Entrustable Professional Activities (EPAs) are increasingly adopted in competency-based medical education to structure clinical training around observable, assessable tasks. However, in many institutions, EPA evaluation remains operationally fragmented, frequently relying on paper-based workflows or loosely integrated digital tools that fail to support longitudinal competency analytics. This work presents a data-centric digital infrastructure designed to operationalize EPA-based assessment at institutional scale. We formalize the EPA model into a structured computational framework that integrates Micro-Entrustable Competencies (MECs), autonomy levels (N1–N3), tutor feedback loops, and cross-curricular progression into a unified persistence model. The system supports multi-year academic continuity, structured reevaluation mechanisms, and role-based evaluation workflows while enabling longitudinal tracking of competency acquisition. A unified data model links evaluations to competency dimensions, enabling fine-grained analytics such as autonomy evolution curves, feedback density metrics, and cross-cohort performance comparison. The platform was deployed within a newly established medical program, replacing a paper-based evaluation notebook. Usability validation and load testing demonstrate feasibility, scalability, and high user acceptance in real academic conditions. Beyond system implementation, this work contributes a formalized digital representation of entrustment logic that enables measurable, data-driven competency monitoring. Our results suggest that structured EPA digitalization can transform competency-based medical education from episodic assessment toward continuous, analytics-enabled professional development. The proposed framework provides a scalable foundation for institutional and cross-institutional EPA data integration.