Detecting drug interactions using personal digital assistants in an out-patient clinic

QJM. 2007 Nov;100(11):691-7. doi: 10.1093/qjmed/hcm088. Epub 2007 Oct 11.

Abstract

Background: The installation of drug databases on personal digital assistants (PDAs) allows for rapid detection of adverse drug interactions at the point of care.

Aim: To test the ability of a drug interaction database (ePocrates RX) to correctly identify clinically significant adverse drug interactions in an out-patient setting.

Design: Retrospective file review of 1801 drug prescriptions in out-patients consulting a medical walk-in clinic.

Methods: Each prescription was assessed independently by a clinical pharmacologist using drug-drug interaction compendia, and by a general internist using the drug interaction database. Discrepant results were systematically reviewed by both, using published literature, and a consensus was then reached. This consensus was used as the criterion against which the PDA drug interaction database was judged.

Results: The prevalence of potential adverse drug interactions was 23%. When compared to the opinion of the clinical pharmacologist and drug-drug interaction compedia, the sensitivity of the drug interaction database to correctly identify clinically relevant adverse drug interactions was 81% (95%CI 77%-85%) and the specificity was 88% (95%CI 86-89%). The positive predictive value was poor (67%, 95%CI 62%-71%) but the negative predictive value was excellent (94%, 95%CI 92%-95%).

Discussion: The database was an efficient tool for rapidly checking for potentially harmful drug interaction, but also flagged up several clinically non-significant interactions. When used appropriately, this drug interaction database could help physicians decrease prescription error, by ruling out the risk of clinically relevant adverse drug interactions for newly prescribed drugs, and thereby increase patient safety.

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Aged, 80 and over
  • Computers, Handheld* / standards
  • Drug Interactions*
  • Female
  • Humans
  • Information Systems*
  • Male
  • Medication Errors / prevention & control*
  • Middle Aged
  • Point-of-Care Systems
  • Retrospective Studies
  • Sensitivity and Specificity
  • Software / standards*