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The advancement in healthcare has shifted focus toward patient-centricapproaches, particularly in self-care and patient education, facilitated byaccess to Electronic Health Records (EHR). However, medical jargon in EHRsposes significant challenges in patient comprehension. To address this, weintroduce a new task of automatically generating lay definitions, aiming tosimplify complex medical terms into patient-friendly lay language. We firstcreated the README dataset, an extensive collection of over 20,000 uniquemedical terms and 300,000 mentions, each offering context-aware lay definitionsmanually annotated by domain experts. We have also engineered a data-centricHuman-AI pipeline that synergizes data filtering, augmentation, and selectionto improve data quality. We then used README as the training data for modelsand leveraged a Retrieval-Augmented Generation (RAG) method to reducehallucinations and improve the quality of model outputs. Our extensiveautomatic and human evaluations demonstrate that open-source mobile-friendlymodels, when fine-tuned with high-quality data, are capable of matching or evensurpassing the performance of state-of-the-art closed-source large languagemodels like ChatGPT. This research represents a significant stride in closingthe knowledge gap in patient education and advancing patient-centric healthcaresolutions

README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP