Literature DB >> 30257842

Detection of potential look-alike/sound-alike medication errors using Veterans Affairs administrative databases.

Jessica M Zacher1, Francesca E Cunningham1, Xinhua Zhao2, Muriel L Burk1, Von R Moore1, Chester B Good3, Peter A Glassman4, Sherrie L Aspinall5.   

Abstract

PURPOSE: Results of a study to estimate the prevalence of look-alike/sound-alike (LASA) medication errors through analysis of Veterans Affairs (VA) administrative data are reported.
METHODS: Veterans with at least 2 filled prescriptions for 1 medication in 20 LASA drug pairs during the period April 2014-March 2015 and no history of use of both medications in the preceding 6 months were identified. First occurrences of potential LASA errors were identified by analyzing dispensing patterns and documented diagnoses. For 7 LASA drug pairs, potential errors were evaluated via chart review to determine if an actual error occurred.
RESULTS: Among LASA drug pairs with overlapping indications, the pairs associated with the highest potential-error rates, by percentage of treated patients, were tamsulosin and terazosin (3.05%), glipizide and glyburide (2.91%), extended- and sustained-release formulations of bupropion (1.53%), and metoprolol tartrate and metoprolol succinate (1.48%). Among pairs with distinct indications, the pairs associated with the highest potential-error rates were tramadol and trazodone (2.20%) and bupropion and buspirone (1.31%). For LASA drug pairs found to be associated with actual errors, the estimated error rates were as follows: lamivudine and lamotrigine, 0.003% (95% confidence interval [CI], 0-0.01%); carbamazepine and oxcarbazepine, 0.03% (95% CI, 0-0.09%); and morphine and hydromorphone, 0.02% (95% CI, 0-0.05%).
CONCLUSION: Through the use of administrative databases, potential LASA errors that could be reviewed for an actual error via chart review were identified. While a high rate of potential LASA errors was detected, the number of actual errors identified was low.
Copyright © 2018 by the American Society of Health-System Pharmacists, Inc. All rights reserved.

Entities:  

Keywords:  Veterans Administration; database management systems; medical order-entry systems; medication errors

Mesh:

Year:  2018        PMID: 30257842     DOI: 10.2146/ajhp170703

Source DB:  PubMed          Journal:  Am J Health Syst Pharm        ISSN: 1079-2082            Impact factor:   2.637


  3 in total

1.  Identification and safe storage of look-alike, sound-alike medicines in automated dispensing cabinets.

Authors:  Henna Karoliina Ruutiainen; Miia Marjukka Kallio; Sini Karoliina Kuitunen
Journal:  Eur J Hosp Pharm       Date:  2021-01-15

2.  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

3.  Identification of medication errors through a monitoring and minimization program in outpatients in Colombia, 2018-2019

Authors:  Manuel Enrique Machado-Duque; Jorge Enrique Machado-Alba; Andrés Gaviria-Mendoza; Luis Fernando Valladales-Restrepo; Ilsa Yadira Parrado-Fajardo; Mauren Ospina-Castellanos; Luisa Fernanda Rojas-Chavarro; John Alexander López-Rincón
Journal:  Biomedica       Date:  2021-03-19       Impact factor: 0.935

  3 in total

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