EHRTutor：增强患者对出院指导的理解
EHRTutor: Enhancing Patient Understanding of Discharge Instructions
AI总结：
EHRTutor利用大型语言模型，通过基于出院指导的对话式问答和总结对患者进行教育，评估显示其优于基线，并可用于生成合成训练对话。
摘要：
大型语言模型已在多个领域的教育中展现出作为导师的成功。对患者进行临床就诊相关教育，在患者出院后遵循治疗计划方面起着关键作用。本文提出EHRTutor，这是一个创新的多组件框架，利用大型语言模型（LLM）通过对话式问答开展患者教育。EHRTutor首先围绕电子健康记录中的出院指导生成问题。然后，它通过将每个问题作为测试来实施，以对话方式教育患者。最后，它在对话结束时生成一份总结。使用LLM和领域专家进行的评估结果表明，与基线相比，EHRTutor获得了明显偏好。此外，EHRTutor还提供了一个生成合成患者教育对话的框架，可用于未来内部系统训练。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
