Electronic health records (EHRs) store an extensive array of patient information, encompassing medical histories, diagnoses, treatments, and test outcomes. These records are crucial for enabling healthcare providers to make well-informed decisions regarding patient care. Summarizing clinical notes further assists healthcare professionals in pinpointing potential health risks and making better-informed decisions. This process contributes to reducing errors and enhancing patient outcomes by ensuring providers have access to the most pertinent and current patient data. Recent research has shown that incorporating prompts with large language models (LLMs) substantially boosts the efficacy of summarization tasks. However, we show that this approach also leads to increased output variance, resulting in notably divergent outputs even when prompts share similar meanings. To tackle this challenge, we introduce a model-agnostic Soft Prompt-Based Calibration (SPeC) pipeline that employs soft prompts to diminish variance while preserving the advantages of prompt-based summarization. Experimental findings on multiple clinical note tasks and LLMs indicate that our method not only bolsters performance but also effectively curbs variance for various LLMs, providing a more uniform and dependable solution for summarizing vital medical information.

翻译：电子健康记录（EHR）存储了丰富的患者信息，包括病史、诊断、治疗及检验结果。这些记录对于协助医疗提供者做出关于患者照护的明智决策至关重要。临床笔记摘要进一步帮助医疗专业人员识别潜在健康风险并做出更优决策。通过确保提供者能够获取最相关且最新的患者数据，这一过程有助于减少错误并改善患者预后。近期研究显示，将提示与大型语言模型（LLMs）结合能显著提升摘要任务的效果。然而，我们发现该方法也会导致输出方差增大，即使提示具有相似含义，输出结果也可能出现显著差异。为解决这一挑战，我们提出了一种模型无关的基于软提示的校准（SPeC）流程，该流程利用软提示在降低方差的同时保留基于提示的摘要优势。在多个临床笔记任务和LLMs上的实验结果表明，我们的方法不仅能提升性能，还能有效抑制不同LLMs的方差，为汇总关键医疗信息提供了更统一且可靠的解决方案。