Statistical competence of clinicians in the context of emerging medical education and digital-intelligent medicine: composition and educational implications

In the context of emerging medical education, statistical competence has become an essential core competence for clinicians engaged in evidence-based decision-making and medical research. With the increasing adoption of data-driven medical paradigms such as real-world evidence, multivariable prediction models, and AI-assisted clinical decision-support systems, clinicians are required not only to understand statistical analysis results but also to assess model applicability, generalizability, and potential sources of bias in clinical practice. Drawing on available literature, policy directions, and teaching practices, this study theoretically re-conceptualizes the statistical competence required for clinicians in the current healthcare environments. We propose a three-tier competence framework consisting of statistical literacy, statistical competence, and data reasoning competence, and further delineate six corresponding competence domains. Using this integrated competence framework, this study explores potential pedagogical mappings in curriculum design, contextualized teaching strategies, and assessment approaches aligned with competence-oriented learning outcomes. This framework is intended to provide a conceptual basis for redefining the training objectives of clinicians′ statistical competence in the context of emerging medical education.