摘要
目的:破解城市基层医疗“质量―效率―灵活性”不可能三角的结构性困境,构建融合多模态大模型与多智能体协同的数字化服务平台,提升家庭医生团队服务能力与居民健康参与度。方法:基于Transformer-XL跨模态对齐编码器与联邦学习技术,打造华佗GPT-AI家医系统,采用“三端协同”架构实现数据采集、智能分析、精准干预与效果反馈闭环管理。以深圳市龙岗区181.7万签约居民为样本,从医疗服务质量与效率、居民参与度与就医行为、卫生经济学与资源配置效率三个维度验证系统效能。结果:健康档案完整率100%,多类型医疗数据解读准确率≥92%,家庭医生签约率达90.59%,年度门诊随访率由64.99%提升至72.66%,平均每个家庭医生团队管理1009人,基层有效转诊率97%,平均转诊时间缩短约40%。结论:该系统重塑了城市基层医疗服务生态,推动从粗放式管理向精细化治理转型,为数字医学在基层医疗规模化落地提供了可复制的技术路径与实践范式。
Objective To resolve the structural dilemma of the"quality-efficiency-flexibility"impossible trinity in urban primary healthcare,construct a digital service platform integrating multimodal large models and multi-agent collaboration,and enhance the service capabilities of family doctor teams as well as residents'health engagement.Methods By leveraging the Transformer-XL cross-modal alignment encoder and federated learning technology,we developed the Hua Tuo GPT-AI Family Doctor System.Utilizing a'three-end collaboration'architecture,the system achieves closed-loop management of data collection,intelligent analysis,precise intervention,and feedback.Using 1.817 million contracted residents in Longgang District,Shenzhen,as samples,the system's effectiveness was validated across three dimensions:quality and efficiency of medical services,resident engagement and healthcare-seeking behavior,and health economics and resource allocation efficiency.Results The health record completeness rate reached 100%,with a multimodal medical data interpretation accuracy rate of≥92%.The family doctor contracting rate achieved 90.59%,and the annual outpatient follow-up rate increased from 64.99%to 72.66%.The average number of residents managed per family doctor team was approximately 1009.The effective referral rate at the primary level reached 97%,and the average referral time was shortened by approximately 40%.Conclusion This system reshapes the ecosystem of urban primary healthcare services,driving the transition from extensive management to refined governance.It provides a replicable technical pathway and practical paradigm for the large-scale implementation of digital medicine in primary healthcare.
作者
吕航
肖庆颖
李鹏
于广军
LYU Hang;XIAO Qingying;LI Peng;YU Guangjun(The Second Affiliated Hospital,CUHK-Shenzhen/Longgang District People's Hospital of Shenzhen,Shenzhen 518100,Guangdong Province,China;National Health Data Institute,Shenzhen)
出处
《中国数字医学》
2026年第2期32-37,共6页
China Digital Medicine
关键词
多模态大模型
多智能体系统
城市基层医疗
数字医学
Multimodal large model
Multi-agent system
Urban primary healthcare
Digital medicine
