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Purpose: This study explores the architectural frameworks, technological components, and implementation strategies for an Artificial Intelligence-supported smart healthcare-elderly care integration platform addressing the challenges of aging populations, focusing on data architecture standardization, system interoperability, and ethical considerations in AI deployment for elderly care. Methodology: The research employs a microservice-based distributed architectural pattern integrating AI technologies through four system layers. The implementation incorporates a hybrid data model combining relational and non-relational database technologies with international medical standards. Evaluation utilizes a six-dimensional indicator system measuring technical performance, functional effectiveness, user satisfaction, service quality, economic benefits, and social impact during a 12-month pilot implementation. Findings: The platform demonstrated exceptional technical performance (99.95% availability, 99.3% data accuracy) while preventing 430 high-risk events. Care plan compliance improved by 35%, depression scale scores decreased by 21.6%, and user satisfaction exceeded 89% across all stakeholder groups. The system reduced avoidable hospitalizations by 23.7% and emergency visits by 5.2 per 100 elderly annually while improving resource utilization by 28.6%. Conclusion: The integration platform effectively addresses multifaceted challenges of elderly care coordination through AI-enhanced microservice architecture, though limitations regarding small-scale implementation and generalizability require further investigation. Practical Implications: The platform offers substantial improvements in risk detection, personalized care planning, and resource optimization, providing a sustainable model for healthcare-elderly care integration responsive to demographic transitions while enhancing both service quality and operational efficiency.
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Yujie Hu (2025) studied this question.
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