EHRTutor: Enhancing Patient Understanding of Discharge Instructions
This addresses the challenge of improving patient understanding and adherence to treatment plans post-discharge, though it appears incremental as it applies existing LLM tutoring methods to a new healthcare domain.
The paper tackles the problem of patient education on discharge instructions by introducing EHRTutor, a multi-component framework using a large language model for conversational question-answering, which was preferred over a baseline in evaluations by LLMs and domain experts.
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.