Rx Strategist: Prescription Verification using LLM Agents System
This work addresses prescription errors to enhance patient safety in healthcare, representing an incremental improvement over existing LLM methods.
The paper tackles prescription verification by developing Rx Strategist, a system that combines knowledge graphs, search strategies, and a multi-stage LLM pipeline to improve accuracy and reliability, achieving performance comparable to an experienced clinical pharmacist.
To protect patient safety, modern pharmaceutical complexity demands strict prescription verification. We offer a new approach - Rx Strategist - that makes use of knowledge graphs and different search strategies to enhance the power of Large Language Models (LLMs) inside an agentic framework. This multifaceted technique allows for a multi-stage LLM pipeline and reliable information retrieval from a custom-built active ingredient database. Different facets of prescription verification, such as indication, dose, and possible drug interactions, are covered in each stage of the pipeline. We alleviate the drawbacks of monolithic LLM techniques by spreading reasoning over these stages, improving correctness and reliability while reducing memory demands. Our findings demonstrate that Rx Strategist surpasses many current LLMs, achieving performance comparable to that of a highly experienced clinical pharmacist. In the complicated world of modern medications, this combination of LLMs with organized knowledge and sophisticated search methods presents a viable avenue for reducing prescription errors and enhancing patient outcomes.