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Predicting look-alike and sound-alike medication errors

B L Lambert1

  • 1Department of Pharmacy Administration, University of Illinois at Chicago 60612-7231, USA. lambertb@uic.edu

American Journal of Health-System Pharmacy : AJHP : Official Journal of the American Society of Health-System Pharmacists
|May 15, 1997
PubMed
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Automated analysis of medication name spelling (orthographic similarity) can predict drug errors. This model identifies confusing drug names, significantly reducing medication errors and improving patient safety.

Area of Science:

  • Pharmacovigilance
  • Health Informatics
  • Drug Safety

Background:

  • Medication errors frequently arise from look-alike and sound-alike drug names.
  • Current methods for ensuring drug nomenclature safety and identifying confusing names are limited.

Purpose of the Study:

  • To develop and evaluate an automated model for predicting medication name confusion.
  • To assess the relationship between orthographic similarity and medication errors.

Main Methods:

  • Three quantitative measures of orthographic similarity (bigram, trigram, Levenshtein distance) were used.
  • Case-control studies compared similarity scores of error-prone drug pairs with control pairs.
  • Predictive tests for medication name confusion were developed and validated.

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Main Results:

  • Orthographic similarity was a significant risk factor for medication errors.
  • Drug name pairs exceeding similarity thresholds were 25-523 times more likely to cause errors.
  • A prognostic test achieved 91% accuracy, with 84% sensitivity and 99% specificity.

Conclusions:

  • Automated orthographic similarity measures can accurately predict potential medication name confusion.
  • This approach offers a sensitive and specific tool for enhancing drug safety and preventing errors.