JOURNAL ARTICLE

Evaluation of an electronic health record Drug Interaction Customization Editor (DICE).

  • Published In: American Journal of Health-System Pharmacy, 2024, v. 81, n. 22. P. 1142 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Romero, Andrew; Gomez-Lumbreras, Ainhoa; Villa-Zapata, Lorenzo; Tan, Malinda; Horn, John; Malone, Daniel C. 3 of 3

Abstract

This article focuses on the development and evaluation of the Drug Interaction Customization Editor (DICE), a prototype tool designed to enable healthcare organizations to customize drug-drug interaction (DDI) alerts within electronic health record (EHR) systems. The DICE tool allows filtering of DDI warnings based on four sections—General, Medication, Patient, and Visit—using discrete EHR data such as drug properties, patient demographics, laboratory values, and encounter types. A survey of primarily pharmacists with informatics roles found high perceived usefulness for customizing alerts by patient attributes (e.g., age, weight), laboratory values (e.g., creatinine clearance, international normalized ratio), and medication characteristics (e.g., route of administration). Respondents indicated that implementing DICE could reduce alert fatigue and override rates, though concerns were raised about the effort required to maintain such customizations. The prototype aims to serve as a template for EHR vendors and users to develop more advanced, user-friendly DDI alert filtering systems that improve clinical relevance and patient safety.

Additional Information

  • Source:American Journal of Health-System Pharmacy. 2024/11, Vol. 81, Issue 22, p1142
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2024
  • ISSN:1079-2082
  • DOI:10.1093/ajhp/zxae169
  • Accession Number:180763802
  • Copyright Statement:Copyright of American Journal of Health-System Pharmacy is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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