🤖 AI Summary
This work addresses the challenges clinicians face when using electronic health records (EHRs), particularly fragmented information and the absence of structured summaries. To mitigate these issues, the authors propose a privacy-preserving, FHIR-native, stateless architecture that retrieves key FHIR R4 resources, normalizes them into a unified clinical context bundle, and generates evidence-based, structured summaries devoid of diagnostic suggestions while explicitly flagging missing information domains. Designed to support data minimization and local deployment, the approach has undergone end-to-end validation in both synthetic and test FHIR environments. The resulting outputs align with clinical structuring requirements; however, formal evaluation of clinical impact remains pending.
📝 Abstract
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.