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Objectives: This study aims to address the critical challenges of data integrity, accuracy, consistency, and precision in the application of electronic medical record (EMR) data within the healthcare sector, particularly within the context of Chinese medical information data management. The research seeks to propose a solution in the form of a medical metadata governance framework that is efficient and suitable for clinical research and transformation. Methods: The article begins by outlining the background of medical information data management and reviews the advancements in artificial intelligence (AI) technology relevant to the field. It then introduces the “Service, Patient, Regression, base/Away, Yeast” (SPRAY)-type AI application as a case study to illustrate the potential of AI in EMR data management. Results: The research identifies the scarcity of scientific research on the transformation of EMR data in Chinese hospitals and proposes a medical metadata governance framework as a solution. This framework is designed to achieve scientific governance of clinical data by integrating metadata management and master data management, grounded in clinical practices, medical disciplines, and scientific exploration. Furthermore, it incorporates an information privacy security architecture to ensure data protection. Conclusion: The proposed medical metadata governance framework, supported by AI technology, offers a structured approach to managing and transforming EMR data into valuable scientific research outcomes. This framework provides guidance for the identification, cleaning, mining, and deep application of EMR data, thereby addressing the bottlenecks currently faced in the healthcare scenario and paving the way for more effective clinical research and data-driven decision-making.
Lu et al. (Mon,) studied this question.
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