Document Type : Letter to Editor
Authors
1 Students Research Committee, Qom University of Medical Sciences, Qom, Iran
2 Department of Physiology and Pharmacology, Faculty of Medicine, Neuroscience Research Center, Qom University of Medical Sciences, Qom, Iran
Abstract
The National Coordinating Council on Medication Error Reporting and Prevention (NCC MERP) defines a medication error (ME) as any preventable incident that could lead to the incorrect use of a medication or to patient harm at any point (1). Medication errors are a major healthcare issue, leading to severe problems like death, disability, and long-term harm. The most common preventable cause is side effects, accounting for 10–18% of hospital injuries (2,3). A study in England reports that about 237 million medication errors occur during various stages of the medication process, with 38.4% of them taking place in primary care. Although 72% of these errors had a minimal chance of causing harm, around 66 million were clinically significant. In primary care, prescribing was responsible for 34% of all potentially clinically important errors. It is estimated that Adverse Drug Events (ADEs) cost the NHS approximately 98.5 million pounds annually and cause 1,708 deaths (4).
Artificial Intelligence (AI) technologies can help prevent medication errors by offering decision support to clinicians. By examining patient information—like medical history, current medications, allergies, and potential drug interactions—AI algorithms can help propose suitable drug options and dosages (5). This directly addresses the problem of medication errors by minimizing the risk of incorrect prescriptions. Research shows that AI can enhance medication safety by predicting potential adverse drug events, which directly links it back to the importance of reducing medication errors (6). By integrating AI into clinical workflows, healthcare providers can identify discrepancies and errors more effectively, which improves overall patient safety.
Keywords
Main Subjects
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