用人工智能提升农村用药安全：一项范围综述
Improving Rural Medication Safety with AI: A Scoping Review
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中文总结 AI 辅助
本范围综述探究AI在农村医疗场景中提升用药安全的应用，发现AI可覆盖用药全流程，机器学习类技术能减少34%-80%的用药错误，但存在基础设施不足等农村特有挑战。
中文摘要 AI 辅助
引言：用药错误（MEs）是全球医疗系统的重大威胁，会导致患者受到伤害。在农村医疗中引入人工智能（AI）可提升患者安全。本研究旨在探究AI技术在农村医疗场景中提升患者安全、减少用药错误的应用情况与有效性。方法：通过系统文献检索开展范围综述，检索时间跨度为2012年至2025年，涉及EBSCohost、Emcare（Ovid）、MEDLINE、ProQuest消费者健康数据库等多个数据库，共纳入来自9个不同国家的12项原始研究，对数据进行主题分析以获取AI干预措施在用药流程各环节的相关见解。结果：AI技术已被整合至用药管理的各个阶段，从处方开具、配药到给药及给药后监测均有应用。研究揭示了4个关键主题：（1）所使用的各类AI，如临床决策支持系统、机器学习、自然语言处理、智能输液泵；（2）受影响的用药流程阶段；（3）这些技术在减少错误、提升流程安全性方面的有效性；（4）农村特有的挑战，包括基础设施、人员培训、系统集成、警报疲劳。多项研究表明，基于机器学习的监测可提升事件检测能力，将处方和转录错误减少34%至80%，而治理框架缺失、资金限制、临床医生抵触等障碍仍是主要阻碍。结论：在农村医疗中，AI技术在提升用药安全方面具有巨大潜力，可支持数据驱动的监测、实现流程自动化并提供临床决策辅助。
英文摘要
Introduction: Medication errors (MEs) represent a significant threat to global healthcare systems, contributing to patient harm. Introducing artificial intelligence (AI) in rural healthcare enhances patient safety. The aim is to explore the applications and effectiveness of AI technologies in enhancing patient safety and reducing medication errors in rural health settings. Methods: A scoping review was conducted through a systematic literature search spanning 2012 to 2025 across multiple databases, including EBSCohost, Emcare (Ovid), MEDLINE, and the ProQuest Consumer Health Database. Twelve primary studies from nine different nations were examined. Data were analysed thematically to obtain insights on AI interventions across the medication process. Results: AI technologies have been integrated into every stage of medication management, right from prescribing and dispensing to administration and post-administration monitoring. Four key themes came to light: (1) the various types of AI being utilised (like Clinical Decision Support Systems, Machine Learning, Natural Language Processing, and smart pumps); (2) the phases of the medication process that are affected; (3) how effective these technologies are in minimising errors and boosting workflow safety; and (4) rural-specific challenges including infrastructure, staff training, system integration, and alert fatigue. Several studies have demonstrated that machine learning-based surveillance improves incident detection and reduces prescribing and transcription errors by an impressive 34% to 80%. Barriers included lack of governance frameworks, financial limitations, and clinician resistance, which still present major obstacles. Conclusion: In rural healthcare, AI technologies hold great potential for enhancing pharmaceutical safety. They can allow data-driven monitoring, automate processes, and offer clinical decision assistance.
