Look-alike or sound-alike (LASA) medication names may be mistaken for each other, e.g. mercaptamine and mercaptopurine. If an error of this sort is not intercepted, it can reach the patient and may result in harm. LASA errors occur because of shared linguistic properties between names (phonetic or orthographic), and potential for error is compounded by similar packaging, tablet appearance, tablet strength, route of administration or therapeutic indication. Estimates of prevalence range from 0.00003 to 0.0022% of all prescriptions, 7% of near misses, and between 6.2 and 14.7% of all medication error events. Solutions to LASA errors can target people or systems, and include reducing interruptions or distractions during medication administration, typographic tweaks, such as selective capitalization (Tall Man letters) or boldface, barcoding, and computerized physician order entry.
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http://dx.doi.org/10.1111/bcp.14285 | DOI Listing |
Lancet Reg Health Southeast Asia
July 2024
Independent Economist, India.
Poor drug regulation in India is not a recent problem. The Indian drug market is full of look-alike, sound-alike (LASA) drugs which have not yet caught the attention of the media or the medical community. This viewpoint highlights the problem of LASA drugs and poor prescription practices and proposes solutions for involving all stakeholders in this unaddressed issue which is a huge public health problem in India.
View Article and Find Full Text PDFCurr Aging Sci
June 2024
Department of Clinical Pharmacology, Evidence-Based Therapeutics Group, Faculty of Medicine, Universidad de La Sabana and Clínica Universidad de La Sabana, Campus del Puente del Común, Km. 7, Autopista Norte de Bogotá. Chía, Cundinamarca, 250001, Colombia.
Background: Elderly people have multiple comorbidities that often require treatment with multiple medications. Having strategies to lessen the risks associated with pharmacological interactions and potentially inadequate prescribing (PIP) is of major importance. The STOPP- START criteria are useful in identifying PIP along with other tools, such as LASA (look alike/sound alike) drugs and high-risk medications (HRM).
View Article and Find Full Text PDFComput Methods Programs Biomed
January 2024
College of Medical Science and Technology, Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan; International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan; Research Center of Big Data and Meta-analysis, Wanfang Hospital, Taipei Medical University, Taipei, Taiwan; Department of Dermatology, Wanfang Hospital, Taipei Medical University, Taiwan. Electronic address:
Background And Objective: The overall benefits of using clinical decision support systems (CDSSs) can be restrained if physicians inadvertently ignore clinically useful alerts due to "alert fatigue" caused by an excessive number of clinically irrelevant warnings. Moreover, inappropriate drug errors, look-alike/sound-alike (LASA) drug errors, and problem list documentation are common, costly, and potentially harmful. This study sought to evaluate the overall performance of a machine learning-based CDSS (MedGuard) for triggering clinically relevant alerts, acceptance rate, and to intercept inappropriate drug errors as well as LASA drug errors.
View Article and Find Full Text PDFConfusion of drug names has been identified as a leading cause of medication errors and potential iatrogenic harm. Most of these errors occur because of look-alike or sound-alike drugs. This case series gives examples of duplication errors due to brand confusion, where there are no similarities in the names.
View Article and Find Full Text PDFInt J Clin Pharm
February 2024
College of Medicine and Dentistry, James Cook University, Townsville, Australia.
Background: Look-alike medications, where ampoules or vials of intravenous medications look similar, may increase the risk of medication errors in the perioperative setting.
Aim: This scoping review aimed to identify and explore the issues related to look-alike medication incidents in the perioperative setting and the reported risk reduction interventions.
Method: Eight databases were searched including: CINAHL Complete, Embase, OVID Emcare, Pubmed, Scopus, Informit, Cochrane and Prospero and reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR).
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