The advancement in healthcare has shifted focus toward patient-centric approaches, particularly in self-care and patient education, facilitated by access to Electronic Health Records (EHR). However, medical jargon in EHRs poses significant challenges in patient comprehension. To address this, we introduce a new task of automatically generating lay definitions, aiming to simplify complex medical terms into patient-friendly lay language. We first created the README dataset, an extensive collection of over 50,000 unique (medical term, lay definition) pairs and 300,000 mentions, each offering context-aware lay definitions manually annotated by domain experts. We have also engineered a data-centric Human-AI pipeline that synergizes data filtering, augmentation, and selection to improve data quality. We then used README as the training data for models and leveraged a Retrieval-Augmented Generation method to reduce hallucinations and improve the quality of model outputs. Our extensive automatic and human evaluations demonstrate that open-source mobile-friendly models, when fine-tuned with high-quality data, are capable of matching or even surpassing the performance of state-of-the-art closed-source large language models like ChatGPT. This research represents a significant stride in closing the knowledge gap in patient education and advancing patient-centric healthcare solutions.
@article{arxiv.2312.15561,
title = {README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP},
author = {Zonghai Yao and Nandyala Siddharth Kantu and Guanghao Wei and Hieu Tran and Zhangqi Duan and Sunjae Kwon and Zhichao Yang and README annotation team and Hong Yu},
journal= {arXiv preprint arXiv:2312.15561},
year = {2024}
}
Comments
To appear in Findings of the Association for Computational Linguistics: EMNLP 2024. We sincerely appreciate the tremendous efforts of the entire README annotation team throughout the expert annotation process of the README dataset