Dynamic Closed-Loop Medical Record Quality Management Using an AI-Driven Multilevel Quality Control System: Development and Implementation Study

BACKGROUND: As the core documentation of the clinical diagnosis and treatment process, the quality of medical records is directly related to medical safety and efficiency. However, traditional manual quality control (QC) has limitations such as limited coverage, low efficiency, and inconsistent standards, making it difficult to meet the high standards of modern hospitals. OBJECTIVE: The purpose of this study was to develop a medical record QC platform based on AI technology and to build a multilevel QC system relying on the platform to comprehensively improve the form and content of electronic medical records (EMRs). METHODS: This study established a QC rule database based on relevant medical record writing standards, combined deep learning and natural language processing technologies to build an AI-driven QC engine, designed a 4-level QC and management process, and implemented the system in a comprehensive tertiary hospital in China. RESULTS: After applying AI for the first level of QC, the coverage rate of automated initial screening of EMRs reached 100%. On this basis, combined with manual participation in the second to fourth level of QC, a new model of medical record quality management was formed, featuring human-machine collaboration, interaction between QC units and clinical departments, efficient closed-loop operation, and equal emphasis on form and connotation. The completeness, timeliness, consistency, and accuracy of EMRs significantly improved, while various indicators, such as the grade A rate and excellent rate, continued to increase (all P<.001). CONCLUSIONS: This study demonstrated that the implementation of an AI-based 4-level QC management model helped overcome the limitations of traditional manual QC methods to a certain extent, facilitated comprehensive, dynamic, and closed-loop management of EMRs, and effectively improved the overall quality of medical records. This model can provide a reference for other hospitals seeking to reform their QC models.