JAMIA: Pharmacy CDS tools spotty at identifying drug-drug interactions

I had wanted to write an article on CDS (a previous project I worked got me involved in CDSS for quite a fair bit), hence I thought this article from CMIO would serve as a great way to introduce the topic to you readers 🙂

Many pharmacy clinical decision-support (CDS) systems don’t always identify well-known, clinically relevant drug-drug interactions (DDIs), according to a study in the January edition of Journal of the American Medical Informatics Association.

Pharmacy CDS software that contains DDI information can augment pharmacists’ ability to detect clinically significant interactions, wrote lead author Kim R. Saverno, a graduate assistant in the Department of Pharmacy Practice and Science at the University of Arizona College of Pharmacy in Tucson, and colleagues. “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.”

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 that were non-interacting drug pairs.

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

Only 18 (28 percent) of the 64 pharmacies correctly identified eligible interactions and non-interactions, the authors found. The median percentage of correct DDI responses was 89 percent (range 47–100 percent) 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).

“Comprehensive system improvements regarding the manner in which pharmacy information systems identify potential DDIs are warranted,” the authors concluded.

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