arXiv cs.CY· Jordan L. Cahoon, Chloe Stanwyck, Asad Aali, Rachel Madding, Sulaiman S. Somani, Emma Sun, Yixing Jiang, Renumathy Dhanasekaran, Emily Alsentzer·· 11 小时前AI 评分38
TRACE 方法如何减少临床笔记冗余以降低 LLM 使用成本
Clinical Note Bloat Reduction for Efficient LLM Use
AI 导读
TRACE 利用 EHR 元数据识别模板化与复制内容，在 5.3M 份笔记上捕获了 86% 的标注模板字符，被删除文本中仅 0.3-6.6% 为作者原创。在 1000 名 Stanford Health Care 患者中，TRACE 将病历文本减少 47.3%（742.7M 字符），平均每位患者少 220,167 个 token。
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.CY · arxiv.org