Related Experiment Videos
RxReduce: a medication deprescribing agent framework leveraging large language models to optimize proton pump
Benjamin Michaels1, Reed Evans2, Kris Riedman3
1Department of Pharmacy, UCSF Health, San Francisco, CA 94158, United States.
Objectives:
Polypharmacy rates continue to rise which can lead to increased risk of adverse drug events. Deprescribing is essential but time-consuming, requiring extensive clinical note and chart review. We developed RxReduce, a large language model (LLM)-powered framework as a proof of concept that can assist with medication deprescribing.
Materials And Methods:
RxReduce utilizes LLMs and retrieval-augmented generation (RAG) to evaluate patient data. Focusing on proton pump inhibitors, the system analyzes diagnoses and clinical notes to generate recommendations (Continue, Deprescribe, or Stop). To optimize accuracy, clinical notes were preprocessed into summaries before RAG retrieval.
Results:
The optimized system achieved an F1 score of 0.84, significantly outperforming the baseline (0.70).
Conclusion:
RxReduce demonstrates the viability of artificial intelligence-assisted deprescribing. By extracting key contextual insights, the framework reduces clinician burden and can be adapted for various medication classes to improve patient safety.