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EHR Interaction Between Patients and AI: NoteAid EHR Interaction

arXiv 2312.17475 ↗First posted 29 Dec 2023cs.CL cs.AIcited by 0

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Abstract (arXiv)

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.

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References · 18

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022Large Language Models Encode Clinical Knowledgecited by 1,830
  3. 2022Large Language Models Are Human-Level Prompt Engineerscited by 1,045
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  12. 2023BioInstruct: Instruction Tuning of Large Language Models for Biomedical Natural Language Processingcited by 25
6 more
  1. 2022MedJEx: A Medical Jargon Extraction Model with Wiki's Hyperlink Span and Contextualized Masked Language Model Scorecited by 20
  2. 2023Large Language Models Perform Diagnostic Reasoningcited by 18
  3. 2023README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLPcited by 17
  4. 2022Secure Multiparty Computation for Synthetic Data Generation from Distributed Datacited by 6
  5. 2023Synthetic Imitation Edit Feedback for Factual Alignment in Clinical Summarizationcited by 5
  6. 2023EHRTutor: Enhancing Patient Understanding of Discharge Instructionscited by 4

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