GENIE: Generative Note Information Extraction model for structuring EHR data

TL;DR

GENIE is a novel system leveraging fine-tuned small LLMs to efficiently extract structured information from unstructured clinical notes in EHRs, outperforming traditional methods and enhancing scalability.

Contribution

The paper introduces GENIE, a unified, end-to-end generative model that streamlines EHR note structuring, addressing limitations of existing rule-based and large LLM approaches.

Findings

GENIE achieves high accuracy in extracting entities, assertions, and other attributes.

It outperforms traditional tools like cTAKES and MetaMap in multiple tasks.

The system is scalable and suitable for real-world healthcare applications.

Abstract

Electronic Health Records (EHRs) hold immense potential for advancing healthcare, offering rich, longitudinal data that combines structured information with valuable insights from unstructured clinical notes. However, the unstructured nature of clinical text poses significant challenges for secondary applications. Traditional methods for structuring EHR free-text data, such as rule-based systems and multi-stage pipelines, are often limited by their time-consuming configurations and inability to adapt across clinical notes from diverse healthcare settings. Few systems provide a comprehensive attribute extraction for terminologies. While giant large language models (LLMs) like GPT-4 and LLaMA 405B excel at structuring tasks, they are slow, costly, and impractical for large-scale use. To overcome these limitations, we introduce GENIE, a Generative Note Information Extraction system that leverages LLMs to streamline the structuring of unstructured clinical text into usable data with standardized format. GENIE processes entire paragraphs in a single pass, extracting entities, assertion statuses, locations, modifiers, values, and purposes with high accuracy. Its unified, end-to-end approach simplifies workflows, reduces errors, and eliminates the need for extensive manual intervention. Using a robust data preparation pipeline and fine-tuned small scale LLMs, GENIE achieves competitive performance across multiple information extraction tasks, outperforming traditional tools like cTAKES and MetaMap and can handle extra attributes to be extracted. GENIE strongly enhances real-world applicability and scalability in healthcare systems. By open-sourcing the model and test data, we aim to encourage collaboration and drive further advancements in EHR structurization.