GuidelineGuard: An Agentic Framework for Medical Note Evaluation with Guideline Adherence

TL;DR

GuidelineGuard is an LLM-powered framework that autonomously evaluates medical notes for guideline adherence, helping clinicians improve documentation quality and reduce errors by identifying deviations and suggesting evidence-based corrections.

Contribution

It introduces a novel agentic framework leveraging LLMs for systematic medical note evaluation against healthcare guidelines.

Findings

Effective detection of guideline deviations in medical notes

Provides evidence-based suggestions for compliance

Enhances documentation quality and reduces errors

Abstract

Although rapid advancements in Large Language Models (LLMs) are facilitating the integration of artificial intelligence-based applications and services in healthcare, limited research has focused on the systematic evaluation of medical notes for guideline adherence. This paper introduces GuidelineGuard, an agentic framework powered by LLMs that autonomously analyzes medical notes, such as hospital discharge and office visit notes, to ensure compliance with established healthcare guidelines. By identifying deviations from recommended practices and providing evidence-based suggestions, GuidelineGuard helps clinicians adhere to the latest standards from organizations like the WHO and CDC. This framework offers a novel approach to improving documentation quality and reducing clinical errors.