Construction of Smart Health Education Prescriptions Driven by Knowledge, Data, and Models

This study focuses on the core needs of primary healthcare and establishes an smart health education prescription based on a "data-knowledge-business" interactive model. It addresses the lack of interpretability in existing clinical reasoning models. Knowledge-driven approaches provide a solid foundation of medical and health knowledge for these prescriptions, allowing the content to be formulated based on the latest medical research findings and clinical practice guidelines, ensuring scientific rigor and authority. Data-driven approaches leverage big data technologies to comprehensively collect multimodal health data from individuals, such as medical information, lifestyle data, and disease monitoring data within health records. By conducting in-depth analyses of this data, we can identify individual health characteristics and potential risks, providing substantial support for the development of personalized health education prescriptions. Model-driven approaches employ various advanced algorithmic models to analyze the collected data, enabling precise predictions of disease risk and accurate identification of health needs. This process further optimizes the content and format of health education prescriptions, enhancing their effectiveness and practicality. The Knowledge Base System for Health Education Prescriptions has been piloted in the Dongcheng District of Beijing, and the result shows that it has a significant improvement on patient behavior and patient health.