Nursing notes, an important component of Electronic Health Records (EHRs), keep track of the progression of a patient's health status during a care episode. Distilling the key information in nursing notes through text summarization techniques can improve clinicians' efficiency in understanding patients' conditions when reviewing nursing notes. However, existing abstractive summarization methods in the clinical setting have often overlooked nursing notes and require the creation of reference summaries for supervision signals, which is time-consuming. In this work, we introduce QGSumm, a query-guided self-supervised domain adaptation framework for nursing note summarization. Using patient-related clinical queries as guidance, our approach generates high-quality, patient-centered summaries without relying on reference summaries for training. Through automatic and manual evaluation by an expert clinician, we demonstrate the strengths of our approach compared to the state-of-the-art Large Language Models (LLMs) in both zero-shot and few-shot settings. Ultimately, our approach provides a new perspective on conditional text summarization, tailored to the specific interests of clinical personnel.

翻译：护理记录作为电子健康记录（EHRs）的重要组成部分，记录了患者在护理期间健康状况的演变过程。通过文本摘要技术提炼护理记录中的关键信息，能够提高临床医生在查阅护理记录时理解患者病情的效率。然而，临床环境中现有的抽象摘要方法往往忽视了护理记录，且需要创建参考摘要作为监督信号，这一过程耗时费力。在本研究中，我们提出了QGSumm，一种用于护理记录摘要的查询引导自监督领域自适应框架。该方法以患者相关的临床查询为引导，无需依赖参考摘要进行训练，即可生成高质量、以患者为中心的摘要。通过自动评估和临床专家的人工评估，我们证明了该方法在零样本和少样本设置下相较于最先进的大型语言模型（LLMs）的优势。最终，我们的研究为条件文本摘要提供了新的视角，能够根据临床人员的特定需求进行定制。