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）存储了大量患者信息，涵盖病史、诊断、治疗及检验结果。这些记录对于医疗提供者做出明智的患者护理决策至关重要。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。该过程通过确保医疗提供者获取最相关、最新的患者数据，有助于减少错误并改善患者预后。近期研究表明，将提示与大语言模型（LLMs）结合使用能显著提升摘要任务的效能。然而，我们发现该方法也会导致输出方差增大，即使提示语意相近，产出结果仍存在显著差异。为应对这一挑战，我们提出了一种模型无关的软提示校准（SPeC）流程，该流程利用软提示在保留基于提示的摘要优势的同时降低方差。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的输出方差，为关键医疗信息摘要提供更统一、可靠的解决方案。