arXiv — cs.AI preprintsInternational2 October 2026
Clinical Note Bloat Reduction for Efficient LLM Use
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arXiv:2604.16364v2 Announce Type: replace-cross Abstract: Background: Clinical notes contain extensive duplicated text from templates, copy-paste, and auto-populated fields ("note bloat"), diluting clinical signal, limiting longitudinal context, and increasing large language model (LLM) costs. Methods: TRACE removes note bloat using note-level EHR metadata to identify templated and copied content, with frequency-based de-duplication when metadata are unavailable. We evaluated TRACE using blinded physician span review and gold-standard templated-text annotations across four cohorts spanning liv
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