Conceptualization and pathways of the medical education governance digital twin in the context of precision education

In the context of the ongoing advancement of precision medical education, governance in medical education within teaching hospitals has gradually shifted from traditional experience-based management to data-supported management. However, current approaches still largely remain at the stages of educational evidence-chain construction, profiling analysis, and risk prediction, with relatively limited capacity to support intervention consequence simulation, governance strategy comparison, and system-level optimization. Against this background, the concept of the medical education governance digital twin (MEGDT) is proposed. By integrating digital twin theory, data governance practices in teaching hospitals, and international frontier cases, this paper discusses the conceptual connotation, implementation framework, and governance value of MEGDT. The potential value of MEGDT lies not only in enhancing the dynamic perception, simulation, and feedback capabilities of medical education governance, but also in providing decision support for teaching hospitals to achieve a better balance among educational effectiveness, resource input, organizational efficiency, and educational equity. At present, MEGDT remains at the stage of conceptual proposal and pathway exploration. Future work should prioritize minimum viable prototype studies in scenarios such as postgraduate medical education, while also addressing data quality, model credibility, privacy and security, and cost-effectiveness balance.

Key Words: precision medical education; digital twin; medical education governance; evidence-informed decision-making