Large language models have shown success as a tutor in education in various fields. Educating patients about their clinical visits plays a pivotal role in patients' adherence to their treatment plans post-discharge. This paper presents EHRTutor, an innovative multi-component framework leveraging the Large Language Model (LLM) for patient education through conversational question-answering. EHRTutor first formulates questions pertaining to the electronic health record discharge instructions. It then educates the patient through conversation by administering each question as a test. Finally, it generates a summary at the end of the conversation. Evaluation results using LLMs and domain experts have shown a clear preference for EHRTutor over the baseline. Moreover, EHRTutor also offers a framework for generating synthetic patient education dialogues that can be used for future in-house system training.

EHRTutor是一个创新的多组件框架，利用大型语言模型（LLM）通过对话问答为患者教育提供了电子健康记录出院指示，评估结果表明EHRTutor在LLMs和领域专家中都表现出明显优势，并且EHRTutor还提供了一个从事后系统训练的未来内部系统的框架。

EHRTutor：提升患者对出院指导的理解