Abstract
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 (this https URL), 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.
Abstract (translated)
在数字健康领域，提升临床决策支持（CDS）、减少文档负担以及提高患者健康素养仍然是持续面临的挑战。本文介绍了一种开源、基于代理的框架，该框架通过模型上下文协议（MCP）将大型语言模型（LLMs）与HL7 FHIR数据进行集成，以实现实时提取和推理电子健康记录（EHRs）。此框架建立在成熟的MCP-FHIR实现之上，允许通过JSON配置声明性地访问各种FHIR资源，支持实时汇总、解释，并为包括临床医生、护理人员及患者在内的多个用户角色提供个性化沟通。为了确保隐私性和可重复性，该框架使用来自SMART Health IT沙盒（此链接）的合成EHR数据进行评估，这些数据符合FHIR R4标准。 与依赖于硬编码检索和静态工作流的传统方法不同，所提出的方法能够为AI赋能的电子健康记录应用提供可扩展、解释性强且互操作性高的解决方案。代理架构进一步支持多种FHIR格式，为推进个性化数字健康解决方案奠定了坚实的基础。
URL
https://arxiv.org/abs/2506.13800
