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

ABSTRACT: Unclear 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.A 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.Sixteen 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.Studies 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.

SUBMITTER: Larmene-Beld KHM

PROVIDER: S-EPMC6061459 | biostudies-other | 2018 May

REPOSITORIES: biostudies-other

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

Larmené-Beld Karin H M KHM Alting E Kim EK Taxis Katja K

European journal of clinical pharmacology 20180512 8

<h4>Purpose</h4>Unclear 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.<h4>Methods</h4>A 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 readab ...[more]

PMID: 29754215

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Project description:While the importance of random sequencing errors decreases at higher DNA or RNA sequencing depths, systematic sequencing errors (SSEs) dominate at high sequencing depths and can be difficult to distinguish from biological variants. These SSEs can cause base quality scores to underestimate the probability of error at certain genomic positions, resulting in false positive variant calls, particularly in mixtures such as samples with RNA editing, tumors, circulating tumor cells, bacteria, mitochondrial heteroplasmy, or pooled DNA. Most algorithms proposed for correction of SSEs require a training data set, which is typically either from a part of the data set being M-bM-^@M-^\recalibratedM-bM-^@M-^] (Genome Analysis ToolKit, or GATK) or from a separate data set with special characteristics (SysCall). Here, we combine the advantages of these approaches by adding synthetic RNA spike-in standards to human RNA, and use GATK to recalibrate base quality scores with reads mapped to the spike-in standards. Compared to conventional GATK recalibration that uses reads mapped to the genome, spike-ins improve the accuracy of Illumina base quality scores by a mean of 5 units, and by as much as 13 units M-BM- at CpG sites. In addition, since reads mapping to the genome are not used for recalibration, our method allows run-specific recalibration even for the many species without a comprehensive and accurate SNP database. We also use GATK with the spike-in standards to demonstrate that the Illumina RNA sequencing runs overestimate quality scores for AC, CC, GC, GG, and TC dinucleotides, while SOLiD has less dinucleotide SSEs but more SSEs for certain cycles. We conclude that using these DNA and RNA spike-in standards with GATK improves base quality score recalibration. Four human RNA samples with equimolar ERCC spike-in standards were sequenced on Illumina. Two human brain/liver/muscle RNA mixtures with dynamic range of ERCC spike-in standards were sequenced on SOLiD.

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

Publications

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

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