价医院质量运行情况，从而提高医疗质量和患者安全水平。
基金
收起
  • 山东省重点研发计划（软科学）(2022RKY07004)
  • 山东省中医药科技项目(2021Z004)
  • 山东省卫生健康政策研究课题(WZY202360)
文献
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2024年第51卷第18期
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doi: 10.20043/j.cnki.MPM.202403099
  • 接收时间：2024-03-06
  • 首发时间：2026-03-20
  • 出版时间：2024-09-25
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  • 收稿日期：2024-03-06
基金
山东省重点研发计划（软科学）(2022RKY07004)
山东省中医药科技项目(2021Z004)
山东省卫生健康政策研究课题(WZY202360)
作者信息
    1.山东第二医科大学管理学院，山东 潍坊 261053
    2.山东省卫生健康委员会医疗管理服务中心
    3.山东省烟台业达医院

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