Authors: Saranya Krishnamoorthy, Ayush Singh, Shabnam Tafreshi

Published on: April 25, 2024

Impact Score: 7.4

Arxiv code: Arxiv:2404.16294

Summary

• What is new: Using Large Language Models (LLMs) like GPT-4 for sectioning EHRs in a zero or few-shot setting, outperforming state-of-the-art methods.
• Why this is important: Electronic Health Records (EHRs) are becoming overly complex and lengthy, making it difficult for healthcare practitioners to sift through them efficiently.
• What the research proposes: Proposing the use of GPT-4 to identify relevant section headers within EHRs, facilitating easier navigation and information retrieval.
• Results: GPT-4 demonstrated effective sectioning of EHRs in both zero and few-shot settings, surpassing existing methods. However, it faced challenges with a complex real-world dataset, indicating the need for further research.

Technical Details

Technological frameworks used: Large Language Models (LLMs), specifically GPT-4

Models used: GPT-4

Data used: Zero-shot and few-shot learning on EHR datasets

Potential Impact

EHR software providers, healthcare IT firms, and potentially any healthcare organization leveraging EHRs could benefit or need to adapt.

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