JOURNAL ARTICLE

Minimization of preventable drug waste through use of a vial combination calculator tool.

  • Published In: American Journal of Health-System Pharmacy, 2024, v. 81, n. 11. P. e311 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Song, Ju Young; Wysocki, Mark; Chen, Franklin; Martinez, Dylcia; Cabie, Eric 3 of 3

Abstract

This article focuses on the development and implementation of a Microsoft Excel–based automated vial selection calculator designed to minimize preventable drug waste in outpatient oncology pharmacies. Targeting 23 high-cost chemotherapy and monoclonal antibody medications packaged in single-dose vials of varying sizes, the tool was introduced across 11 outpatient pharmacies within a large oncology network alongside monthly staff education. Following implementation, preventable drug waste costs and suboptimal vial combination selections decreased by approximately 51% and 54%, respectively, in fiscal year 2022, with continued reductions observed in fiscal year 2023. The study highlights that combining automation with a culture emphasizing waste reduction can significantly improve pharmaceutical cost efficiency, though limitations include manual data entry and lack of integration with electronic medical records (EMR) and inventory systems. Future recommendations include developing fully integrated systems to automate vial selection from order entry to product dispensing to further optimize waste reduction and operational efficiency.

Additional Information

  • Source:American Journal of Health-System Pharmacy. 2024/06, Vol. 81, Issue 11, pe311
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2024
  • ISSN:1079-2082
  • DOI:10.1093/ajhp/zxae023
  • Accession Number:177467827
  • 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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