arXiv 2510.275352025-10-31cs.CL

面向AI临床摘要的以患者为中心的摘要框架：一种混合方法设计

Patient-Centered Summarization Framework for AI Clinical Summarization: A Mixed-Methods Design

Mayo Clinic（梅奥诊所）；University Hospitals Plymouth NHS Trust（普利茅斯大学医院NHS信托）；University of Plymouth（普利茅斯大学）；University of Edinburgh（爱丁堡大学）

Maria Lizarazo Jimenez, Ana Gabriela Claros, Kieran Green, David Toro-Tobon, Felipe Larios, Sheena Asthana, Camila Wenczenovicz, Kerly Guevara Maldonado, Luis V… 展开作者

Maria Lizarazo Jimenez, Ana Gabriela Claros, Kieran Green, David Toro-Tobon, Felipe Larios, Sheena Asthana, Camila Wenczenovicz, Kerly Guevara Maldonado, Luis Vilatuna-Andrango, Cristina Proano-Velez, Satya Sai Sri Bandi, Shubhangi Bagewadi, Megan E. Branda, Misk Al Zahidy, Saturnino Luz, Mirella Lapata, Juan P. Brito, Oscar J. Ponce-Ponte

备注：: The first two listed authors contributed equally Pages: 21; Figures:2; Tables:3

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AI总结：

本文提出以患者为中心的临床摘要（PCS）框架，通过混合方法制定标注指南并评估五个开源LLMs在零样本和少样本提示下的生成效果，发现模型在完整性和流畅性上接近专家，但在正确性和患者中心度上仍有不足。

摘要：

大型语言模型（LLMs）在从患者与临床医生的对话中生成临床摘要方面，越来越展现出达到人类水平表现的潜力。然而，这些摘要通常侧重于患者的生物学特征，而非他们的偏好、价值观、意愿和担忧。为实现以患者为中心的护理，我们提出了一种针对人工智能（AI）临床摘要任务的新标准：以患者为中心的摘要（PCS）。我们的目标是开发一个框架来生成PCS，使其能够捕捉患者的价值观并确保临床实用性，同时评估当前的开源LLMs能否在此任务中达到人类水平的表现。我们采用了混合方法流程。英国的两个患者与公众参与小组（10名患者和8名临床医生）参与了半结构化访谈，探讨临床摘要中应包含哪些个人和情境信息，以及应如何为临床使用构建这些信息。访谈结果为八名临床医生提供了标注指南，用于从88次心房颤动问诊中创建金标准PCS。16次问诊被用于完善与指南对齐的提示词。五个开源LLMs（Llama-3.2-3B、Llama-3.1-8B、Mistral-8B、Gemma-3-4B和Qwen3-8B）使用零样本和少样本提示为72次问诊生成摘要，并使用ROUGE-L、BERTScore和定性指标进行评估。患者强调生活方式、社会支持、近期压力源和护理价值观。临床医生则寻求简洁的功能、心理社会和情感背景。最佳的零样本表现由Mistral-8B（ROUGE-L 0.189）和Llama-3.1-8B（BERTScore 0.673）取得；最佳的少样本表现由Llama-3.1-8B（ROUGE-L 0.206，BERTScore 0.683）取得。专家与模型在完整性和流畅性方面表现相似，而在正确性和以患者为中心度上，人类PCS更受青睐。

英文摘要：

Large Language Models (LLMs) are increasingly demonstrating the potential to reach human-level performance in generating clinical summaries from patient-clinician conversations. However, these summaries often focus on patients' biology rather than their preferences, values, wishes, and concerns. To achieve patient-centered care, we propose a new standard for Artificial Intelligence (AI) clinical summarization tasks: Patient-Centered Summaries (PCS). Our objective was to develop a framework to generate PCS that capture patient values and ensure clinical utility and to assess whether current open-source LLMs can achieve human-level performance in this task. We used a mixed-methods process. Two Patient and Public Involvement groups (10 patients and 8 clinicians) in the United Kingdom participated in semi-structured interviews exploring what personal and contextual information should be included in clinical summaries and how it should be structured for clinical use. Findings informed annotation guidelines used by eight clinicians to create gold-standard PCS from 88 atrial fibrillation consultations. Sixteen consultations were used to refine a prompt aligned with the guidelines. Five open-source LLMs (Llama-3.2-3B, Llama-3.1-8B, Mistral-8B, Gemma-3-4B, and Qwen3-8B) generated summaries for 72 consultations using zero-shot and few-shot prompting, evaluated with ROUGE-L, BERTScore, and qualitative metrics. Patients emphasized lifestyle routines, social support, recent stressors, and care values. Clinicians sought concise functional, psychosocial, and emotional context. The best zero-shot performance was achieved by Mistral-8B (ROUGE-L 0.189) and Llama-3.1-8B (BERTScore 0.673); the best few-shot by Llama-3.1-8B (ROUGE-L 0.206, BERTScore 0.683). Completeness and fluency were similar between experts and models, while correctness and patient-centeredness favored human PCS.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。