Fight for the People's Health: The Application of Al Multiagent Systems in Medical Consortia

Background: The nationwide implementation of medical consortia at both district and county levels has reshaped China's healthcare system profoundly by establishing collaborative institutional networks and tiered service delivery pathways. However, difficulties such as loose referral, fragmented information, and resource disparity have hampered the delivery of integrated care in these consortia. Leveraging cutting-edge information technology, this study aims to propose a set of AI-driven integrated medical alliance solutions catering to the needs of patients, medical workers, and administrators. Methods: In the study, we introduce a multiagent system using the coordinator worker model and role-based architecture. The system uses the retrieval-augmented generation (RAG) framework, the ERNIE model, the chain of thought (CoT) reasoning mechanism, and an interactive platform. It is capable of enhancing full life-cycle healthcare service by supporting patients' navigation of the system, doctors' clinical decision-making, and hospital management, providing key functions like triage guidance, medical research assistance, and real-time hospital operational data analysis. Results: This intelligent medical decision support platform provides tailored healthcare, ensures treatment continuity, improves decision-making quality, and optimizes resource allocation efficiency. Discussion: Following the detailed analysis of the applications and advantages of the framework, the study further explores the challenges faced during the implementation of this platform, particularly related to hallucination, data security, and cost control. Conclusions: Finally, it calls for continued efforts to build intelligent, equitable, and high-value healthcare systems through expanded applications of medical multiagent systems.