Research-Led Curriculum Innovation under Medical Engineering Convergence: Redesign and Practice of a Course in Clinical Medical Data Analytics

Abstract

The rapid transition toward data-driven and intelligent healthcare has imposed new competency requirements on medical education, particularly in the integration of clinical reasoning with data analytics and engineering methodologies. Conventional teaching models in clinical data analysis often suffer from a separation between research and instruction, simplified datasets, and limited exposure to real-world clinical complexity. This study reports a research-led teaching reform of the course Clinical Medical Data Analytics, guided by the principle of deep medical–engineering integration. Instead of organizing instruction around isolated analytical techniques, the course was redesigned around authentic clinical research problems derived from ongoing projects on sepsis-related complication prediction. Real-world intensive care databases, such as MIMIC-IV, and advanced analytical approaches—including dynamic early warning modeling and model interpretability techniques—were systematically embedded into course modules and project-based learning tasks. A progressive instructional framework was developed, encompassing clinical problem formulation, medical data governance, intelligent modeling, and clinical value assessment. Teaching effectiveness was evaluated through learning outcomes, student feedback, teaching supervision, and research spillover effects. Over two academic years, the reformed course demonstrated significant improvements in student engagement, interdisciplinary competence, and research-oriented learning outcomes. The proposed model illustrates a sustainable "research–teaching symbiosis" paradigm and offers a transferable reference for interdisciplinary curriculum development in intelligent medical education.

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