MRQC-LLM: A Novel Large Language Model Framework for Enhancing Medical Record Quality Control

Objective: This study introduces MRQC-LLM, a novel large language model (LLM)-based framework designed to enhance quality control in electronic medical records (EMRs). By leveraging fine-tuned LLMs, we compare the performance of several models in clinical documentation review to identify the most effective approach. Methods: We selected xunfei Spark LLM as the base model, enhanced through LoRA-based fine-tuning for resource optimization. The study employed multiple AI agents tailored to specific quality control tasks, including surgical records, test results, and antibiotic treatment evaluations. Additionally, Retrieval-Augmented Generation technology was integrated with a quality control knowledge base to further improve the precision of context-aware analysis and decision-making processes. Results: Our evaluation, using 2,600 medical records, demonstrated that the xunfei Spark Model with LoRA fine-tuning outperformed other models, including GPT-4o, ChatGLM3, Qwen2, and LLama3, achieving accuracy rates between 91%-100% and consistency consistently above 90%. The model excelled particularly in tasks such as surgical record consistency and antibiotic treatment effectiveness, showcasing its adaptability to complex clinical scenarios. Conclusion: The xunfei Spark model with LoRA fine-tuning demonstrates strong potential for improving EMR quality control, offering high accuracy, efficiency, and consistency across clinical scenarios. Future research will focus on expanding MRQC-LLM’s capabilities to encompass a wider array of quality control tasks in increasingly complex clinical environments.