Design and Application of an AI-driven Medical Record Management System

Corresponding author: ZHANGLI, 380063447@qq.com

DOI: 10.12201/bmr.202601.00076

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  Abstract: Purpose/Significance In response to current challenges in medical record management at healthcare institutions, such as complex processes, delayed archiving, low quality control efficiency, high management costs, and insufficient data utilization, an AI-powered medical record management system is proposed to enhance medical quality and service efficiency. Method/Process Leveraging deep learning technologies such as Optical Character Recognition, Natural Language Processing, and Convolutional Neural Networks, the system integrates core modules including data collection and preprocessing, model-assisted classification, quality control and review, secure archiving, and intelligent applications. It supports multiple functions such as medical record borrowing and tracking, statistical queries, intelligent reporting, and research assistance. Result/Conclusion After the system was launched and operated stably, the 3-day archiving efficiency increased from the original 72.6% to 96.9%, and the medical record quality control coverage rate rose from approximately 50% to 100%. The system has effectively restructured the medical record management model, streamlined operational processes, and provided robust support for improving medical quality and service efficiency.

  Key words: artificial intelligence; medical record management system; natural language processing; deep learning

  Submit time: 27 January 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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  1	2025-09-24	10.12201/bmr.202601.00076V1	Download

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