用LLM+MCP协议实现智能病历分析，让医患沟通更高效。
Enhancing Clinical Decision Support and EHR Insights through LLMs and the Model Context Protocol: An Open-Source MCP-FHIR Framework
- 通过MCP协议动态读取FHIR病历数据，支持多角色实时摘要与解释。
- 基于合成数据测试，可实现跨系统、可复现的智能医疗决策支持。
- 开源框架适合开发者构建隐私保护的个性化健康应用。
提升临床决策支持、减轻文档负担、改善患者健康素养仍是数字健康领域的长期挑战。本文提出一个开源的基于代理的框架，将大语言模型（LLMs）与HL7 FHIR数据通过模型上下文协议（MCP）结合，实现对电子健康记录（EHR）的动态提取与推理。基于已有的MCP-FHIR实现，该框架通过基于JSON的配置，支持对多种FHIR资源的声明式访问，可在临床医生、照护者和患者等多用户角色间实现实时摘要、解读与个性化沟通。为保障隐私与可复现性，框架采用符合FHIR R4标准的SMART Health IT沙盒合成EHR数据进行评估。相比依赖硬编码检索与静态流程的传统方法，该方案提供可扩展、可解释、可互操作的AI驱动型EHR应用。其代理式架构还兼容多种FHIR格式，为个性化数字健康解决方案奠定了坚实基础。
原文摘要 · Abstract (English)
Enhancing clinical decision support (CDS), reducing documentation burdens, and improving patient health literacy remain persistent challenges in digital health. This paper presents an open-source, agent-based framework that integrates Large Language Models (LLMs) with HL7 FHIR data via the Model Context Protocol (MCP) for dynamic extraction and reasoning over electronic health records (EHRs). Built on the established MCP-FHIR implementation, the framework enables declarative access to diverse FHIR resources through JSON-based configurations, supporting real-time summarization, interpretation, and personalized communication across multiple user personas, including clinicians, caregivers, and patients. To ensure privacy and reproducibility, the framework is evaluated using synthetic EHR data from the SMART Health IT sandbox (https://r4.smarthealthit.org/), which conforms to the FHIR R4 standard. Unlike traditional approaches that rely on hardcoded retrieval and static workflows, the proposed method delivers scalable, explainable, and interoperable AI-powered EHR applications. The agentic architecture further supports multiple FHIR formats, laying a robust foundation for advancing personalized digital health solutions.
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