传统慢病管理体系面临医保基金支出压力增长、医疗资源配置低效及监管手段滞后等挑战。本研究以山东省泰安市探索的慢病互联网医联体模式实践为例,探讨基于大数据与人工智能（AI）技术的门诊慢特病医保基金全链条管理体系。在架构上,该模式搭建了“1平台+4子系统”的功能框架,以风险管理AI分析平台为中枢,集成资格准入与退出、标准诊疗路径、医保智能监管及健康管理四个功能模块;在制度层面,该模式配套实施了政府和社会资源合作的治理框架、高效统一的药品供应链管理和激励相容的医保支付方式。基于系统运行的实际数据统计和实证分析显示,该系统有效缓解了医保基金支出压力,提升了患者健康水平。
Abstract
The traditional chronic disease management system is confronted with challenges such as the growing pressure on medical insurance fund expenditures, inefficient allocation of medical resources, and inadequate regulatory methods. Taking the practice of Tai'an City, Shandong Province as a case study, this research discusses a full-chain management system for medical insurance funds of outpatient chronic and special diseases based on big data and artificial intelligence (AI) technologies. In terms of architecture, this model constructs a functional framework of "1 platform plus 4 subsystems", with the AI-based risk management analysis platform as the core, integrating four functional modules: eligibility admission and withdrawal, standardized diagnosis and treatment pathways, intelligent medical insurance supervision, and health management. At the institutional level, the model is supported by a governance framework featuring government-society resource collaboration, an efficient and unified pharmaceutical supply chain management, and an incentive-compatible medical insurance payment method. Statistics and empirical analysis based on the actual operation data of the system show that this system has significantly alleviated the expenditure pressure on medical insurance funds and improved the health status of patients.
关键词
门诊慢特病 /
医保基金 /
数智赋能 /
健康管理
Key words
outpatient chronic and special disease /
medical insurance fund /
digital intelligence empowerment /
health management
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基金
国家自然科学基金重点项目“大数据环境下医保资源优化配置的支付机制与政策研究” （72334002）; 国家自然科学基金面上项目“基本医疗保险中欺诈骗保行为识别及监管策略优化研究” （72473030）
