  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.
