背景:人工智能(AI)提供了识别患者健康风险的机会,从而影响患者安全结果。
目的:本系统文献综述的目的是识别和分析利用或整合AI的定量研究,以解决和报告临床水平的患者安全结果。
方法:我们的搜索仅限于PubMed,PubMedCentral,和WebofScience数据库检索2009年1月至2019年8月以英文发表的研究文章。我们专注于报告阳性的定量研究,负,或使用AI应用程序的患者安全结果的中间变化,特别是那些基于机器学习算法和自然语言处理的算法。仅报告AI表现但不报告其对患者安全结果的影响的定量研究被排除在进一步审查之外。
结果:我们确定了53项符合条件的研究,关于他们的患者安全子类别的总结,最常用的AI,和报告的性能指标。公认的安全子类别是临床警报(n=9;主要基于决策树模型),临床报告(n=21;基于支持向量机模型),和药物安全性(n=23;主要基于决策树模型)。对这53项研究的分析还发现了两个重要发现:(1)缺乏标准化基准;(2)AI报告中的异质性。
结论:本系统综述表明,人工智能支持的决策支持系统,如果正确实施,可以通过改进错误检测来帮助提高患者的安全性,患者分层,和药物管理。未来的工作仍然需要在前瞻性和真实世界的临床环境中对这些系统进行可靠的验证,以了解AI在医疗保健环境中预测安全结果的能力。
BACKGROUND: Artificial intelligence (AI) provides opportunities to identify the health risks of patients and thus influence patient safety outcomes.
OBJECTIVE: The purpose of this systematic literature review was to identify and analyze quantitative studies utilizing or integrating AI to address and report clinical-level patient safety outcomes.
METHODS: We restricted our search to the PubMed, PubMed Central, and Web of Science databases to retrieve research articles published in English between January 2009 and August 2019. We focused on quantitative studies that reported positive, negative, or intermediate changes in patient safety outcomes using AI apps, specifically those based on machine-learning algorithms and natural language processing. Quantitative studies reporting only AI performance but not its influence on patient safety outcomes were excluded from further review.
RESULTS: We identified 53 eligible studies, which were summarized concerning their patient safety subcategories, the most frequently used AI, and reported performance metrics. Recognized safety subcategories were clinical alarms (n=9; mainly based on decision tree models), clinical reports (n=21; based on support vector machine models), and drug safety (n=23; mainly based on decision tree models). Analysis of these 53 studies also identified two essential findings: (1) the lack of a standardized benchmark and (2) heterogeneity in AI reporting.
CONCLUSIONS: This systematic review indicates that AI-enabled decision support systems, when implemented correctly, can aid in enhancing patient safety by improving error detection, patient stratification, and drug management. Future work is still needed for robust validation of these systems in prospective and real-world clinical environments to understand how well AI can predict safety outcomes in health care settings.