Clinical evidence underpins informed healthcare decisions, yet integrating it into real-time practice remains challenging due to intensive workloads, complex procedures, and time constraints. This study presents Quicker, an LLM-powered system that automates evidence synthesis and generates clinical recommendations following standard guideline development workflows. Quicker delivers an end-to-end pipeline from clinical questions to recommendations and supports customized decision-making through integrated tools and interactive interfaces. To evaluate how closely Quicker can reproduce guideline development processes, we constructed Q2CRBench-3, a benchmark derived from guideline development records for three diseases. Experiments show that Quicker produces precise question decomposition, expert-aligned retrieval, and near-comprehensive screening. Quicker assistance improved the accuracy of extracted study data, and its recommendations were more comprehensive and coherent than clinician-written ones. In system-level testing, Quicker working with one participant reduced recommendation development to 20-40 min. Overall, the findings demonstrate Quicker's potential to enhance the speed and reliability of evidence-based clinical decision-making.

翻译：临床证据是医疗决策的基础，然而由于工作负荷繁重、流程复杂且时间有限，将其整合到实时临床实践中仍面临挑战。本研究提出Quicker系统，这是一个基于大型语言模型的自动化证据合成系统，能够遵循标准指南制定流程生成临床推荐。Quicker构建了从临床问题到推荐建议的端到端流程，并通过集成工具与交互界面支持定制化决策。为评估Quicker对指南制定流程的还原程度，我们构建了Q2CRBench-3基准数据集，该数据集源自三种疾病的指南制定记录。实验表明，Quicker能够实现精准的问题分解、专家级对齐的文献检索以及接近完整的文献筛选。Quicker辅助显著提升了研究数据提取的准确性，其生成的推荐建议比临床医师撰写的版本更具全面性与连贯性。在系统级测试中，Quicker与一名参与者协同工作可将推荐制定时间缩短至20-40分钟。总体而言，本研究证明了Quicker在提升循证临床决策速度与可靠性方面的潜力。