Clinicians routinely navigate fragmented electronic health record (EHR) interfaces to assemble a coherent picture of a patient's problems, medications, recent encounters, and longitudinal trends. This work describes EHRSummarizer, a privacy-aware, FHIR-native reference architecture that retrieves a targeted set of high-yield FHIR R4 resources, normalizes them into a consistent clinical context package, and produces structured summaries intended to support structured chart review. The system can be configured for data minimization, stateless processing, and flexible deployment, including local inference within an organization's trust boundary. To mitigate the risk of unsupported or unsafe behavior, the summarization stage is constrained to evidence present in the retrieved context package, is intended to indicate missing or unavailable domains where feasible, and avoids diagnostic or treatment recommendations. Prototype demonstrations on synthetic and test FHIR environments illustrate end-to-end behavior and output formats; however, this manuscript does not report clinical outcomes or controlled workflow studies. We outline an evaluation plan centered on faithfulness, omission risk, temporal correctness, usability, and operational monitoring to guide future institutional assessments.

翻译：临床医生在日常工作中需要从分散的电子健康记录（EHR）界面中整合信息，以形成关于患者病情、用药情况、近期就诊记录及纵向趋势的完整视图。本研究提出EHRSummarizer，一种支持隐私保护、原生FHIR的参考架构。该架构能够检索一组目标性的高价值FHIR R4资源，将其规范化为统一的临床上下文数据包，并生成旨在支持结构化病历审查的结构化摘要。该系统可配置为支持数据最小化、无状态处理及灵活部署（包括在机构信任边界内进行本地推理）。为降低出现不受支持或不安全行为的风险，摘要生成阶段严格限定于检索到的上下文数据包中的证据，尽可能标注缺失或不可用的数据域，并避免提供诊断或治疗建议。在合成及测试FHIR环境中的原型演示展示了端到端行为与输出格式，但本文未报告临床结果或受控工作流程研究。我们提出了以忠实性、遗漏风险、时序准确性、可用性及运行监测为核心的评估方案，以指导未来的机构评估。