The ongoing expansion of China’s long-term care insurance (LTCI) system, characterized by diversified services, complex stakeholders, and extended supervision chains, has exposed the limitations of traditional manual supervision, including inefficiency, inadequate coverage, and heightened moral hazard. Intelligent technologies, represented by artificial intelligence (AI), offer a novel approach to enhancing LTCI supervision efficacy. Based on the investigation of the intelligent supervision practices in Hangzhou City, this study systematically identifies key dilemmas in areas such as information silos, fragmented processes, lack of procedural visibility, and blurred responsibility boundaries. It analyzes the practical applications and shortcomings of the smart medical insurance platform and institution-built systems in identity verification, process supervision, risk warning, and cost settlement. Building on this analysis, the paper constructs a comprehensive closed-loop intelligent supervision framework encompassing pre-event prevention, in-process monitoring, and post-event accountability. It designs a functional division and data interaction architecture between the supervisory and service ends and proposes optimization pathways involving multi-agent collaborative governance and the integration of humanistic care into technological applications. Finally, the study outlines five directions for future supervision system development: intelligent closed-loop supervision, deep platform integration, smart multi-agent collaboration, service-friendly orientation, and data-driven incentive and penalty mechanisms, in order to establish a technically feasible, responsibility-defined, operationally efficient, and ethically conscious intelligent supervision system for LTCI, providing practical insights for promoting the high-quality development of LTCI at the national level.
Key words
long-term care insurance /
intelligent supervision /
artificial intelligence /
multi-agent collaboration
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