Article(id=1241036248891052092, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241036242561855785, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202501182, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1736438400000, receivedDateStr=2025-01-10, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773815699073, onlineDateStr=2026-03-18, pubDate=1756051200000, pubDateStr=2025-08-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773815699073, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773815699073, creator=13701087609, updateTime=1773815699073, updator=13701087609, issue=Issue{id=1241036242561855785, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='16', pageStart='2881', pageEnd='3072', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773815697565, creator=13701087609, updateTime=1773840190562, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241138973712634304, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241036242561855785, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241138973712634305, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241036242561855785, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=2986, endPage=2993, ext={EN=ArticleExt(id=1241036249457283137, articleId=1241036248891052092, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Analysis of influencing factors of medical staff’s voluntary reporting of medical adverse events based on logistic regression and decision tree models, columnId=1228016567846367388, journalTitle=Modern Preventive Medicine, columnName=Health Policy and Management, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the influencing factors of medical staff’s active reporting of adverse medical events by using Logistic regression and decision tree models, and to provide corresponding solutions.

Methods

A total of 811 medical workers in a tertiary hospital were investigated by random sampling. Logistic regression and decision tree model were used to analyze the factors of active reporting of medical adverse events by medical staff, and the area under ROC curve was calculated to compare and judge the analysis effect of the two models.

Results

Only 55.1% of the medical staff in this hospital have voluntarily reported medical adverse events. The results of the two models showed that occupation, working years, knowledge of the reporting process of the hospital, and whether additional work would be added to the cumbersome reporting procedures were the influencing factors for the active reporting of medical staff (P<0.05). The AUC of Logistic regression model was greater than that of decision tree model, and the difference was statistically significant (Z=3.424, P<0.001).

Conclusion

The rate of active reporting of medical adverse events by medical staff in this hospital is relatively low. It is suggested that multiple measures be taken to promote the active reporting by medical staff.

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目的

采用logistic回归和决策树模型分别探讨医务人员主动上报医疗不良事件的影响因素并提供对应的解决措施。

方法

采用单纯随机抽样,对某三级医院811名医务人员进行调查。采用logistic回归与决策树模型对医务人员医疗不良事件主动上报的因素进行分析,并计算ROC曲线下面积以比较判断两模型分析效果。

结果

该院仅有55.1%的医务人员主动上报过医疗不良事件。两模型结果显示职业、工作年限、对本院报告流程的知晓情况、上报程序繁琐程度是否会增加额外工作是影响医务人员主动上报的影响因素(P<0.05);Logistic回归模型AUC大于决策树模型,差异具有统计学意义(Z=3.424,P<0.001)。

结论

该院医务人员医疗不良事件主动上报率较低,建议采取多种措施以促进医务人员主动上报。

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张江萍,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=TGq8d8obQ5waS2rRc89m1A==, magXml=1dZm4m5R1LBsp0tOa4FLIQ==, pdfUrl=null, pdf=uDmLlPX1sFWAQJsCka4XEA==, pdfFileSize=943658, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=oZu990WKjTnqv3vsEfhbGw==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=NJYs9otT9k3bTiYwU5RvHg==, mapNumber=null, authorCompany=null, fund=null, authors=

陈昌阳(2000—),女,硕士在读,研究方向:疾病预防与控制

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陈昌阳(2000—),女,硕士在读,研究方向:疾病预防与控制

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陈昌阳(2000—),女,硕士在读,研究方向:疾病预防与控制

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(In Chinese), articleTitle=Research on adverse event reporting information system of X psychiatric hospital based on PDCA cycle, refAbstract=null), Reference(id=1241057512758235441, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036248891052092, doi=null, pmid=null, pmcid=null, year=2018, volume=31, issue=20, pageStart=118, pageEnd=120, url=null, language=null, rfNumber=[20], rfOrder=35, authorNames=谭智, 罗碧眉, 谷玉婷, journalName=医学信息, refType=null, unstructuredReference=谭智,罗碧眉,谷玉婷,等.广州市某三甲医院分院医疗安全不良事件情况分析[J].医学信息2018,31(20):118-120., articleTitle=广州市某三甲医院分院医疗安全不良事件情况分析, refAbstract=null), Reference(id=1241057512888258871, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036248891052092, doi=null, pmid=null, pmcid=null, year=2018, volume=31, issue=20, pageStart=118, pageEnd=120, url=null, language=null, rfNumber=[20], rfOrder=36, authorNames=Tan Z, Luo BM, Gu YT, journalName=Medical Information, refType=null, unstructuredReference=Tan Z, Luo BM, Gu YT, et al. 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(In Chinese), articleTitle=Analysis of adverse events of medical safety in a branch hospital of a third grade a hospital in Guangzhou city, refAbstract=null), Reference(id=1241057513102168385, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036248891052092, doi=null, pmid=null, pmcid=null, year=2021, volume=41, issue=4, pageStart=52, pageEnd=55, 59, url=null, language=null, rfNumber=[21], rfOrder=37, authorNames=董晓飞, 钱宇, 王小合, journalName=中国医院管理, refType=null, unstructuredReference=董晓飞,钱宇,王小合,等.医院安全不良事件管理体系建设的构想与展望[J].中国医院管理2021,41(4):52-55, 59., articleTitle=医院安全不良事件管理体系建设的构想与展望, refAbstract=null), Reference(id=1241057513290912074, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036248891052092, doi=null, pmid=null, pmcid=null, year=2021, volume=41, issue=4, pageStart=52, pageEnd=55, 59, url=null, language=null, rfNumber=[21], rfOrder=38, authorNames=Dong XF, Qian Y, Wang XH, journalName=Chinese Hospital Management, refType=null, unstructuredReference=Dong XF, Qian Y, Wang XH, et al. 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(In Chinese), articleTitle=Analysis of big data and strategy research on medical safety (adverse)events in China, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1241057503493018297, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036248891052092, xref=1., ext=[AuthorCompanyExt(id=1241057503497212603, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036248891052092, companyId=1241057503493018297, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=School of Public Health, the key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, Guizhou 561113, China), AuthorCompanyExt(id=1241057503509795516, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036248891052092, companyId=1241057503493018297, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.贵州医科大学公共卫生与健康学院,环境污染与疾病监控教育部重点实验室,贵州 贵阳 561113)]), AuthorCompany(id=1241057503572710080, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036248891052092, xref=2., ext=[AuthorCompanyExt(id=1241057503581098690, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036248891052092, companyId=1241057503572710080, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.贵阳市公共卫生救治中心)])], figs=[ArticleFig(id=1241057507049788390, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036248891052092, language=EN, label=Fig.1, caption=Visualization of variable importance, figureFileSmall=LwQyncOSCpoYaAO1h+sd2Q==, figureFileBig=Z7klNvvbWnBZtME1QOYSkw==, tableContent=null), ArticleFig(id=1241057507142063083, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036248891052092, language=CN, label=图1, caption=变量重要性可视化, figureFileSmall=LwQyncOSCpoYaAO1h+sd2Q==, figureFileBig=Z7klNvvbWnBZtME1QOYSkw==, tableContent=null), ArticleFig(id=1241057507288863736, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036248891052092, language=EN, label=Fig.2, caption=Visualization of the decision tree for adverse event reporting, figureFileSmall=jeaKkyXtQ6McP66850PhhA==, figureFileBig=IQFwmAhkM3Rqqg8+BHRr3g==, tableContent=null), ArticleFig(id=1241057507418886145, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036248891052092, language=CN, label=图2, caption=医务人员主动上报行为决策树可视化, figureFileSmall=jeaKkyXtQ6McP66850PhhA==, figureFileBig=IQFwmAhkM3Rqqg8+BHRr3g==, tableContent=null), ArticleFig(id=1241057507536326668, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036248891052092, language=EN, label=Fig.3, caption=Comparison of ROC curves between the two models, figureFileSmall=KCplpgyQ+e2qumB7lI7yeQ==, figureFileBig=hxJZ6EWiYfBSSehQa8Nlzg==, tableContent=null), ArticleFig(id=1241057507636989976, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036248891052092, language=CN, label=图3, caption=两模型ROC曲线的对比图, figureFileSmall=KCplpgyQ+e2qumB7lI7yeQ==, figureFileBig=hxJZ6EWiYfBSSehQa8Nlzg==, tableContent=null), ArticleFig(id=1241057507813150754, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036248891052092, language=EN, label=Table 1, caption=

The results of univariate analysis of proactive reporting behaviors of medical staff

, figureFileSmall=null, figureFileBig=null, tableContent=
指标分组总人数主动上报人数主动上报人数占比(%)χ2P
性别2019145.2710.3510.001
60935558.29
工作年限(年)≤1711318.31
>1~2602541.6752.709<0.001
>2~51377454.01
>5~101539662.75
>1038923861.18
职业医生28215856.03
护士41226764.8181.153<0.001
医技人员992020.20
行政及其他人员171 5.88
学历专科及以下1468457.53
本科60033555.835.9650.051
硕士及以上642640.63
职称初级41321351.57
中级28117160.8521.820<0.001
高级905763.33
其他26519.23
是否为医院中层以上干部624674.199.9320.002
74840053.48
定义的知晓情况不知道4125.00
了解部分1103531.82-<0.001
了解掌握37219652.69
熟练掌握32421466.05
分类的知晓情况不知道13538.46
了解部分1856937.3034.806<0.001
了解掌握34220058.48
熟练掌握27017263.70
各等级具体内容的知晓情况不知道25728.00
了解部分2299641.9233.590<0.001
了解掌握32819960.67
熟练掌握22814463.16
报告原则的知晓情况不知道13323.08
了解部分1816435.3651.793<0.001
了解掌握33819156.51
熟练掌握27818867.63
本院报告流程的知晓情况不知道16425.00
了解部分1565333.9758.028<0.001
了解掌握33217853.61
熟练掌握30621168.95
上报程序繁琐程度是否会增加额外的工作非常不赞同1558353.55
不赞同39322757.76
不确定1275341.7314.8310.005
赞同1086358.33
非常赞同272074.07
平台在线报告是否会增加上报意愿非常不赞同462860.87
不赞同1025957.84
不确定20310551.721.9130.752
赞同40622455.17
非常赞同533056.60
激励政策是否会增加上报意愿非常不赞同181161.11
不赞同623454.84
不确定1466443.849.4340.051
赞同49628557.46
非常赞同885259.09
上报不良事件,其他人是否会对您有负面看法非常不赞同1418963.12
不赞同38021055.26
不确定22811349.56-0.152
赞同563155.36
非常赞同5360.00
上报他人不良事件是否会影响同事间的关系非常不赞同1449465.28
不赞同36720856.68
不确定21910347.0314.0650.007
赞同612947.54
非常赞同191263.16
领导是否鼓励上报77043556.49
9222.22-0.001
不明确31929.03
发生不良事件时,科室、相关部门是否会制定相应措施防止再次发生77543255.74
21942.863.5273.527
不明确14535.71
发生不良事件后,科室领导是否会组织讨论77043756.75
4250.00-<0.001
不明确36719.44
科室有无管理不良事件的人员72041056.949.2830.002
903640.00
), ArticleFig(id=1241057507918008361, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036248891052092, language=CN, label=表1, caption=

医务人员主动上报行为单因素分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
指标分组总人数主动上报人数主动上报人数占比(%)χ2P
性别2019145.2710.3510.001
60935558.29
工作年限(年)≤1711318.31
>1~2602541.6752.709<0.001
>2~51377454.01
>5~101539662.75
>1038923861.18
职业医生28215856.03
护士41226764.8181.153<0.001
医技人员992020.20
行政及其他人员171 5.88
学历专科及以下1468457.53
本科60033555.835.9650.051
硕士及以上642640.63
职称初级41321351.57
中级28117160.8521.820<0.001
高级905763.33
其他26519.23
是否为医院中层以上干部624674.199.9320.002
74840053.48
定义的知晓情况不知道4125.00
了解部分1103531.82-<0.001
了解掌握37219652.69
熟练掌握32421466.05
分类的知晓情况不知道13538.46
了解部分1856937.3034.806<0.001
了解掌握34220058.48
熟练掌握27017263.70
各等级具体内容的知晓情况不知道25728.00
了解部分2299641.9233.590<0.001
了解掌握32819960.67
熟练掌握22814463.16
报告原则的知晓情况不知道13323.08
了解部分1816435.3651.793<0.001
了解掌握33819156.51
熟练掌握27818867.63
本院报告流程的知晓情况不知道16425.00
了解部分1565333.9758.028<0.001
了解掌握33217853.61
熟练掌握30621168.95
上报程序繁琐程度是否会增加额外的工作非常不赞同1558353.55
不赞同39322757.76
不确定1275341.7314.8310.005
赞同1086358.33
非常赞同272074.07
平台在线报告是否会增加上报意愿非常不赞同462860.87
不赞同1025957.84
不确定20310551.721.9130.752
赞同40622455.17
非常赞同533056.60
激励政策是否会增加上报意愿非常不赞同181161.11
不赞同623454.84
不确定1466443.849.4340.051
赞同49628557.46
非常赞同885259.09
上报不良事件,其他人是否会对您有负面看法非常不赞同1418963.12
不赞同38021055.26
不确定22811349.56-0.152
赞同563155.36
非常赞同5360.00
上报他人不良事件是否会影响同事间的关系非常不赞同1449465.28
不赞同36720856.68
不确定21910347.0314.0650.007
赞同612947.54
非常赞同191263.16
领导是否鼓励上报77043556.49
9222.22-0.001
不明确31929.03
发生不良事件时,科室、相关部门是否会制定相应措施防止再次发生77543255.74
21942.863.5273.527
不明确14535.71
发生不良事件后,科室领导是否会组织讨论77043756.75
4250.00-<0.001
不明确36719.44
科室有无管理不良事件的人员72041056.949.2830.002
903640.00
), ArticleFig(id=1241057508048031795, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036248891052092, language=EN, label=Table 2, caption=

Medical staff actively reported the results of logistic regression analysis

, figureFileSmall=null, figureFileBig=null, tableContent=
指标分组参照组BPOR(95%CIGVIF^
(1/2df)
性别0.2040.3471.23(0.80~1.88)1.150
工作年限>1~2≤11.2290.0053.42(1.45~8.09)1.096
>2~51.799<0.0016.04(2.84~12.85)
>5~101.977<0.0017.22(3.40~15.33)
>101.970<0.0017.17(3.40~15.12)
职业护士医生0.0830.7091.09(0.70~1.68)1.168
医技人员-1.477<0.0010.23(0.12~0.43)
行政及其他-2.5910.0320.07(0.01~0.80)
职称中级初级0.1360.5401.15(0.74~1.77)1.207
高级0.1110.7551.12(0.56~2.24)
其他-0.3090.6640.73(0.18~2.95)
是否为医院中层以上干部-0.5290.1530.59(0.28~1.22)1.137
定义的知晓情况了解部分不知道0.0390.9801.04(0.05~22.88)1.446
了解掌握0.0170.9911.02(0.04~23.41)
熟练掌握0.6300.6991.88(0.08~45.60)
分类的知晓情况了解部分不知道-0.2540.7670.78(0.15~4.15)1.555
了解掌握-0.0750.9340.93(0.16~5.46)
熟练掌握-0.6960.4710.50(0.08~3.31)
各等级具体内容的知晓情况了解部分不知道0.5140.4701.67(0.42~6.73)1.674
了解掌握0.7350.3432.08(0.46~9.15)
熟练掌握-0.3300.7040.72(0.13~3.95)
报告原则的知晓情况了解部分不知道0.5940.5931.81(0.21~15.94)1.916
了解掌握0.7450.5132.11(0.23~19.68)
熟练掌握1.4860.2294.42(0.39~49.70)
本院报告流程的知晓情况了解部分不知道-0.2460.8030.78(0.11~5.38)1.743
了解掌握-0.0510.9600.95(0.13~6.99)
熟练掌握0.5700.6001.77(0.21~14.88)
不良事件上报程序繁琐程度是否会增加额外的工作不赞同非常不赞同0.7750.0042.17(1.29~3.66)1.130
不确定0.6190.0761.86(0.94~3.68)
赞同0.9530.0072.59(1.30~5.16)
非常赞同1.7710.0035.88(1.84~18.78)
上报他人不良事件是否会影响同事间的关系不赞同非常不赞同-0.3920.1600.68(0.39~1.17)1.127
不确定-0.5940.0700.55(0.29~1.05)
赞同-0.9320.0230.39(0.18~0.88)
非常赞同-0.1890.7640.83(0.24~2.84)
领导是否鼓励上报-1.8750.0400.15(0.03~0.92)1.073
不明确-0.1410.7840.87(0.32~2.39)
发生不良事件后,科室领导是否会组织讨论0.5220.7041.69(0.11~25.00)1.111
不明确-1.1450.0240.32(0.12~0.86)
科室有无管理不良事件的人员-0.0260.9310.97(0.54~1.75)1.074
), ArticleFig(id=1241057508186443837, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036248891052092, language=CN, label=表2, caption=

医务人员主动上报行为logistic回归分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
指标分组参照组BPOR(95%CIGVIF^
(1/2df)
性别0.2040.3471.23(0.80~1.88)1.150
工作年限>1~2≤11.2290.0053.42(1.45~8.09)1.096
>2~51.799<0.0016.04(2.84~12.85)
>5~101.977<0.0017.22(3.40~15.33)
>101.970<0.0017.17(3.40~15.12)
职业护士医生0.0830.7091.09(0.70~1.68)1.168
医技人员-1.477<0.0010.23(0.12~0.43)
行政及其他-2.5910.0320.07(0.01~0.80)
职称中级初级0.1360.5401.15(0.74~1.77)1.207
高级0.1110.7551.12(0.56~2.24)
其他-0.3090.6640.73(0.18~2.95)
是否为医院中层以上干部-0.5290.1530.59(0.28~1.22)1.137
定义的知晓情况了解部分不知道0.0390.9801.04(0.05~22.88)1.446
了解掌握0.0170.9911.02(0.04~23.41)
熟练掌握0.6300.6991.88(0.08~45.60)
分类的知晓情况了解部分不知道-0.2540.7670.78(0.15~4.15)1.555
了解掌握-0.0750.9340.93(0.16~5.46)
熟练掌握-0.6960.4710.50(0.08~3.31)
各等级具体内容的知晓情况了解部分不知道0.5140.4701.67(0.42~6.73)1.674
了解掌握0.7350.3432.08(0.46~9.15)
熟练掌握-0.3300.7040.72(0.13~3.95)
报告原则的知晓情况了解部分不知道0.5940.5931.81(0.21~15.94)1.916
了解掌握0.7450.5132.11(0.23~19.68)
熟练掌握1.4860.2294.42(0.39~49.70)
本院报告流程的知晓情况了解部分不知道-0.2460.8030.78(0.11~5.38)1.743
了解掌握-0.0510.9600.95(0.13~6.99)
熟练掌握0.5700.6001.77(0.21~14.88)
不良事件上报程序繁琐程度是否会增加额外的工作不赞同非常不赞同0.7750.0042.17(1.29~3.66)1.130
不确定0.6190.0761.86(0.94~3.68)
赞同0.9530.0072.59(1.30~5.16)
非常赞同1.7710.0035.88(1.84~18.78)
上报他人不良事件是否会影响同事间的关系不赞同非常不赞同-0.3920.1600.68(0.39~1.17)1.127
不确定-0.5940.0700.55(0.29~1.05)
赞同-0.9320.0230.39(0.18~0.88)
非常赞同-0.1890.7640.83(0.24~2.84)
领导是否鼓励上报-1.8750.0400.15(0.03~0.92)1.073
不明确-0.1410.7840.87(0.32~2.39)
发生不良事件后,科室领导是否会组织讨论0.5220.7041.69(0.11~25.00)1.111
不明确-1.1450.0240.32(0.12~0.86)
科室有无管理不良事件的人员-0.0260.9310.97(0.54~1.75)1.074
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基于logistic回归与决策树模型的医务人员主动上报医疗不良事件行为影响因素分析
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陈昌阳 1 , 陈太好 2 , 张蔓娜 2 , 刘韦 2 , 万亿 2 , 龙海 2 , 汪俊华 1 , 张江萍 1, 2
现代预防医学 | 卫生政策与管理 2025,52(16): 2986-2993
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现代预防医学 | 卫生政策与管理 2025, 52(16): 2986-2993
基于logistic回归与决策树模型的医务人员主动上报医疗不良事件行为影响因素分析
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陈昌阳1, 陈太好2, 张蔓娜2, 刘韦2, 万亿2, 龙海2, 汪俊华1, 张江萍1, 2
作者信息
  • 1.贵州医科大学公共卫生与健康学院,环境污染与疾病监控教育部重点实验室,贵州 贵阳 561113
  • 2.贵阳市公共卫生救治中心
  • 陈昌阳(2000—),女,硕士在读,研究方向:疾病预防与控制

通讯作者:

张江萍,E-mail:
Analysis of influencing factors of medical staff’s voluntary reporting of medical adverse events based on logistic regression and decision tree models
Chang-yang CHEN1, Tai-hao CHEN2, Man-na ZHANG2, Wei LIU2, Yi WAN2, Hai LONG2, Jun-hua WANG1, Jiang-ping ZHANG1, 2
Affiliations
  • School of Public Health, the key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, Guizhou 561113, China
出版时间: 2025-08-25 doi: 10.20043/j.cnki.MPM.202501182
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目的

采用logistic回归和决策树模型分别探讨医务人员主动上报医疗不良事件的影响因素并提供对应的解决措施。

方法

采用单纯随机抽样,对某三级医院811名医务人员进行调查。采用logistic回归与决策树模型对医务人员医疗不良事件主动上报的因素进行分析,并计算ROC曲线下面积以比较判断两模型分析效果。

结果

该院仅有55.1%的医务人员主动上报过医疗不良事件。两模型结果显示职业、工作年限、对本院报告流程的知晓情况、上报程序繁琐程度是否会增加额外工作是影响医务人员主动上报的影响因素(P<0.05);Logistic回归模型AUC大于决策树模型,差异具有统计学意义(Z=3.424,P<0.001)。

结论

该院医务人员医疗不良事件主动上报率较低,建议采取多种措施以促进医务人员主动上报。

医疗安全不良事件  /  医疗不良事件  /  影响因素
Objective

To explore the influencing factors of medical staff’s active reporting of adverse medical events by using Logistic regression and decision tree models, and to provide corresponding solutions.

Methods

A total of 811 medical workers in a tertiary hospital were investigated by random sampling. Logistic regression and decision tree model were used to analyze the factors of active reporting of medical adverse events by medical staff, and the area under ROC curve was calculated to compare and judge the analysis effect of the two models.

Results

Only 55.1% of the medical staff in this hospital have voluntarily reported medical adverse events. The results of the two models showed that occupation, working years, knowledge of the reporting process of the hospital, and whether additional work would be added to the cumbersome reporting procedures were the influencing factors for the active reporting of medical staff (P<0.05). The AUC of Logistic regression model was greater than that of decision tree model, and the difference was statistically significant (Z=3.424, P<0.001).

Conclusion

The rate of active reporting of medical adverse events by medical staff in this hospital is relatively low. It is suggested that multiple measures be taken to promote the active reporting by medical staff.

Medical safety adverse events  /  Medical adverse events  /  Influencing factors
陈昌阳, 陈太好, 张蔓娜, 刘韦, 万亿, 龙海, 汪俊华, 张江萍. 基于logistic回归与决策树模型的医务人员主动上报医疗不良事件行为影响因素分析. 现代预防医学, 2025 , 52 (16) : 2986 -2993 . DOI: 10.20043/j.cnki.MPM.202501182
Chang-yang CHEN, Tai-hao CHEN, Man-na ZHANG, Wei LIU, Yi WAN, Hai LONG, Jun-hua WANG, Jiang-ping ZHANG. Analysis of influencing factors of medical staff’s voluntary reporting of medical adverse events based on logistic regression and decision tree models[J]. Modern Preventive Medicine, 2025 , 52 (16) : 2986 -2993 . DOI: 10.20043/j.cnki.MPM.202501182
据报道平均每件医疗不良事件会增加6~9个住院日,全球每年因医疗不良事件损失170~290亿美元[1]。医疗不良事件不但给患者、医务人员造成了不良影响,还严重损害了患者对医疗机构和医护人员的信任,加剧了医患矛盾[2-3],而每20个患者中至少有1位因可预防的医疗不良事件受到损害[4]。医疗不良事件发生率为7.02%[2],但医务人员主动上报医疗不良事情的情况却不容乐观,我国相关研究表明某二级医院仅有40.15%的医护人员上报过不良事件[5],20%的医务人员完全不报告发生过的医疗不良事件[6]。医务人员及时、主动上报医疗不良事件可以降低不良事件的发生,对于提高医疗质量和改善医患关系具有重要意义[7]。Logistic回归可以通过系数的权重对结果进行解释,并量化因素之间的相关程度与回归拟合程度的高低,但对于复杂和高维的数据集,解释会变得困难且不能提供良好的决策建议。而决策树模型可以自动选择对结果影响最大的特征来进行节点分裂,直观地解释因素间的关系及各因素在因变量中的作用程度和概率,一定程度上解决了logistic回归的局限性[8-10]。基于此,本研究将结合logistic回归与决策树模型对医务人员主动上报医疗不良事件的现状及影响因素进行调查,为规范和提高医务人员主动上报医疗不良事件提供理论依据和建议。
于2023年10月9日—13日,通过单纯随机抽样选取某三级医院2022年1月至调查之日经历过医疗不良事件的医生、护士、医技人员、行政人员等811名研究对象进行调查。共回收问卷811份,其中有效问卷810份,有效应答率约99.9%。此次研究在获得所有调查对象的知情同意下进行。本研究获得贵阳市公共卫生救治中心伦理委员会批准,批准号为(2025)论文第(08)号。
其中各参数取值如下:检验水准取双侧95%,当α=0.05 时,相应的μ=1.96;p=22.5%[11],设计效应deff=1.2;相对误差r=20%,d=20%×22.5%;考虑10%的无应答率,最小样本量为437人。
通过参考文献和质性访谈结果,自行设计调查问卷,内容包括性别、工作年限、职业、对不良事件的定义、分类、各等级具体内容的知晓情况、上报程序繁琐程度是否会增加额外的工作等20个条目。问卷信效度分析结果显示,Cronbach α为0.965、KMO为0.970,表明信效度良好。
严格按照标准选取研究对象并开展预调查;对调查员进行统一培训,现场推送电子问卷;由双人进行问卷复核,答题时被调查者间不必商量,保证问卷答题质量;及时纠正漏项、错项,空项达3处及以上予以剔除。
使用Excel 2023软件建立数据库,用R 4.3.3进行统计分析。计量资料呈正态分布时采用()表示,偏态分布时用中位数(四分位数间距)表示;计数资料采用率或构成比表示,组间比较采用χ2检验或Fisher确切概率法,将单因素分析中有统计学差异的因素纳入二元非条件logistic回归和决策树模型,分析影响医务人员主动上报的因素,两模型比较采用ROC曲线,检验水准α=0.05。
本次调查,男性占24.8%,女性占75.2%;医生占34.8%,护士占50.9%,医技人员占12.2%,行政及其他人员占2.1%;专科及以下学历占18.0%,本科学历占74.1%,硕士及以上学历占7.9%;工作年限1年以下占8.8%,1~2年占7.4%,2~5年占16.9%,5~10年占18.9%,10年以上占48.0%;初级职称占51.0%,中级职称占34.7%,高级职称占11.1%,其他占3.2%;中层及以上干部占7.7%。
结果显示,医务人员主动上报医疗不良事件行为在性别、工作年限、职业、职称、是否为中层以上干部、对不良事件的定义、分类、各等级的具体内容、报告原则的知晓情况等15个指标间差异具有统计学意义(P<0.05),详见表1
将单因素分析中有统计学差异的变量纳入回归模型。工作年限、职业、上报程序繁琐程度是否会增加额外的工作、上报他人不良事件是否会影响同事间的关系、领导是否鼓励上报、发生不良事件时,科室领导是否会组织讨论是医务人员主动上报医疗不良事件行为的独立影响因素,详见表2。经Hosmer检验模型拟合优度较高,χ2值=10.125,P=0.256,可认为该模型拟合程度较好。
将单因素分析中有统计学意义的自变量纳入模型进行CHAID决策树分析。结果显示决策树模型分为5层,11个节点和4个预测因子,其中职业是医疗不良事件主动上报最重要的影响因素,变量重要性为36.99,详见图1。当医生及护士的工作年限在1年及以上,且能熟练掌握、了解掌握本院不良事件报告流程则不良事件主动上报率为65%;而当医生及护士的工作年限在1年以内时,未主动上报不良事件的概率为8%;当上报者为医技、行政及其他人员时,未主动上报不良事件的概率为14%,详见图2
两种模型的分析结果均显示,职业、工作年限、上报程序繁琐程度是否会增加额外工作是医务人员主动上报医疗不良事件行为的影响因素。分别对两模型绘制ROC曲线,其中logistic回归模型AUC为0.789(95%CI:0.758~0.820),分类决策树模型的AUC为0.712(95%CI:0.681~0.744),两模型的AUC在0.7~0.9之间说明模型具有一定准确性,使用Z检验[12]对两模型进行比较,结果显示差异具有统计学意义(Z=3.424,P<0.001)可认为logistic回归较决策树模型的效果更好,详见图3
医疗不良事件是医疗质量的重要体现,促进上报医疗不良事件,可以在一定程度上避免医疗纠纷的发生。本研究显示该院有55.1%的医务人员主动上报过医疗不良事件,高于谢舒(48.0%)[13]、蒋婷婷(22.5%)[11]等人的研究结果,可能与样本的选择、调查工具,以及各医院的上报政策的差异有关。提示我们,医疗不良事件的上报工作仍面临严峻挑战,还需要全院职工的共同努力。
决策树模型提示,职业是影响医务人员主动上报的关键因素。医技、行政及其他人员由于相关医学背景的缺失很多情况下无法判定不良事件[14],往往会阻碍上报行为,因此,我们可以将既往发生的不良事件形成案例,加强非临床科室人员的学习以增强其安全意识和专业素养,提高识别不良事件的敏锐度[15];同时,应定期开展医疗安全培训,尤其是对事件上报标准、上报流程和应对措施进行培训,确保每个人员都具备必要的知识和能力来识别、报告和处理安全不良事件。
结合决策树和logistic回归结果,工作年限作为影响医务人员主动上报的因素,在医护人员中发挥着明显作用。一方面考虑医务人员工作年限越长,更具有风险意识和全局意识,对医疗不良事件的认知水平越高[16];另一方面随着工作年限的增加,工作经验不断积累,能敏锐地发现医疗不良事件,因此表现出随着工作年限的增加,医务人员在发生不良事件后会更倾向于主动上报。故可以由1位资质年长的人员带领1~2位新人形成“对子”的方式,由老员工对医疗不良事件进行初步识别分析后带领新员工上报以提高上报参与率。
与logistic回归不同,决策树提示越熟练掌握本院报告流程的职工越能主动上报医疗不良事件,因此可以制作上报系统操作指南手册,分发给职工学习,从而促使职工熟练掌握上报流程。值得注意的是与其他研究不同[17],两个模型均显示越赞同上报程序繁琐,会增加额外工作的医务人员更愿意上报,一方面可能是规范化和制度化的程序使得员工感受到更加明确和透明的指导,另一方面,繁琐的系统可能反映了医院对医疗不良事件管理的重视,增强了员工的责任感,从而提高了人员上报的积极性。良好的不良事件报告系统不仅能改进医疗质量,而且可以促进医务人员主动报告行为[18-19],因此可以公开征集全院职工对现有上报系统的建议,对系统进行优化;同时形成PC端和手机移动端多渠道上报的形式,从而提高员工的积极性。
与谭智、董晓飞[20-21]研究一致,很多医务人员认为医疗不良事件的发生与自身没有关系时,上报他人的不良事件往往会影响同事间的关系,因此不愿主动上报。针对这种现象,首先可以采取匿名上报的方式,减轻上报他人医疗不良事件的思想负担,提升医务人员的上报积极性;其次帮助员工树立上报他人的医疗不良事件是为了纠正不安全因素、避免再次发生而非举报、告状的正确观念;最后不得将上报他人的医疗不良事件作为自身业绩考核、评优评选的标准,避免形成恶意上报的不良竞争关系。
医疗不良事件处置不当往往会对医务人员的自身安全带来潜在风险,甚至引发医疗纠纷或医疗事故[22],因此大多数医务人员担心受到上级的责备和处罚不愿主动报告。可以通过设立积分机制,对主动上报尤其是能够发现潜在风险并及时上报的员工进行加分,而积分可作为推选最美医护、外派进修学习等机会的评选依据[23],实现把领导的鼓励转化为医务人员主动上报的积极因素。
本研究运用logistic回归和决策树模型互为补充,对医务人员主动上报医疗不良事件行为的影响因素进行了分析,并对影响因素的重要性进行了排序,为促进医务人员主动报告制定合理措施提供了理论依据。但本研究为横断面调查,不能判定主动上报行为和影响因素之间的因果关系,接下来还需扩大样本量、采用前瞻性研究对上述影响因素进行验证。
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doi: 10.20043/j.cnki.MPM.202501182
  • 接收时间:2025-01-10
  • 首发时间:2026-03-18
  • 出版时间:2025-08-25
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  • 收稿日期:2025-01-10
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    1.贵州医科大学公共卫生与健康学院,环境污染与疾病监控教育部重点实验室,贵州 贵阳 561113
    2.贵阳市公共卫生救治中心

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Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
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
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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