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

arXiv: https://arxiv.org/abs/2411.06264 · HTML: https://mlsanity.com/paper/2411.06264/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 16 of its 29 references that are in the map.
- First posted on arXiv 9 Nov 2024.

## Placed in

- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Builds on (most cited first, 16 of 16)

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- [Scalable and accurate deep learning for electronic health records](https://mlsanity.com/paper/1801.07860/) (2018)
- [MEDITRON-70B: Scaling Medical Pretraining for Large Language Models](https://mlsanity.com/paper/2311.16079/) (2023)
- [Is Cosine-Similarity of Embeddings Really About Similarity?](https://mlsanity.com/paper/2403.05440/) (2024)
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- [Intelligent Clinical Documentation: Harnessing Generative AI for Patient-Centric Clinical Note Generation](https://mlsanity.com/paper/2405.18346/) (2024)
- [Demystifying Embedding Spaces using Large Language Models](https://mlsanity.com/paper/2310.04475/) (2023)
- [Healthcare Copilot: Eliciting the Power of General LLMs for Medical Consultation](https://mlsanity.com/paper/2402.13408/) (2024)
- [Enhancing Clinical Efficiency through LLM: Discharge Note Generation for Cardiac Patients](https://mlsanity.com/paper/2404.05144/) (2024)
- [From RAGs to riches: Utilizing large language models to write documents for clinical trials](https://mlsanity.com/paper/2402.16406/) (2024)
- [oRetrieval Augmented Generation for 10 Large Language Models and its Generalizability in Assessing Medical Fitness](https://mlsanity.com/paper/2410.08431/) (2024)

## Abstract (arXiv)

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.