A systematic literature review on strategies to avoid look-alike errors of labels

Abstract

PurposeUnclear labeling has been recognized as an important cause of look-alike medication errors. The aim of this literature review is to systematically evaluate the current evidence on strategies to minimize medication errors due to look-alike labels.MethodsA literature search of PubMed and EMBASE for all available years was performed independently by two reviewers. Original studies assessing strategies to minimize medication errors due to look-alike labels focusing on readability of labels by health professionals or consumers were included. Data were analyzed descriptively due to the variability of study methods.ResultsSixteen studies were included. Thirteen studies were performed in a laboratory and three in a healthcare setting. Eleven studies evaluated Tall Man lettering, i.e., capitalizing parts of the drug name, two color-coding, and three studies other strategies. In six studies, lower error rates were found for the Tall Man letter strategy; one showed significantly higher error rates. Effects of Tall Man lettering on response time were more varied. A study in the hospital setting did not show an effect on the potential look-alike sound-alike error rate by introducing Tall Man lettering. Color-coding had no effect on the prevention of syringe-swaps in one study.ConclusionsStudies performed in laboratory settings showed that Tall Man lettering contributed to a better readability of medication labels. Only few studies evaluated other strategies such as color-coding. More evidence, especially from real-life setting is needed to support safe labeling strategies.Electronic supplementary materialThe online version of this article (10.1007/s00228-018-2471-z) contains supplementary material, which is available to authorized users.

A systematic review and meta-analysis of microbial contamination of parenteral medication prepared in a clinical versus pharmacy environment

“…Finally, more work needs to be done to address the clinical implications of contaminated medications. This is an important aspect of patient safety of parenteral products and it should be aligned with other initiatives such as good prescribing [56], safe labelling [57], and technologies to improve the administration of medications, for example, a bar-code administration system [58,59].…”

Identification of potential drug name confusion errors in the Sentinel System

“…For example, Galanter and colleagues examined the use of indication alerts at the time of computerized physician order entry and found that some errors were averted . Manufacturers use “tall‐man” lettering to differentiate sections of a medication name on the medication's packaging, labeling, and bottle to decrease the confusion (eg, ARIPiprazole and RABEprazole ), though the effectiveness of this methodology has not been clearly demonstrated in real‐world settings …”

“…12 Manufacturers use "tall-man" lettering to differentiate sections of a medication name on the medication's packaging, labeling, and bottle to decrease the confusion (eg, ARIPiprazole and RABEprazole), 20 though the effectiveness of this methodology has not been clearly demonstrated in real-world settings. 21,22 The strengths of our analysis include the large size of the Sentinel System and the ability to deploy readily available analytic tools to facilitate efficient identification and review of potential LASA errors. There are important limitations to consider as well.…”

Prevention strategies to identify LASA errors: building and sustaining a culture of patient safety

“…Most prevention strategies to identify LASA errors have been developed at hospital setting. Some of the strategies that have been proposed and tested to minimize risks include: 1) computer algorithms that detect potential LASA errors by analyzing names, medication orders and diagnostic claims data [18]; 2) changes in the appearance of labeling and packaging of LASA medicines, paying special attention to their differences (for example the use of uppercase-lowercase letters, boldface or coloured letters to highlight the differences between names; coloured labeling and contrasting background) [19,20]; 3) inclusion of stickers with security symbols or pictograms on the packaging [19,20]; 4) re-packaging [17]; 5) bar-code-assisted medication administration [5]; 6) legible manual prescription avoiding abbreviations [3]; 7) avoiding oral and vague prescription [19]; 8) changing order and separate storage of products with similar name, with special attention in case of medicines with narrow therapeutical margin [19]; 9) reminders and alerts for LASA products [19]; 10) correct verification before dispensing and/or administration [5,19]; 11) the joint use of the brand name and the generic name (in brackets) in prescriptions and drug labeling [19]; 12) review of new medications added in formularies and changes in packaging resulting from contract changes or drug shortages [19].…”

“…At present, the emerging strategies that could be implemented in short and mid-term are the following: 1) the configuration of computer selection screens and drop down menus in prescription systems to prevent LASA names from appearing adjacent to each other and thus avoid errors; 2) the design of an international system for the validation of names for new drugs/medicines which bears the issue of possible confusions in mind; 3) automated dispensing by means of electronic devices and serialization technology (DataMatrix code), which makes the healthcare supply chain safer as well as more efficient and accurate; 4) use of a closed-loop system with barcode technology to enhance the readability of look-alike labels [20]; 5) consideration of potential LASA errors when ordering stocks; 6) stringent feedback of LASA issues to FDA, EMA and pharmaceutical industry via pharmacists from hospitals and other health professionals; 7) developing strategies to involve patients and their caregivers in reducing risks through information (for example by means of apps that specify the indication for which the medicine has been prescribed and expected appearance, nonproprietary and brand names and potential medication side effects). Artificial intelligence and automated methods will be one of the most accurate tools in the next future to avoid LASA errors and ME in general by minimizing human factors risk.…”