The advancement in healthcare has shifted focus toward patient-centric approaches, particularly in self-care and patient education, facilitated by access to Electronic Health Records (EHR). However, medical jargon in EHRs poses significant challenges in patient comprehension. To address this, we introduce a new task of automatically generating lay definitions, aiming to simplify complex medical terms into patient-friendly lay language. We first created the README dataset, an extensive collection of over 20,000 unique medical terms and 300,000 mentions, each offering context-aware lay definitions manually annotated by domain experts. We have also engineered a data-centric Human-AI pipeline that synergizes data filtering, augmentation, and selection to improve data quality. We then used README as the training data for models and leveraged a Retrieval-Augmented Generation (RAG) method to reduce hallucinations and improve the quality of model outputs. Our extensive automatic and human evaluations demonstrate that open-source mobile-friendly models, when fine-tuned with high-quality data, are capable of matching or even surpassing the performance of state-of-the-art closed-source large language models like ChatGPT. This research represents a significant stride in closing the knowledge gap in patient education and advancing patient-centric healthcare solutions

翻译：医疗健康的进步日益聚焦于以患者为中心的方法，特别是在借助电子健康档案（EHR）进行自我护理与患者教育方面。然而，EHR中的医学术语给患者理解带来了显著挑战。为解决此问题，我们提出了一项新任务：自动生成科普性定义，旨在将复杂的医学术语转化为患者易于理解的通俗语言。我们首先构建了README数据集，包含超过2万个独特医学术语及30万条提及记录，每条记录均附有领域专家人工标注的、基于上下文的科普定义。我们还设计了一种以数据为中心的人机协作流程，通过数据筛选、增强与选择来协同提升数据质量。随后，我们使用README作为模型训练数据，并利用检索增强生成（RAG）方法减少模型幻觉、提升输出质量。大量自动评估与人工评估表明，经高质量数据微调的开源移动端友好模型，其性能可媲美甚至超越ChatGPT等顶尖闭源大语言模型。本研究在弥合患者教育知识差距、推动以患者为中心的医疗解决方案方面迈出了重要一步。