AI赋能社区老年人慢性病管理的模式比较与优化路径

With the acceleration of population aging, community-based chronic disease management has become a key focus in public health governance. Traditional models primarily rely on periodic follow-ups and passive interventions, resulting in delayed monitoring, fragmented management, and difficulties in meeting the needs of multimorbidity, longterm management, and continuous surveillance. Artificial intelligence(AI) technology offers new solutions for chronic disease management through intelligent monitoring devices, health management platforms, and smart follow-up systems, enabling proactive early warnings and sustained interventions. This study examines three case studies and finds that AI significantly improves real-time chronic disease monitoring, facilitates information sharing, and enhances diagnostic and treatment efficiency. However, challenges persist, including insufficient aging adaptability, data silos, a lack of multi-stakeholder collaborative governance, and privacy and security risks. To address these issues, this paper proposes: advancing system aging adaptability and data integration, establishing a collaborative governance mechanism involving communities, medical institutions, and platform enterprises, strengthening data security and ethical oversight, and improving older adults' digital health literacy and technology acceptance. This research provides a practical foundation for intelligent community health management models.