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期刊来源：Nat Med

原文链接：https://doi.org/10.1038/s41591-024-02933-8

摘要内容如下：

药房用药说明中的错误，如不正确的剂量或频率说明，会增加药物不良事件的几率，从而大大增加患者的安全风险。本研究探讨了如何将领域知识与大型语言模型（LLM）（能够进行复杂的文本解释和生成）相结合来减少这些错误。我们介绍了MEDIC（药物指导副驾驶员），这是一个通过优先考虑处方的核心临床组成部分（如剂量和频率）的精确沟通来模拟药剂师推理的系统。它使用来自Amazon Pharmacy的1，000个专家注释和增强的方向对第一代LLM进行微调，以提取核心组件，并使用药房逻辑和安全护栏将它们组装成完整的方向。我们将MEDIC与两个基于LLM的基准进行了比较：一个利用了150万种药物说明，另一个使用了最先进的LLM。在1，200份专家审查的处方中，两个基准分别记录了1.51（置信区间（CI）1.03，2.31）和4.38（CI 3.13，6.64）倍的未遂事件-在到达患者之前发现并纠正的错误。此外，我们通过在网上药店的生产系统中部署来测试Medic，在此实验期间，它将未遂事件减少了33%（CI 26%，40%）。这项研究表明，LLM凭借领域专业知识和保障措施，提高了药房运营的准确性和效率。

英文原文如下：

Abstracts

Errors in pharmacy medication directions, such as incorrect instructions for dosage or frequency, can increase patient safety risk substantially by raising the chances of adverse drug events. This study explores how integrating domain knowledge with large language models (LLMs)-capable of sophisticated text interpretation and generation-can reduce these errors. We introduce MEDIC (medication direction copilot), a system that emulates the reasoning of pharmacists by prioritizing precise communication of core clinical components of a prescription, such as dosage and frequency. It fine-tunes a first-generation LLM using 1,000 expert-annotated and augmented directions from Amazon Pharmacy to extract the core components and assembles them into complete directions using pharmacy logic and safety guardrails. We compared MEDIC against two LLM-based benchmarks: one leveraging 1.5 million medication directions and the other using state-of-the-art LLMs. On 1,200 expert-reviewed prescriptions, the two benchmarks respectively recorded 1.51 (confidence interval (CI) 1.03, 2.31) and 4.38 (CI 3.13, 6.64) times more near-miss events-errors caught and corrected before reaching the patient-than MEDIC. Additionally, we tested MEDIC by deploying within the production system of an online pharmacy, and during this experimental period, it reduced near-miss events by 33% (CI 26%, 40%). This study shows that LLMs, with domain expertise and safeguards, improve the accuracy and efficiency of pharmacy operations.

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