Article

Decoding Clinician Authorial Style: A Style-Informed Pipeline for Clinical Document Summary Generation with Large Language Models

2026-03-26

Abstract excerpt

<title>Abstract</title> <p>Large language models (LLMs) can automate clinical document summary generation. However, even clinically accurate outputs often fail to reflect individual clinicians’ writing styles, leading to substantial post-editing. We examine this stylistic gap using a multi-author corpus of de-identified clinical summaries. We propose a style-informed generation framework that extracts clinician-s...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 466e74f7-9908-570a-b0a3-64eadedaf11d
DOI: 10.21203/rs.3.rs-9054955/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Decoding Clinician Authorial Style: A Style-Informed Pipeline for Clinical Document Summary Generation with Large Language ModelsDOI 10.21203/rs.3.rs-9054955/v1Read

A Framework to Assess Clinical Safety and Hallucination Rates of LLMs for Medical Text SummarisationSemantic proximity Read

A Comparative Investigation of Zero-shot Prompting and Fine-tuning for Clinical Note SummarizationSemantic proximity Read

Measuring the Quality of AI-Generated Clinical Notes: A Systematic Review and Experimental Benchmark of Evaluation MethodsSemantic proximity Read

Fine-Tuning LLaMA2 for Summarizing Discharge Notes: Evaluating the Role of Highlighted InformationSemantic proximity Read

Towards Inpatient Discharge Summary Automation via Large Language Models: A Multidimensional Evaluation with a HIPAA-Compliant Instance of GPT-4o and Clinical Expert AssessmentSemantic proximity Read

Faithful Structured Representation from Clinical Notes via Iterative Feedback–RefinementSemantic proximity Read

Evaluating Large Language Models for Drafting Emergency Department Discharge SummariesSemantic proximity Read

Development of a Human Evaluation Framework and Correlation with Automated Metrics for Natural Language Generation of Medical DiagnosesSemantic proximity Read

From Generation to Collaboration: Using LLMs to Edit for Empathy in HealthcareSemantic proximity Read

AI-Generated Clinical Summaries: Errors and Susceptibility to Speech and Speaker VariabilitySemantic proximity Read

An Open-source Fine-tuned Large Language Model for Radiological Impression Generation: A Multi-reader Performance StudySemantic proximity Read

Accuracy and Safety of an AI Ambient Scribe Compared with Handwritten Clinical NotesSemantic proximity Read