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Article(id=1324044839305740781, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324044701648695723, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202512372, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1766332800000, receivedDateStr=2025-12-22, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793606489253, onlineDateStr=2026-11-02, pubDate=1779638400000, pubDateStr=2026-05-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793606489253, onlineIssueDateStr=2026-11-02, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793606489253, creator=13701087609, updateTime=1793606489253, updator=13701087609, issue=Issue{id=1324044701648695723, tenantId=1146029695717560320, journalId=1227665162245664772, year='2026', volume='53', issue='10', pageStart='1729', pageEnd='1920', issueExtLink='null', onlineDate='null', pubDate='1779638400000', pubDateStr='2026-05-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793606456434, creator='13701087609', updateTime=1793670074552, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1324311535425815306, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324044701648695723, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1324311535425815307, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324044701648695723, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1855, endPage=1860, ext={EN=ArticleExt(id=1324044840127824366, articleId=1324044839305740781, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Construction of a debt risk early-warning model for public hospitals based on factor analysis: evidence from Sichuan, columnId=1228016567846367388, journalTitle=Modern Preventive Medicine, columnName=Health Policy and Management, runingTitle=null, highlight=null, articleAbstract=
Objective

In response to the continuously expanding debt scale and increasingly prominent debt risks of public hospitals in China, this study aims to construct a debt risk evaluation model suitable for public hospitals. This study empirically evaluates debt risk patterns across public hospitals by administrative level, type, and grade, aiming to support targeted risk mitigation policies.

Methods

Using the relevant data from the 2023 financial annual reports of 408 public hospitals under the Health Commission of Sichuan Province as the sample, this study selected 11 indicators highly correlated with debt risk and established a risk early-warning model through factor analysis.

Results

Factor analysis identified four common factors, with a cumulative variance contribution rate of 87.026%, which collectively constitute the core determinants of debt risk. Debt risk distribution exhibited significant variations across hospitals grouped by different dimensions. At the administrative level, prefecture-level hospitals had the highest proportion of high-risk cases, standing at 28.38%. By hospital type, traditional Chinese medicine hospitals recorded the highest share of high-risk instances, reaching 31.16%. By hospital grade, secondary hospitals accounted for the largest proportion of high-risk cases, at 27.38%.

Conclusion

The debt risk model constructed using factor analysis demonstrates good applicability and effectiveness, enabling a scientific assessment of public hospitals’ debt risk levels. Based on the evaluation findings, we recommend a coordinated approach across multiple areas: strengthening planning constraints and government investment, deepening health insurance price reforms and enhancing internal management, establishing a monitoring and early-warning system. This comprehensive strategy aims to resolve existing debt in a tiered and categorized manner and prevent systemic risks.

, authors=Hong-mei LIU, Ling ZHANG, Sha LI, Xuan-ping ZHOU, Han-jian ZHANG, authorsList=Hong-mei LIU, Ling ZHANG, Sha LI, Xuan-ping ZHOU, Han-jian ZHANG, authorCompany=null, correspAuthors=Ling ZHANG, 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=1324044843051254259, articleId=1324044839305740781, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=基于因子分析的公立医院债务风险预警模型构建——以四川省为例, columnId=1228016568014139551, journalTitle=现代预防医学, columnName=卫生政策与管理, runingTitle=null, highlight=null, articleAbstract=
目的

针对我国公立医院负债规模持续扩大、债务风险日益凸显的问题，本研究旨在构建一个适用于公立医院的债务风险预警模型，对不同层级、类型和等级公立医院的债务风险分布状况进行实证评估，为精准防控债务风险提供决策依据。

方法

以四川省卫生健康委辖下的408家公立医院2023年财务年报相关数据为样本，选择11个与债务风险高度相关指标，通过因子分析法，建立债务风险预警模型。

结果

因子分析萃取出4个公共因子，累计方差贡献率87.026%，共同构成影响债务风险的核心要素。债务风险分布显示，在不同维度的医院分类中，债务风险分布呈现显著差异：从层级看，地市级医院高风险占比最高为28.38%；从类型看，中医医院高风险占比最高达31.16%；从等级看，二级医院高风险占比最高为27.38%。

结论

基于因子分析法构建的债务风险预警模型具有良好的适用性，能系统识别公立医院债务风险的结构化特征。评估发现，四川省公立医院的债务风险在不同类型医院间分布不均衡，尤其集中于地市级医院、中医医院及二级医院。因此，建议从强化规划约束与政府投入、深化医保价格改革与运营管理、构建监测预警体系等方面协同施策，分级分类化解存量债务，防范系统性风险。

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刘洪梅（1987—），女，硕士，高级经济师，研究方向：医院管理，卫生政策

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张玲，E-mail：
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Debt risk indicators of public hospitals

, figureFileSmall=null, figureFileBig=null, tableContent=
指标类型指标编码指标名称计算公式
偿债能力指标X1资产负债率负债总额/资产总额
X2流动比率流动资产/流动负债
X3速动比率（流动资产-存货-预付账款-待摊费用-一年内到期的非流动资产）/流动负债
X4现金比率（货币资金+短期投资）/流动负债
支付能力指标X5支付保障比负债总额/总收入
X6债务依存率负债总额/总费用
X7营业收现率日常活动的现金流入小计/总收入
运营效率指标X8应收账款周转率总收入/（本年应收账款原值+上年应收账款原值）/2
X9应付账款周转率总费用/（本年应付账款原值+上年应付账款原值）/2
X10床位使用率实际占用总床日数/实际开放总床日数
X11医疗盈余率医疗盈余/总收入
), ArticleFig(id=1324044966460273579, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044839305740781, language=CN, label=表1, caption=

公立医院债务风险指标

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指标类型指标编码指标名称计算公式
偿债能力指标X1资产负债率负债总额/资产总额
X2流动比率流动资产/流动负债
X3速动比率（流动资产-存货-预付账款-待摊费用-一年内到期的非流动资产）/流动负债
X4现金比率（货币资金+短期投资）/流动负债
支付能力指标X5支付保障比负债总额/总收入
X6债务依存率负债总额/总费用
X7营业收现率日常活动的现金流入小计/总收入
运营效率指标X8应收账款周转率总收入/（本年应收账款原值+上年应收账款原值）/2
X9应付账款周转率总费用/（本年应付账款原值+上年应付账款原值）/2
X10床位使用率实际占用总床日数/实际开放总床日数
X11医疗盈余率医疗盈余/总收入
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Total variance of common factors

, figureFileSmall=null, figureFileBig=null, tableContent=
公共因子初始特征值提取载荷平方和旋转载荷平方和
总计方差百分比
（%）		累积
（%）		总计方差百分比
（%）		累积
（%）		总计方差百分比
（%）		累积
（%）
Y16.09555.40855.4086.09555.40855.4086.08555.32255.322
Y21.43313.02868.4361.43313.02868.4361.38512.59367.915
Y31.0459.49677.9321.0459.49677.9321.0899.90277.817
Y41.0009.09487.0261.0009.09487.0261.0139.20987.026
), ArticleFig(id=1324044968423207853, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044839305740781, language=CN, label=表2, caption=

公共因子的总方差

, figureFileSmall=null, figureFileBig=null, tableContent=
公共因子初始特征值提取载荷平方和旋转载荷平方和
总计方差百分比
（%）		累积
（%）		总计方差百分比
（%）		累积
（%）		总计方差百分比
（%）		累积
（%）
Y16.09555.40855.4086.09555.40855.4086.08555.32255.322
Y21.43313.02868.4361.43313.02868.4361.38512.59367.915
Y31.0459.49677.9321.0459.49677.9321.0899.90277.817
Y41.0009.09487.0261.0009.09487.0261.0139.20987.026
), ArticleFig(id=1324044968620340142, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044839305740781, language=EN, label=Table 3, caption=

Distribution of debt risk across different tiers of public hospitals

, figureFileSmall=null, figureFileBig=null, tableContent=
医院层级低较低较高高
数量占比
（%）		数量占比
（%）		数量占比
（%）		数量占比
（%）
地市级医院1722.971418.922229.732128.38
区级医院3633.033229.361715.602422.02
省级医院633.33527.78422.22316.67
县级医院5024.154722.716028.995024.15
), ArticleFig(id=1324044968729392047, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044839305740781, language=CN, label=表3, caption=

不同层级公立医院债务风险分布情况

, figureFileSmall=null, figureFileBig=null, tableContent=
医院层级低较低较高高
数量占比
（%）		数量占比
（%）		数量占比
（%）		数量占比
（%）
地市级医院1722.971418.922229.732128.38
区级医院3633.033229.361715.602422.02
省级医院633.33527.78422.22316.67
县级医院5024.154722.716028.995024.15
), ArticleFig(id=1324044968834249648, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044839305740781, language=EN, label=Table 4, caption=

Distribution of debt risk across different types of public hospitals

, figureFileSmall=null, figureFileBig=null, tableContent=
医院类型低较低较高高
数量占比
（%）		数量占比
（%）		数量占比
（%）		数量占比
（%）
中医医院2921.013323.913323.914331.16
专科医院4263.641015.1569.09812.12
综合医院3818.635526.966431.374723.04
), ArticleFig(id=1324044969010410417, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044839305740781, language=CN, label=表4, caption=

不同类型公立医院债务风险分布情况

, figureFileSmall=null, figureFileBig=null, tableContent=
医院类型低较低较高高
数量占比
（%）		数量占比
（%）		数量占比
（%）		数量占比
（%）
中医医院2921.013323.913323.914331.16
专科医院4263.641015.1569.09812.12
综合医院3818.635526.966431.374723.04
), ArticleFig(id=1324044969094296498, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044839305740781, language=EN, label=Table 5, caption=

Distribution of debt risk across different levels of public hospitals

, figureFileSmall=null, figureFileBig=null, tableContent=
医院级别低较低较高高
数量占比
（%）		数量占比
（%）		数量占比
（%）		数量占比
（%）
三级4520.645625.696931.654822.02
二级4929.174124.403219.054627.38
一级及未定级1568.1814.5429.09418.18
), ArticleFig(id=1324044969169793971, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324044839305740781, language=CN, label=表5, caption=

不同级别公立医院债务风险分布情况

, figureFileSmall=null, figureFileBig=null, tableContent=
医院级别低较低较高高
数量占比
（%）		数量占比
（%）		数量占比
（%）		数量占比
（%）
三级4520.645625.696931.654822.02
二级4929.174124.403219.054627.38
一级及未定级1568.1814.5429.09418.18
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基于因子分析的公立医院债务风险预警模型构建——以四川省为例
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刘洪梅 , 张玲 , 李莎 , 周玄平 , 张晗剑
现代预防医学 | 卫生政策与管理 2026,53(10): 1855-1860
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现代预防医学 |卫生政策与管理 2026 , 53 (10) : 1855 -1860
基于因子分析的公立医院债务风险预警模型构建——以四川省为例
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刘洪梅（1987—），女，硕士，高级经济师，研究方向：医院管理，卫生政策

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刘洪梅（1987—），女，硕士，高级经济师，研究方向：医院管理，卫生政策

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刘洪梅, 张玲 , 李莎, 周玄平, 张晗剑
作者信息
  • 四川大学华西公共卫生学院/华西第四医院，四川 成都 610041
通讯作者:
张玲，E-mail：
作者简介:

刘洪梅（1987—），女，硕士，高级经济师，研究方向：医院管理，卫生政策

Construction of a debt risk early-warning model for public hospitals based on factor analysis: evidence from Sichuan
Hong-mei LIU, Ling ZHANG , Sha LI, Xuan-ping ZHOU, Han-jian ZHANG
Affiliations
  • West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
出版时间: 2026-05-25 doi: 10.20043/j.cnki.MPM.202512372
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摘要
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目的

针对我国公立医院负债规模持续扩大、债务风险日益凸显的问题，本研究旨在构建一个适用于公立医院的债务风险预警模型，对不同层级、类型和等级公立医院的债务风险分布状况进行实证评估，为精准防控债务风险提供决策依据。

方法

以四川省卫生健康委辖下的408家公立医院2023年财务年报相关数据为样本，选择11个与债务风险高度相关指标，通过因子分析法，建立债务风险预警模型。

结果

因子分析萃取出4个公共因子，累计方差贡献率87.026%，共同构成影响债务风险的核心要素。债务风险分布显示，在不同维度的医院分类中，债务风险分布呈现显著差异：从层级看，地市级医院高风险占比最高为28.38%；从类型看，中医医院高风险占比最高达31.16%；从等级看，二级医院高风险占比最高为27.38%。

结论

基于因子分析法构建的债务风险预警模型具有良好的适用性，能系统识别公立医院债务风险的结构化特征。评估发现，四川省公立医院的债务风险在不同类型医院间分布不均衡，尤其集中于地市级医院、中医医院及二级医院。因此，建议从强化规划约束与政府投入、深化医保价格改革与运营管理、构建监测预警体系等方面协同施策，分级分类化解存量债务，防范系统性风险。

公立医院  /  债务风险预警模型  /  因子分析  /  四川省
Objective

In response to the continuously expanding debt scale and increasingly prominent debt risks of public hospitals in China, this study aims to construct a debt risk evaluation model suitable for public hospitals. This study empirically evaluates debt risk patterns across public hospitals by administrative level, type, and grade, aiming to support targeted risk mitigation policies.

Methods

Using the relevant data from the 2023 financial annual reports of 408 public hospitals under the Health Commission of Sichuan Province as the sample, this study selected 11 indicators highly correlated with debt risk and established a risk early-warning model through factor analysis.

Results

Factor analysis identified four common factors, with a cumulative variance contribution rate of 87.026%, which collectively constitute the core determinants of debt risk. Debt risk distribution exhibited significant variations across hospitals grouped by different dimensions. At the administrative level, prefecture-level hospitals had the highest proportion of high-risk cases, standing at 28.38%. By hospital type, traditional Chinese medicine hospitals recorded the highest share of high-risk instances, reaching 31.16%. By hospital grade, secondary hospitals accounted for the largest proportion of high-risk cases, at 27.38%.

Conclusion

The debt risk model constructed using factor analysis demonstrates good applicability and effectiveness, enabling a scientific assessment of public hospitals’ debt risk levels. Based on the evaluation findings, we recommend a coordinated approach across multiple areas: strengthening planning constraints and government investment, deepening health insurance price reforms and enhancing internal management, establishing a monitoring and early-warning system. This comprehensive strategy aims to resolve existing debt in a tiered and categorized manner and prevent systemic risks.

Public hospital  /  Debt risk  /  Factor analysis  /  Sichuan Province
刘洪梅, 张玲, 李莎, 周玄平, 张晗剑. 基于因子分析的公立医院债务风险预警模型构建——以四川省为例. 现代预防医学, 2026 , 53 (10) : 1855 -1860 . DOI: 10.20043/j.cnki.MPM.202512372
Hong-mei LIU, Ling ZHANG, Sha LI, Xuan-ping ZHOU, Han-jian ZHANG. Construction of a debt risk early-warning model for public hospitals based on factor analysis: evidence from Sichuan[J]. Modern Preventive Medicine, 2026 , 53 (10) : 1855 -1860 . DOI: 10.20043/j.cnki.MPM.202512372
正文
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公立医院是我国医疗服务体系的核心，也是推进健康中国战略的关键支柱，在基本医疗保障和应对突发公共卫生事件中发挥着不可替代的作用。然而，随着医药卫生体制改革步入深水区，公立医院面临财政投入相对不足、药品耗材加成取消、医保支付方式改革持续推进等多重压力，导致其运营负担加重，负债规模持续扩大，债务风险日益凸显，严重制约了医院的可持续发展和医疗服务能力的提升。《中国卫生健康统计年鉴》数据显示，2009年至2022年，我国政府办公立医院负债总额从3 687亿元激增至21 345亿元，增幅达578.92%，平均资产负债率由32%攀升至46%[1-2]。目前，部分地区公立医院已出现债务累积、收不抵支等财务困境，造成不良社会影响。
针对上述问题，国家层面密集出台了一系列管控政策。财政部、国家卫生健康委等部门联合印发的《关于进一步加强公立医院内部控制建设的指导意见》，明确严禁公立医院无序扩张、举债购置大型医用设备等行为。国家卫生健康委办公厅发布的《关于防范公立医院经济运行风险的通知》，则要求加强债务风险的评估、动态监测与化解工作。2024年8月，国家卫生健康委主任雷海潮在《学习时报》撰文进一步强调，应深化公益性导向的公立医院改革，并逐步化解其长期债务。在此背景下，传统管理手段难以实现对债务风险的精准识别与动态防控，因此，构建一套科学、高效的风险监测与预警机制已成为当前统筹发展与安全的重要课题。
现有债务风险研究主要集中于两方面：一是聚焦区域医院负债规模及单一指标分析，如张冰心等对贵州省公立医院负债状况的研究[3]，以及严莉等对云南省县级公立医院的剖析[4]；二是构建综合性运营指标体系进行实证探索，如赵芬等基于风险视角运用17个指标构建了公立医院资产负债评价体系[5]；郭雨朦等基于层次分析法提出了运用19个指标构建公立医院财务风险评价体系[6]，以及许蓝滢等运用因子分析法构建的包含25个指标的广东省公立医院债务风险模型[7],黎艳娜等运用因子分析法开展深圳市公立医院债务风险评估[8]等，这些研究为全局风险监测提供了有益思路，也提供了有效的方法，但他们的指标体系设计较为复杂，且在兼具指标精简性与实操性的预警模型研究上仍显不足。
本研究旨在侧重关注主要影响债务风险的现金流与流动性，构建一套高效、可操作的债务风险监管工具，为医疗机构主管部门管理决策提供依据，提升债务风险管理的透明性与有效性，助力识别资源配置失衡问题，推动公立医院从规模扩张转向内涵式发展，优化财政资金在学科建设、设备更新等关键领域的投入效率，最终提升医疗卫生服务供给质量。
1　资料与方法
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1.1　资料来源
四川省卫生健康委员会管理包括21个市州行政区域，521家公立医院，本研究以四川省408家公立医院（剔除三个少数民族自治州）2023年的财务年报数据为样本。从医院层级看，包括省级医院18家、地市级医院74家、区级医院109家、县级医院207家；从医院类型看，包括综合医院204家、专科医院66家、中医医院138家；从医院等级看，包括三级医院218家、二级医院168家、一级及未定级医院22家。
1.2　研究方法
通过文献回顾[5-15]、问卷调查及专家咨询，初步构建公立医院债务风险预警指标体系。随后，对全部指标数据进行标准化处理以消除量纲影响，并利用因子分析法进行降维，提取关键公共因子，进而建立债务风险综合评价模型。最后，根据因子方差贡献率计算各家医院的综合得分，据此评估其债务风险水平。数据处理采用SPSS 22.0软件完成。
2　结果
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2.1　公立医院债务风险预警指标体系的构建
本研究在系统梳理债务风险与医院运营管理相关文献的基础上，初步拟定3个一级指标，17个二级指标。随后，面向省市三甲医院财务专家、卫生经济领域学者开展问卷调查，由专家按照指标重要性进行打分，重要性均采用5分制，1分为很不重要，5分为很重要，问卷有效回收率为100%。结合专家打分和意见，聚焦公立医院债务风险因素，以公开规范的财务年报指标为基础，以现金运转情况为核心，按照偿债能力、支付能力、运营效率等因素最终选取了影响公立医院债务风险的11个指标，并确定了各指标的内涵。各指标具体内容见表1。
2.2　因子分析适用性检验
本研究对模型的所有指标数据进行标准化处理，以消除量纲的影响。本研究的样本量为408，指标数为11，样本量是指标量的5倍以上。KMO检验值为0.853，当KMO值接近或大于0.7时，数据被认为适合进行因子分析法。此外，Bartlett球形度检验的结果显示近似卡方值χ2为6 303.759，自由度df为55，P值为0.000，显著小于0.05，表明本研究样本数据适合进行因子分析。
2.3　公共因子萃取根据特征值和累计方差
贡献率的大小，以特征值>1作为标准选取公共因子。本研究选出了4个特征值均>1的因子（Y1~Y4），且累计贡献率达到87.026%，如表2所示。
其中，公共因子Y1主要衡量公立医院短期偿债能力，反映债务流动性管理；Y2衡量公立医院运营绩效与盈利，体现资源利用与财务管理成果；Y3衡量公立医院收款管理效率，强调收款速度对运营的影响；Y4衡量公立医院经营现金效率，衡量经营活动现金回收能力。按照因子分析的原理，上述4个公共因子可以表达原来的11个原始指标中大部分（87.03%）的信息，具有较好的代表性。
2.4　建立公立医院债务风险预警模型
根据成分得分系数矩阵，公立医院公共因子得分结果计算的线性组合模型可以表示为：
2.5　公立医院债务风险实证结果分析
通过回归法计算得到408家医院在四个公共因子的得分。随后，以各公共因子的方差贡献率（如表2旋转后方差百分比所示）作为权重，对这四个因子得分进行加权求和，计算出每家医院的综合得分。综合得分越高，债务风险越低。按照四分位法把公立医院的债务风险分为4个等级：低（综合得分≥0.04）、较低（-0.1≤综合得分<0.04）、较高（-0.21≤综合得分<-0.1）、高（综合得分<-0.21）。不同层级、类型和级别的公立医院债务风险分布情况见表3～表5。
表3显示，从医院层级来看，地市级公立医院债务风险高的医院占比最高；表4显示，从医院类型来看，中医医院债务风险高的医院占比最高；表5显示，从医院等级来看，二级医院中债务风险高的医院占比最高。
3　讨论
收起
3.1　关于医院债务风险预警模型有效性分析
债务风险因子分析模型以财务管理理论为基础，涵盖了现金流动性、营运效率等核心驱动因素。结果显示，模型有效展示了不同层级、类型、级别医院债务风险的显著差异：省级医院风险状况最佳；地市级医院风险更大；中医医院风险最高；专科医院债务风险状况明显优于其他类型；二级医院风险最高；三级医院高风险占比低于二级医院，但“较高风险”占比最高；一级及未定级医院风险较低。模型测算结果与现实相符。
同时，根据结果显示，债务风险最高的10家医院，包括2家综合医院，5家专科医院、3家中医医院，均已上报有运营困难，其风险评分与实际情况和专业判断相符，表明模型较好地评价了公立医院债务风险状况，结果具有较高有效性。
3.2　公立医院的债务风险分布成因分析
研究结果显示，四川省不同层级、类型、等级的公立医院债务风险呈现出一定的规律性，其所揭示的债务风险结构并非局部孤例，而是我国公立医院体系中一个具有普遍性的系统困境。
按层级看，地市级医院高风险占比28.38%，显著高于其他层级。核心原因是功能定位与资源支撑不匹配。地市级医院作为区域医疗中心，承担疑难重症诊疗、教学科研等任务，需建设重点专科、引进高端设备，如PET-CT、手术机器人等，并扩建院区以增强竞争力。这些需巨额投入，但地市级政府财政支持有限，医院多依靠自有资金或专项债，盲目扩张是债务累积的主要推手。
按类型看，中医医院高风险占比31.16%，为三类医院之首。主因是中医医院业务量较小，自身财力有限，且普遍存在“中西医结合”趋势，需购入CT、MRI等大型设备开展精细化诊疗，导致在中医特色和西医设备上均需投入的“双重负担”，加剧债务，产生“弱收入与高投入”矛盾。
按级别看，二级医院高风险占比27.38%，明显高于三级医院的22.02%和一级及未定级的18.18%。二级医院在分级诊疗中承上启下，但定价标准低、财政补助不足、收入有限，患者常“小病在社区，大病去三甲”，使其长期处于夹心层。根据国家卫健委统计信息中心发布数据，2024年1-11月，全国医疗卫生机构总诊疗人次68.8亿，同比增长7.3%。其中，三级医院诊疗人次同比增长10%；一级医院次之，同比增长4%；而二级医院诊疗人次仅比去年同期增长了0.8%[16]，足以可见二级医院的发展难题。
债务风险最低的10家医院主要为精神病专科医院，原因包括：精神卫生领域主要由公立医院承担，政府财政投入支持力度较大；行业准入壁垒高，市场竞争小，主动扩张意愿低；资产投入少，少有扩建或大型设备购置，药品耗材成本占比低；四川省实行“按床日付费”医保预付制，床位使用带来稳定现金流。
3.3　债务风险防范建议
公立医院适度负债有利于医院发展，应允许公立医院存在风险可控的债务，但需加强监管，对超能力债务遵循政府与医院共担原则及时化解，防范系统性风险。
3.3.1　强化规划约束与政府投入，构建源头防控机制
确立区域卫生规划的刚性约束作用，严格执行“无规划不批复、无批复不建设、无资金不启动”原则，从严控制医院规模扩张与设备无序购置。推动困难二级医院向康复、医养结合等领域转型，避免与三级医院同质化竞争。同时，全面落实政府办医责任，确保对公立医院基本建设、设备购置、重点学科发展等六方面投入足额到位，推行“交钥匙”工程杜绝新增项目负债，并通过财政支持人员基本薪酬与固定运营成本，形成长效可持续的补偿机制。
3.3.2　深化医保价格改革与运营管理，提升医院内生动力
建立医疗服务价格动态调整机制，每年评估并反映技术劳务成本与价值。完善医保支付体系，探索对中医医院等机构的差异化支付政策，确保基金及时足额支付、化解历史欠款。医院内部应深化业财融合与精益运营，明确功能定位、发展特色专科，避免同质化竞争。通过优化财务结构、加快应收账款周转、规范医保基金使用，提升自身运营效率与偿债能力，实现从规模扩张向内涵式发展的转变。
3.3.3　构建全周期监测预警体系，强化风险精准研判
构建覆盖“借、用、管、还”全周期的债务监测预警框架，推行“穿透式”监管并建立“预警-管控-化解”一体化机制。基于模型实施分级分类动态管控，对高风险机构重点督导。针对存量债务，按照“一院一策”原则制定差异化化解方案，综合运用财政承担、医保预付、金融展期降息、医院提质增效等多种途径，压实地方政府化债主体责任，并将偿债计划纳入中长期预算，确保债务化解有序推进、风险可控。
基金
收起
  • 四川省卫生健康委员会2024年科技项目公立医院债务风险及预警研究(24RKX10)
  • 四川省2024年卫生健康重大改革创新揭榜攻关重点项目
文献
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2026年第53卷第10期
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doi: 10.20043/j.cnki.MPM.202512372
  • 接收时间：2025-12-22
  • 首发时间：2026-11-02
  • 出版时间：2026-05-25
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  • 收稿日期：2025-12-22
基金
四川省卫生健康委员会2024年科技项目公立医院债务风险及预警研究(24RKX10)
四川省2024年卫生健康重大改革创新揭榜攻关重点项目
作者信息
    四川大学华西公共卫生学院/华西第四医院，四川 成都 610041

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