Research on Intelligent Recognition and Intervention Strategies for Quality Control Nodes in the Full Process of Electronic Medical Records from a Data Chain Perspective

Corresponding author: Huang Huai Yu, huanghuaiyu@mzrmyy.com

DOI: 10.12201/bmr.202610.00045

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  Abstract: 〔〕 Purpose/Significance To address issues such as data chain disruptions and inadequate node management in the comprehensive quality control of electronic medical records (EMRs), this study proposes intelligent recognition and intervention strategies to enhance both medical data quality and patient safety management standards. Method/Process Based on the Data Chain Theory, a quality control analysis framework for electronic medical records was constructed. Utilizing literature review, expert consultation, and case analysis methods, key quality control nodes were identified, and intelligent recognition rules were established. Natural language processing and machine learning technologies were employed to conduct real-time monitoring and risk alerts regarding the completeness of medical record documentation, logical consistency, and diagnostic and treatment standardization. A hierarchical intervention strategy was developed to achieve progressive quality control management, ranging from automatic reminders and intelligent corrections to manual review. Result/Conclusion This method effectively enhances the coverage rate of medical record quality control and the defect detection rate, shortens the quality control feedback cycle, and provides a feasible solution for achieving comprehensive, intelligent, and refined quality management throughout the entire electronic medical record process.

  Key words: Electronic Medical Record; Data Chain; Full-Process Quality Control; Intelligent Recognition; Intervention Strategy; Artificial Intelligence

  Submit time: 19 October 2026

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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• ID	Submit time	Number	Download
  1	2026-06-11	10.12201/bmr.202610.00045V1	Download

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