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.

翻译：尽管大型语言模型（LLM）的快速发展正在促进基于人工智能的医疗应用与服务的整合，但目前针对医疗记录指南遵循性的系统性评估研究仍然有限。本文提出GuidelineGuard，一种由LLM驱动的智能体框架，能够自主分析出院记录、门诊记录等医疗文书，确保其符合既定的医疗指南。通过识别与推荐实践方案的偏差并提供循证建议，GuidelineGuard帮助临床医生遵循世界卫生组织和疾病控制与预防中心等机构的最新标准。该框架为提高医疗文书质量、减少临床差错提供了一种创新方法。