EHRTutor: Enhancing Patient Understanding of Discharge Instructions

• URL: http://arxiv.org/abs/2310.19212v1
• Date: Mon, 30 Oct 2023 00:46:03 GMT
• Title: EHRTutor: Enhancing Patient Understanding of Discharge Instructions
• Authors: Zihao Zhang, Zonghai Yao, Huixue Zhou, Feiyun ouyang, Hong Yu
• Abstract summary: This paper presents EHRTutor, an innovative multi-component framework leveraging the Large Language Model (LLM) for patient education through conversational question-answering. It 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.
• Score: 11.343429138567572
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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