JAMIA open · 2026
RxReduce: a medication deprescribing agent framework leveraging large language models to optimize proton pump inhibitor use.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Michaels Benjamin, Evans Reed, Riedman Kris, Yang Joanna, Selby Gray
Аннотация
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.
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.