Automated detection of look-alike/sound-alike medication errors

Christine Rash-Foanio, William Galanter, Michelle Bryson, Suzanne Falck, King Lup Liu, Gordon D. Schiff, Allen Vaida, Bruce L. Lambert*

*Corresponding author for this work

Research output: Contribution to journalArticle

17 Scopus citations


Purpose. The development and evaluation of an algorithm for detecting potential medication errors due to look-alike/sound-alike (LASA) drug names are described. Summary. A computer algorithm that detects potential LASA errors by analyzing medication orders and diagnostic claims data was developed. The algorithm flags a potential error when (1) a medication order is not justified by a diagnosis documented in the patient's record, (2) another medication whose orthographic similarity to the index drug exceeds a specified threshold exists, and (3) the latter drug has an indication that matches an active documented diagnosis. A review of medication orders and diagnostic claims at a large health system identified cases in which cycloserine was ordered but cyclosporine was the intended treatment. Subsequent review of all cycloserine orders over a 7-year period indicated that 11 of 16 orders were erroneous, prompting placement of an alert regarding the potential for LASA errors involving cycloserine and cyclosporine in the electronic order-entry system. Automated detection and confirmation of LASA errors via chart review can be used retrospectively to identify problematic pairs of drug names and to assess associated error rates within a healthcare system. The same techniques can be used to prevent errors in real time through indication alerts if accurate diagnostic information is available at the time of order entry. Conclusion. Automated methods involving the use of medication orders, diagnostic claims, and indications can be used to detect and prevent LASA errors.

Original languageEnglish (US)
Pages (from-to)521-527
Number of pages7
JournalAmerican Journal of Health-System Pharmacy
Issue number7
StatePublished - Apr 1 2017


  • Cycloserine
  • Cyclosporine
  • Electronic prescribing
  • Medication errors
  • Patient harm
  • Systematized nomenclature of medicine

ASJC Scopus subject areas

  • Pharmacology
  • Health Policy

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  • Cite this

    Rash-Foanio, C., Galanter, W., Bryson, M., Falck, S., Liu, K. L., Schiff, G. D., Vaida, A., & Lambert, B. L. (2017). Automated detection of look-alike/sound-alike medication errors. American Journal of Health-System Pharmacy, 74(7), 521-527.