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温煜, 李雄, 曾菲菲, 等. 基于人工智能的病历质控系统的应用研究[J]. koko体育app 学报（医学版）, 2023, 54(6): 1263-1268. DOI:
引用本文: 温煜, 李雄, 曾菲菲, 等. 基于人工智能的病历质控系统的应用研究[J]. koko体育app 学报（医学版）, 2023, 54(6): 1263-1268. DOI:
WEN Yu, LI Xiong, ZENG Feifei, et al. Application of Medical Record Quality Control System Based on Artificial Intelligence[J]. Journal of Sichuan University (Medical Sciences), 2023, 54(6): 1263-1268. DOI:
Citation: ♏ WEN Yu, LI Xiong, ZENG Feifei, et al. Application of Medical Record Quality Control System Based on Artificial Intelligence[J]. Journal of Sichuan University (Medical Sciences), 2023, 54(6): 1263-1268. DOI:

基于人工智能的病历质控系统的应用研究

Application of Medical Record Quality Control System Based on Artificial Intelligence

  • 摘要:
      目的   通过人工智能技术探索自动化病历质控方法，规范病历书写流程，解决人工质控弊端。
      方法   本文构建了基于人工智能的病历质控系统，该系统首先依据权威标准和专家意见设计并构建质控规则库，通过数据采集引擎自动采集病历数据，然后通过后结构化引擎转换为结构化数据，最后由病历质控引擎结合规则库分析数据，进行质量问题判定，实现自动化智能质控。将该系统应用于病历质控，选取现病史雷同、主诉描述缺陷、初步诊断不全、月经婚育史缺失、主诉现病史不匹配5个质控点，随机抽取2022年1月的2 918份出院病历进行人工智能质控，然后组织病历质控专家进行正确性复核，并对比既往人工质控记录，分析结果。以复核正确的问题数作为金标准，对5个质控点进行受试者工作特征（ROC）曲线分析。
      结果  根据病历质控专家复核，人工智能质控正确率达到89.57%。通过对比抽样病历的人工智能质控和既往人工质控结果，抽样病历既往人工质控检出问题中仅有1个在人工智能质控系统中未检出，人工智能质控正确检出病历质量问题的数量约为人工质控的2.97倍。ROC曲线分析示，人工智能质控组的5个质控点AUC值均有统计学意义（P<0.05），且AUC值均接近或大于0.9，而人工质控组仅“现病史雷同”质控点AUC值（0.797）有统计学意义（P<0.05）；组间AUC值比较示，人工智能质控组在5个质控点上比人工质控更具有优势。
      结论   通过基于人工智能的病历质控系统的应用，能够实现高效的病历文书全量质控，有效提高质量问题检出率，有助于节约人力，提升病历书写质量。
     
    Abstract:
      Objective  In this study, we used artificial intelligence (AI) technology to explore for automated medical record quality control methods, standardize the process for medical record documentation, and deal with the drawbacks of manually implemented quality control.
      Methods  In this study, we constructed a medical record quality control system based on AI. We first designed and built, for the system, a quality control rule base based on authoritative standards and expert opinions. Then, medical records data were automatically collected through a data acquisition engine and were converted into structured data through a post-structured engine. Finally, the medical record quality control engine was combined with the rule base to analyze the data, identify quality problems, and realize automated intelligent quality control. This system was applied to the quality control of medical records and five quality control points were selected, including similarities in the history of the present illness, defects in the description of chief complaints, incomplete initial diagnosis, missing in formation in the history of menstruation, marriage, and childbirth, and mismatch between the chief complaints and the history of the present illness. We randomly selected 2 918 medical records of patients discharged in January 2022 to conduct AI quality control. Then we organized medical record quality control experts to conduct an accuracy review, made a comparison with previous manual quality control records, and analyzed the results. The number of quality problems that were verified in the accuracy review was taken as the gold standard and receiver operating characteristic (ROC) curves were drawn for the 5 quality control points.
      Results   According to the accuracy review performed by medical record quality control experts, the accuracy of AI quality control reached 89.57%. For the sampled medical records, the results of AI quality control were compared with those of previous manually performed quality control and only one problem detected by manual quality control of the sampled medical records was not detected by the AI quality control system. The number of medical record quality problems correctly detected by AI quality control was about 2.97 times that of manual quality control. Analysis of the ROC curves showed that the AUC of the five quality control points of the AI quality control system were statistically significant (P<0.05) and all the AUC values approximated or exceeded 0.9. In contrast, results obtained through manually performed quality control found significant AUC (0.797) for only one quality control point—similarities in the history of present illness (P<0.05). Comparison of the AUC values of the two quality control methods showed that AI quality control system had an advantage over manually performed quality control for the five quality control points.
      Conclusion  Through the application of medical record quality control system based on AI, efficient full quality control of medical record documentation can be achieved and the detection rate of quality problems can be effectively improved. In addition, the system helps save manpower and improve the quality of medical record documentation.
     
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