# Development and Testing of a Novel Large Language Model-Based Clinical Decision Support Systems for Medication Safety in 12 Clinical Specialties

arXiv: https://arxiv.org/abs/2402.01741 · HTML: https://mlsanity.com/paper/2402.01741/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Medical and biomedical RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 6 of its 38 references that are in the map.
- First posted on arXiv 29 Jan 2024.

## Placed in

- Retrieval-augmented generation / [Medical and biomedical RAG](https://mlsanity.com/rag/medical-and-biomedical-rag/)

## Builds on (most cited first, 6 of 6)

- [MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI](https://mlsanity.com/paper/2311.16502/) (2023)
- [Towards Expert-Level Medical Question Answering with Large Language Models](https://mlsanity.com/paper/2305.09617/) (2023)
- [Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine](https://mlsanity.com/paper/2311.16452/) (2023)
- [Seven Failure Points When Engineering a Retrieval Augmented Generation System](https://mlsanity.com/paper/2401.05856/) (2024)
- [DRG-LLaMA : Tuning LLaMA Model to Predict Diagnosis-related Group for Hospitalized Patients](https://mlsanity.com/paper/2309.12625/) (2023)
- [MedDM:LLM-executable clinical guidance tree for clinical decision-making](https://mlsanity.com/paper/2312.02441/) (2023)

## Built on by (most cited first, 5 of 5)

- [An evaluation of DeepSeek Models in Biomedical Natural Language Processing](https://mlsanity.com/paper/2503.00624/) (2025)
- [Multilingual Political Views of Large Language Models: Identification and Steering](https://mlsanity.com/paper/2507.22623/) (2025)
- [CDR-Agent: Intelligent Selection and Execution of Clinical Decision Rules Using Large Language Model Agents](https://mlsanity.com/paper/2505.23055/) (2025)
- [Guardrails for avoiding harmful medical product recommendations and off-label promotion in generative AI models](https://mlsanity.com/paper/2406.16455/) (2024)
- [Human-Level and Beyond: Benchmarking Large Language Models Against Clinical Pharmacists in Prescription Review](https://mlsanity.com/paper/2512.02024/) (2025)

## Abstract (arXiv)

Importance: We introduce a novel Retrieval Augmented Generation (RAG)-Large Language Model (LLM) framework as a Clinical Decision Support Systems (CDSS) to support safe medication prescription. Objective: To evaluate the efficacy of LLM-based CDSS in correctly identifying medication errors in different patient case vignettes from diverse medical and surgical sub-disciplines, against a human expert panel derived ground truth. We compared performance for under 2 different CDSS practical healthcare integration modalities: LLM-based CDSS alone (fully autonomous mode) vs junior pharmacist + LLM-based CDSS (co-pilot, assistive mode). Design, Setting, and Participants: Utilizing a RAG model with state-of-the-art medically-related LLMs (GPT-4, Gemini Pro 1.0 and Med-PaLM 2), this study used 61 prescribing error scenarios embedded into 23 complex clinical vignettes across 12 different medical and surgical specialties. A multidisciplinary expert panel assessed these cases for Drug-Related Problems (DRPs) using the PCNE classification and graded severity / potential for harm using revised NCC MERP medication error index. We compared. Results RAG-LLM performed better compared to LLM alone. When employed in a co-pilot mode, accuracy, recall, and F1 scores were optimized, indicating effectiveness in identifying moderate to severe DRPs. The accuracy of DRP detection with RAG-LLM improved in several categories but at the expense of lower precision. Conclusions This study established that a RAG-LLM based CDSS significantly boosts the accuracy of medication error identification when used alongside junior pharmacists (co-pilot), with notable improvements in detecting severe DRPs. This study also illuminates the comparative performance of current state-of-the-art LLMs in RAG-based CDSS systems.