arXiv cs.AI· Jordan L. Cahoon, Chloe Stanwyck, Asad Aali, Rachel Madding, Sulaiman S. Somani, Emma Sun, Yixing Jiang, Renumathy Dhanasekaran, Emily Alsentzer·· 4 小时前AI 评分38
TRACE 研究提出削减临床病历冗余以降低 LLM 使用成本
Clinical Note Bloat Reduction for Efficient LLM Use
AI 导读
TRACE 方法利用 EHR 元数据识别模板和复制内容，去除临床病历中的“note bloat”冗余，覆盖多个医疗系统共 5.3M 份病历的四个队列评估。去除文本中仅 0.3-6.6% 被医生盲评判定为作者生成，信息抽取 F1 差异平均在 -0.009 到 +0.004 之间，性能基本无损。
正文 · 原文
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 liver transplant, obstetrics, and inpatient populations at multiple health systems (5.3M notes). We compared zero-shot LLMs and embedding-based classifiers using original and TRACE-processed notes for 20 information extraction tasks and prediction of 5-year survival, postpartum hemorrhage, and 30-day readmission.
Results: Only 0.3-6.6% of removed text was flagged as author-generated; TRACE captured 86% of annotated templated characters. Information extraction F1 differences averaged by cohort ranged from -0.009 to +0.004; task-specific prediction F1 differences ranged from -0.011 to +0.018. Among 1,000 randomly sampled Stanford Health Care patients, TRACE reduced chart text by 47.3% (742.7M characters), averaging 220,167 fewer tokens per patient. Using 2024 encounter volumes at a large tertiary academic center and one query per encounter, projected three-year net savings ranged from $1.00M to $13.58M across evaluated model pricing schemes, including initial and annual TRACE processing costs.
Conclusion: TRACE substantially reduces clinical note redundancy while preserving information extraction and prediction performance. Underused EHR metadata can reduce LLM inference costs, expand usable longitudinal context, and support scalable clinical AI.
| Subjects: | Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2604.16364 [cs.CY] |
| (or arXiv:2604.16364v3 [cs.CY] for this version) | |
| https://doi.org/10.48550/arXiv.2604.16364 arXiv-issued DOI via DataCite |
Submission history
From: Jordan Cahoon [view email]
[v1]
Sat, 21 Mar 2026 20:56:25 UTC (1,794 KB)
[v2]
Thu, 1 Oct 2026 15:41:31 UTC (2,395 KB)
[v3]
Mon, 26 Oct 2026 23:57:09 UTC (2,395 KB)
来源：arXiv cs.AI · arxiv.org