CNSight: Evaluation of Clinical Note Segmentation Tools

• URL: http://arxiv.org/abs/2512.22795v1
• Date: Sun, 28 Dec 2025 05:40:15 GMT
• Title: CNSight: Evaluation of Clinical Note Segmentation Tools
• Authors: Risha Surana, Adrian Law, Sunwoo Kim, Rishab Sridhar, Angxiao Han, Peiyu Hong,
• Abstract summary: We evaluate rule-based baselines, domain-specific transformer models, and large language models for clinical note segmentation using a curated dataset of 1,000 notes from MIMIC-IV.<n>Our experiments show that large API-based models achieve the best overall performance, with GPT-5-mini reaching a best average F1 of 72.4 across sentence-level and freetext segmentation.
• Score: 3.673249612734457
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Clinical notes are often stored in unstructured or semi-structured formats after extraction from electronic medical record (EMR) systems, which complicates their use for secondary analysis and downstream clinical applications. Reliable identification of section boundaries is a key step toward structuring these notes, as sections such as history of present illness, medications, and discharge instructions each provide distinct clinical contexts. In this work, we evaluate rule-based baselines, domain-specific transformer models, and large language models for clinical note segmentation using a curated dataset of 1,000 notes from MIMIC-IV. Our experiments show that large API-based models achieve the best overall performance, with GPT-5-mini reaching a best average F1 of 72.4 across sentence-level and freetext segmentation. Lightweight baselines remain competitive on structured sentence-level tasks but falter on unstructured freetext. Our results provide guidance for method selection and lay the groundwork for downstream tasks such as information extraction, cohort identification, and automated summarization.

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