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.

翻译：大型语言模型作为教育工具已在多个领域展现成效。患者对临床就医过程的认知对于其出院后遵守治疗计划具有关键作用。本文提出EHRTutor这一创新性多组件框架，通过对话式问答机制，利用大型语言模型进行患者教育。该框架首先基于电子健康档案中的出院说明生成相关问题，继而通过将每个问题作为测试项目与患者进行对话式教学，最终在对话结束时生成总结摘要。基于LLM评估与领域专家的评价结果表明，EHRTutor相较基线方法具有明显优势。此外，该框架还提供了一种生成合成患者教育对话数据的方案，可用于未来机构内部系统的训练。