A Fully Automated Electronic Clinical Quality Measurement for Heart Failure Using Retrieval Augmented Generation and Large Language Models (Llms) | Synapse
April 23, 2026Open Access
A Fully Automated Electronic Clinical Quality Measurement for Heart Failure Using Retrieval Augmented Generation and Large Language Models (Llms)
Key Points
The study aims to evaluate a fully automated electronic clinical quality measurement system for heart failure.
Developed a retrieval augmented generation system using large language models for data extraction.
Implemented the system in clinical settings for heart failure quality measurement assessments.
Analyzed the performance of the system against standard manual methods.
The automated system achieved a 30% increase in efficiency compared to manual assessments (p<0.05).
Quality metrics for heart failure complied with treatment guidelines in 85% of cases with the automated system.
User satisfaction scores improved significantly with the automated approach (mean score 4.5/5).