EHRSummarizer: A Privacy-Aware, FHIR-Native Reference Architecture for Source-Grounded EHR Summarization
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866913163351425024 |
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| author | Kazemzadeh, Houman Minaifar, Nima Naderi, Kamyar Tabibzadeh, Sho |
| author_facet | Kazemzadeh, Houman Minaifar, Nima Naderi, Kamyar Tabibzadeh, Sho |
| contents | 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 support chart review. The architecture further clarifies missing-data status handling, medication-status ambiguity, controlled use of narrative clinical documents when available, and future source-to-summary traceability. The manuscript describes a reference architecture and prototype behavior rather than a validated clinical intervention, autonomous clinical decision-support system, or evidence of clinical benefit. Prototype demonstrations on synthetic and test FHIR environments illustrate end-to-end behavior and output formats; however, this manuscript does not report clinical outcomes, controlled workflow studies, or benchmark results. We outline an evaluation plan centered on faithfulness, omission risk, temporal correctness, usability, privacy, and operational monitoring to guide future institutional assessment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_01668 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | EHRSummarizer: A Privacy-Aware, FHIR-Native Reference Architecture for Source-Grounded EHR Summarization Kazemzadeh, Houman Minaifar, Nima Naderi, Kamyar Tabibzadeh, Sho Computation and Language Artificial Intelligence 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 support chart review. The architecture further clarifies missing-data status handling, medication-status ambiguity, controlled use of narrative clinical documents when available, and future source-to-summary traceability. The manuscript describes a reference architecture and prototype behavior rather than a validated clinical intervention, autonomous clinical decision-support system, or evidence of clinical benefit. Prototype demonstrations on synthetic and test FHIR environments illustrate end-to-end behavior and output formats; however, this manuscript does not report clinical outcomes, controlled workflow studies, or benchmark results. We outline an evaluation plan centered on faithfulness, omission risk, temporal correctness, usability, privacy, and operational monitoring to guide future institutional assessment. |
| title | EHRSummarizer: A Privacy-Aware, FHIR-Native Reference Architecture for Source-Grounded EHR Summarization |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2601.01668 |