Journal Article

Ability of pharmacy clinical decision-support software to alert users about clinically important drug—drug interactions

Kim R Saverno, Lisa E Hines, Terri L Warholak, Amy J Grizzle, Lauren Babits, Courtney Clark, Ann M Taylor and Daniel C Malone

in Journal of the American Medical Informatics Association

Published on behalf of American Medical Informatics Association

Volume 18, issue 1, pages 32-37
Published in print January 2011 | ISSN: 1067-5027
Published online December 2010 | e-ISSN: 1527-974X | DOI: http://dx.doi.org/10.1136/jamia.2010.007609
Ability of pharmacy clinical decision-support software to alert users about clinically important drug—drug interactions

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  • Medical Statistics and Methodology
  • Bioinformatics and Computational Biology
  • Biomathematics and Statistics

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Abstract

Objective Pharmacy clinical decision-support (CDS) software that contains drug–drug interaction (DDI) information may augment pharmacists' ability to detect clinically significant interactions. However, studies indicate these systems may miss some important interactions. The purpose of this study was to assess the performance of pharmacy CDS programs to detect clinically important DDIs.

Design Researchers made on-site visits to 64 participating Arizona pharmacies between December 2008 and November 2009 to analyze the ability of pharmacy information systems and associated CDS to detect DDIs. Software evaluation was conducted to determine whether DDI alerts arose from prescription orders entered into the pharmacy computer systems for a standardized fictitious patient. The fictitious patient's orders consisted of 18 different medications including 19 drug pairs—13 of which were clinically significant DDIs, and six were non-interacting drug pairs.

Measurements The sensitivity, specificity, positive predictive value, negative predictive value, and percentage of correct responses were measured for each of the pharmacy CDS systems.

Results Only 18 (28%) of the 64 pharmacies correctly identified eligible interactions and non-interactions. The median percentage of correct DDI responses was 89% (range 47–100%) for participating pharmacies. The median sensitivity to detect well-established interactions was 0.85 (range 0.23–1.0); median specificity was 1.0 (range 0.83–1.0); median positive predictive value was 1.0 (range 0.88–1.0); and median negative predictive value was 0.75 (range 0.38–1.0).

Conclusions These study results indicate that many pharmacy clinical decision-support systems perform less than optimally with respect to identifying well-known, clinically relevant interactions. Comprehensive system improvements regarding the manner in which pharmacy information systems identify potential DDIs are warranted.

Journal Article.  4489 words.  Illustrated.

Subjects: Medical Statistics and Methodology ; Bioinformatics and Computational Biology ; Biomathematics and Statistics

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