EHR Interaction Between Patients and AI: NoteAid EHR Interaction
Xiaocheng Zhang Zonghai Yao Hong Yu
With the rapid advancement of Large Language Models (LLMs) and their
outstanding performance in semantic and contextual comprehension, the potential
of LLMs in specialized domains warrants exploration. This paper introduces the
NoteAid EHR Interaction Pipeline, an innovative approach developed using
generative LLMs to assist in patient education, a task stemming from the need
to aid patients in understanding Electronic Health Records (EHRs). Building
upon the NoteAid work, we designed two novel tasks from the patient's
perspective: providing explanations for EHR content that patients may not
understand and answering questions posed by patients after reading their EHRs.
We extracted datasets containing 10,000 instances from MIMIC Discharge
Summaries and 876 instances from the MADE medical notes collection,
respectively, executing the two tasks through the NoteAid EHR Interaction
Pipeline with these data. Performance data of LLMs on these tasks were
collected and constructed as the corresponding NoteAid EHR Interaction Dataset.
Through a comprehensive evaluation of the entire dataset using LLM assessment
and a rigorous manual evaluation of 64 instances, we showcase the potential of
LLMs in patient education. Besides, the results provide valuable data support
for future exploration and applications in this domain while also supplying
high-quality synthetic datasets for in-house system training.