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Workshop: Generative AI for Education (GAIED): Advances, Opportunities, and Challenges

Paper 11: EHRTutor: Enhancing Patient Understanding of Discharge Instructions

Zihao Zhang · Zonghai Yao · Huixue Zhou · Feiyun · Hong Yu

Keywords: [ conversational question answering ] [ patient education ] [ large language model ]


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.

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