With the rapid advancement of Large Language Models (LLMs) and their
outstanding performance in semantic and contextual comprehension, the potential
of LLMs in specialized domains warrants exploration. This paper introduces the
NoteAid EHR Interaction Pipeline, an innovative approach developed using
generative LLMs to assist in patient education, a task stemming from the need
to aid patients in understanding Electronic Health Records (EHRs). Building
upon the NoteAid work, we designed two novel tasks from the patient's
perspective: providing explanations for EHR content that patients may not
understand and answering questions posed by patients after reading their EHRs.
We extracted datasets containing 10,000 instances from MIMIC Discharge
Summaries and 876 instances from the MADE medical notes collection,
respectively, executing the two tasks through the NoteAid EHR Interaction
Pipeline with these data. Performance data of LLMs on these tasks were
collected and constructed as the corresponding NoteAid EHR Interaction Dataset.
Through a comprehensive evaluation of the entire dataset using LLM assessment
and a rigorous manual evaluation of 64 instances, we showcase the potential of
LLMs in patient education. Besides, the results provide valuable data support
for future exploration and applications in this domain while also supplying
high-quality synthetic datasets for in-house system training.

使用大型语言模型（LLMs）开发的 NoteAid EHR 交互流水线通过生成式 LLMs 的创新方法，帮助患者理解电子健康记录（EHRs），并提供有关 EHR 内容的解释和患者提问后的回答。研究使用 MIMIC 出院摘要和 MADE 医疗笔记收集的数据集，通过 NoteAid EHR 交互流水线执行这两个任务，并通过 LLM 评估和手动评估展示了 LLMs 在患者教育中的潜力。同时，这些结果为未来的探索和应用提供了宝贵的数据支持，并提供了用于内部系统训练的高质量合成数据集。