The AHRQ PSNet Collection comprises an extensive selection of resources relevant to the patient safety community. These resources come in a variety of formats, including literature, research, tools, and Web sites. Resources are identified using the National Library of Medicine’s Medline database, various news and content aggregators, and the expertise of the AHRQ PSNet editorial and technical teams.
Choudhury A, Asan O. JMIR Med Inform. 2020;8:e18599.
This systematic review explored how artificial intelligence (AI) based on machine learning algorithms and natural language processing is used to address and report patient safety outcomes. The review suggests that AI-enabled decision support systems can improve error detection, patient stratification, and drug management, but that additional evidence is needed to understand how well AI can predict safety outcomes.
Härkänen M, Turunen H, Vehviläinen-Julkunen K. J Patient Saf. 2020;16.
This study compared medication errors detected using incident reports, the Global Trigger Tool method, and direct observations of patient records. Incident reports and the Global Trigger Tool more commonly identified medication errors likely to cause harm. Omission errors were commonly identified by all three methods, but identification of other errors varied. For example, incident reports most commonly identified wrong dose and wrong time errors. The contributing factors also varied by method, but in general, communication issues and human factors were the most common contributors.
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