Using drug knowledgebase information to distinguish between look-alike-sound-alike drugs

Christine M. Cheng*, Alejandra Salazar, Mary G. Amato, Bruce L. Lambert, Lynn A. Volk, Gordon D. Schiff

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Objective: To extract drug indications from a commercial drug knowledgebase and determine to what extent drug indications can discriminate between look-alike-sound-alike (LASA) drugs. Methods: We extracted drug indications disease concepts from the MedKnowledge Indications module from First Databank Inc. (South San Francisco, CA) and associated them with drugs on the Institute for Safe Medication Practices (ISMP) list of commonly confused drug names. We used high-level concepts (rather than granular concepts) to represent the general indications for each drug. Two pharmacists reviewed each drug's association with its high-level indications concepts for accuracy and clinical relevance. We compared the high-level indications for each commonly confused drug pair and categorized each pair as having a complete overlap, partial overlap or no overlap in high-level indications. Results: Of 278 LASA drug pairs, 165 (59%) had no overlap and 58 (21%) had partial overlap in high-level indications. Fifty-five pairs (20%) had complete overlap in high-level indications; nearly half of these were comprised of drugs with the same active ingredient and route of administration (e.g., Adderall, Adderall XR). Conclusions: Drug indications data from a drug knowledgebase can discriminate between many LASA drugs.

Original languageEnglish (US)
Pages (from-to)872-884
Number of pages13
JournalJournal of the American Medical Informatics Association
Issue number7
StatePublished - Jul 1 2018


  • LASA
  • indications
  • look-alike-sound-alike
  • medication safety

ASJC Scopus subject areas

  • Health Informatics


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