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[1]刘金婷常诚林宝禄刘嘉.三级医院评审数据核查机制与系统性错误成因研究[J].中国卫生质量管理,2026,33(1):28-32.[doi:10.13912/j.cnki.chqm.2026.33.1.07]
　LIU Jinting,CHANG Cheng,LIN Baolu.Research on the Verification Mechanism of Tertiary Hospital Accreditation Data and the Causes of Systematic Errors[J].Chinese Health Quality Management,2026,33(1):28-32.[doi:10.13912/j.cnki.chqm.2026.33.1.07]

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三级医院评审数据核查机制与系统性错误成因研究

《中国卫生质量管理》[ISSN:1006-7515/CN:CN 61-1283/R] 卷: 33 期数: 2026年1期页码: 28-32 栏目: 医疗质量出版日期: 2026-01-15

Title:: Research on the Verification Mechanism of Tertiary Hospital Accreditation Data and the Causes of Systematic Errors

作者:: 刘金婷常诚林宝禄刘嘉; 天津市医疗服务评价和指导中心

Author(s):: LIU Jinting; CHANG Cheng; LIN Baolu; Tianjin Medical Service Evaluation and Guidance Center

关键词:: 医院评审; 数据核查; 系统性错误; 瑞士奶酪模型; 数据质量管理

Keywords:: Hospital Accreditation; Data Verification; Systematic Errors; Swiss Cheese Model; Data Quality Management

分类号:: R197.323

DOI:: 10.13912/j.cnki.chqm.2026.33.1.07

文献标志码:: A

摘要:: 目的系统解析三级医院评审数据核查的实施路径，探究评审数据系统性错误的成因，为完善医院数据质量管理提供理论依据与实证支持。方法采用多中心回顾性研究设计，纳入天津市31所三级医院2019-2022年多源异构医疗数据，通过分层随机抽样与多维度交叉验证，结合瑞士奶酪模型解构错误链，系统分析错误数据的分布特征、类别构成及成因机制等。结果错误数据占比均值为1.54%,95%CI为(1.30%,1.70%］，其中死亡类指标错误率（35.63%）最高，主要成因包括指标定义模糊（前端控制失效）、数据提取逻辑偏差（过程监控失效）及多系统数据孤岛（验证机制失效）等。惩罚性扣分后评审分数显著下降（P＜0.001），且错误数据占比与评审分数呈负相关（R=-0.481，P=0.006）。系统性错误表现为多层级防御失效引发的“多米诺效应”，隐性数据治理问题构成“冰山模型”底层风险。结论医院评审数据错误具有系统性特征，亟须构建技术链与管理链协同演进的“双螺旋”管理模型，并建立“反脆弱性”机制以提升系统韧性。建议加强指标定义标准化培训，推进区域医疗数据平台整合，实施全流程闭环质量管理。

Abstract:: ObjectiveTo systematically analyze the implementation pathway of data verification in tertiary hospital accreditation, explore the causes of systematic errors in accreditation data, and provide theoretical evidence and empirical support for improving hospital data quality management.MethodsA multi-center retrospective study design was adopted, incorporating multi-source heterogeneous healthcare data from 31 tertiary hospitals in Tianjin from 2019 to 2022. Through stratified random sampling and multi-dimensional cross-validation, combined with the Swiss Cheese Model to dissect error chains, the distribution characteristics, category composition, and causal mechanisms of erroneous data were systematically analyzed.ResultsThe mean proportion of erroneous data was 1.54%,95% confidence interval (1.30%,1.70%］, with the highest error rate observed in mortality-related indicators (35.63%). The primary causes included ambiguous indicator definitions (failure of front-end control), deviations in data extraction logic (failure of process monitoring), and isolated data systems (failure of validation mechanisms). Following punitive point deductions, accreditation scores decreased significantly (P<0.001), and a negative correlation was identified between the proportion of erroneous data and accreditation scores (R=-0.481, P=0.006). Systematic errors manifested as a "domino effect" triggered by multi-level defense failures, with latent data governance issues constituting underlying risks in the "iceberg model".Conclusion Errors in hospital accreditation data exhibit systemic characteristics, necessitating the construction of a "double helix" management model that synergistically evolves both the technology chain and management chain, along with the establishment of an "anti-fragility" mechanism to enhance system resilience. It is recommended to strengthen standardized training on indicator definitions, promote the integration of regional healthcare data platforms, and implement closed-loop quality management throughout the entire process.

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更新日期/Last Update: 2026-01-15