EHRTutor: Enhancing Patient Understanding of Discharge Instructions

TL;DR

EHRTutor is a novel LLM-based framework that improves patient understanding of discharge instructions through interactive conversation and summaries, enhancing post-discharge adherence and providing synthetic dialogue data.

Contribution

It introduces a multi-component LLM framework for patient education via conversational question-answering and dialogue generation, advancing personalized healthcare communication tools.

Findings

Patients preferred EHRTutor over baseline methods.

EHRTutor effectively educates patients through interactive questioning.

The system can generate synthetic dialogues for training purposes.

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.