A presentation titled, “Generative Terminology Mapping of Source Medication Terms to RxNorm: Using LLMs as Clinical Expert Reviewer,” was given at the 2024 NLP Summit to the American Medical Informatics Association by authors Philip Ballentine, MSc and C. William Pike, MD. Dr. Pike and Mr. Ballentine are affiliated with Atropos Health.
Short Summary:
At Atropos Health, we innovated an approach combining NLP models with Large Language Models (LLMs) to map medication text strings to RxNorm in Real World Data. This method achieved 99.2% accuracy in ingredient correctness on a billion-scale dataset, significantly reducing the NLP model’s error rate from 9% to 0.7%. Notably, this technique displayed a high agreement level (Cohen’s κ of 0.899) between its results and human experts, demonstrating its reliability and potential for broad application.
Key Conclusions:
- This approach led to a 98% reduction in mapping costs compared to manual processes, emphasizing its scalability and cost-effectiveness. This efficient method did not require model fine-tuning, training, extensive data exposure to the LLM, or the use of a clinically-focused LLM at all. The presentation will explore the technical challenges, cost analysis, and future prospects of this innovative approach.
Watch the full presentation here
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