AI-Driven Optimization of Intergenerational Community Services: An Empirical Analysis of Elderly Care Communities in Los Angeles

Abstract

This study presents an innovative AI-driven optimization framework for intergenerational community services in Los Angeles elderly care communities. The research addresses the critical challenges of resource allocation and service delivery efficiency through the integration of advanced machine learning algorithms and dynamic optimization techniques. The proposed model incorporates deep learning-based demand prediction, reinforcement learning for resource allocation, and comprehensive service quality evaluation mechanisms. An observational study conducted across 24 community aged care facilities over a 12-month period involving 2,500 service users revealed significant improvements in service delivery. The implementation results show a 42.3% reduction in service time, 91.4% accuracy in demand forecasting, and 38.2% improvement in resource allocation. The model achieved an 88.9% implementation success rate across diverse demographic profiles, with user satisfaction scores increasing by 31.6%. Cross-validation results confirm the model's robustness and adaptability across different community settings. This research contributes to the advancement of AI applications in social work by creating a large framework that effectively connects different services while maintaining standards. Good service. These findings provide valuable insights for policymakers and service providers in developing sustainable, technology-based solutions for senior community services.