EHRSummarizer: A Privacy-Aware, FHIR-Native Reference Architecture for Source-Grounded EHR Summarization
arXiv:2601.01668v2 Announce Type: replace 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 manuscript describes EHRSummarizer, a privacy-aware, FHIR-native reference architecture for structured EHR summarization. The architecture retrieves a targeted set of high-yield HL7 FHIR R4 resources, normalizes them into a clinical context package, and uses a constrained summarization stage to produce source-grounded summaries intended to suppo
The proliferation of AI models capable of complex information synthesis is driving demand for specialized, privacy-aware architectures in highly regulated domains like healthcare.
This development signals a critical step towards integrating AI for practical clinical decision support while addressing inherent data privacy and regulatory compliance challenges.
The focus on FHIR-native, privacy-aware architectures for EHR summarization moves AI from experimental tools to directly actionable systems within clinical workflows.
- · Healthcare providers
- · Patients
- · Health IT software vendors
- · AI-in-healthcare solution providers
- · Manual data abstractors
- · Legacy EHR systems without robust API integration
Clinicians will experience reduced administrative burden and faster access to relevant patient information.
Improved diagnostic accuracy and treatment planning may lead to better patient outcomes and reduced medical errors.
Standardized AI-generated summaries could enable population-level health insights and accelerate clinical research by making EHR data more accessible and usable.
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Read at arXiv cs.CL