Literature DB >> 26534995

Tall Man lettering and potential prescription errors: a time series analysis of 42 children's hospitals in the USA over 9 years.

Wenjun Zhong1, James A Feinstein2, Neil S Patel3, Dingwei Dai1, Chirs Feudtner4.   

Abstract

BACKGROUND: Despite the widespread implementation of Tall Man lettering, little evidence exists regarding whether this technique has reduced drug errors due to look-alike sound-alike (LA-SA) drug names. This study evaluated rates of potential LA-SA drug errors in the drug management process through to the point of dispensing before and after implementation of Tall Man lettering in 2007.
METHODS: We used detailed pharmacy data for paediatric inpatients (<21 years old) from 42 children's hospitals in 2004-2012. After prespecifying a set of 8 potential LA-SA drug error patterns we searched within each hospitalisation for the occurrence of one of these patterns for a total of 12 LA-SA drug pairs deemed highly relevant to paediatric inpatients. To assess for potential change of error rates before and after Tall Man lettering implementation, we performed segmented regression analyses for each of 11 LA-SA drug pairs (because 1 pair had no detected potential errors) and for the overall total errors of all 11 LA-SA drug pairs.
RESULTS: Among 1 676 700 hospitalisations, no statistically significant change was detected for either the intercept or the slope of LA-SA error rate for each of the 11 drug pairs or for the combined error rate. In a sensitivity analysis of the moving average of the potential error rate over the entire study period, no downward trend in potential LA-SA drug error rates was evident over any time period 2004 onwards.
CONCLUSIONS: Implementation of Tall Man lettering in 2007 was not associated with a reduction in the potential LA-SA error rate. Whether Tall Man lettering is effective in clinical practice warrants further study. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Entities:  

Keywords:  Hospital medicine; Medication safety; Paediatrics

Mesh:

Substances:

Year:  2015        PMID: 26534995     DOI: 10.1136/bmjqs-2015-004562

Source DB:  PubMed          Journal:  BMJ Qual Saf        ISSN: 2044-5415            Impact factor:   7.035


  10 in total

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2.  A national e-Delphi towards the measurement of safe medication practices in Portuguese hospitals.

Authors:  Mara Pereira Guerreiro; Madalena Plácido; Carla Teixeira Barros; Anabela Coelho; Anabela Graça; Maria João Gaspar; Sofia de Oliveira Martins
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3.  Dispensing errors from look-alike drug trade names.

Authors:  Hsiang-Yi Tseng; Chen-Fan Wen; Ya-Lun Lee; Kee-Ching Jeng; Pei-Liang Chen
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4.  Preventing dispensing errors by alerting for drug confusions in the pharmacy information system-A survey of users.

Authors:  Zizi Campmans; Arianne van Rhijn; René M Dull; Jacqueline Santen-Reestman; Katja Taxis; Sander D Borgsteede
Journal:  PLoS One       Date:  2018-05-29       Impact factor: 3.240

5.  Automated detection of wrong-drug prescribing errors.

Authors:  Bruce L Lambert; William Galanter; King Lup Liu; Suzanne Falck; Gordon Schiff; Christine Rash-Foanio; Kelly Schmidt; Neeha Shrestha; Allen J Vaida; Michael J Gaunt
Journal:  BMJ Qual Saf       Date:  2019-08-07       Impact factor: 7.035

6.  A survey about label enhancement methods for parenteral medication in European hospital pharmacies.

Authors:  K H M Larmené-Beld; R N Keers; K Taxis
Journal:  Eur J Clin Pharmacol       Date:  2020-06-20       Impact factor: 2.953

7.  Made to Measure: The Ethics of Routine Measurement for Healthcare Improvement.

Authors:  Polly Mitchell; Alan Cribb; Vikki Entwistle
Journal:  Health Care Anal       Date:  2020-12-20

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

Authors:  Karin H M Larmené-Beld; E Kim Alting; Katja Taxis
Journal:  Eur J Clin Pharmacol       Date:  2018-05-12       Impact factor: 2.953

9.  A drug identification model developed using deep learning technologies: experience of a medical center in Taiwan.

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Review 10.  A systematic literature review of LASA error interventions.

Authors:  Rachel Bryan; Jeffrey K Aronson; Alison J Williams; Sue Jordan
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  10 in total

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