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.
Jacobs S, Hann M, Bradley F, et al. Res Soc Admin Pharm. 2020;16:895-903.
This study evaluated cross-sectional survey data from pharmacists and patients to characterize organizational factors associated with variation in safety climate, patient satisfaction and self-reported medication adherence in community pharmacies in the United Kingdom. Safety climate was associated with pharmacy ownership, organizational culture, working hours, and employment of accuracy checkers. Skill mix and continuity of care also influenced safety culture and quality.
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