EHRTutor: Enhancing Patient Understanding of Discharge Instructions
Zihao Zhang Zonghai Yao Huixue Zhou Feiyun ouyang Hong Yu
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.