JOURNAL ARTICLE

Effect on medication adherence of applying a specialty pharmacy care model to nonspecialty medications: A quasi-experimental cohort study.

  • Published In: American Journal of Health-System Pharmacy, 2023, v. 80. P. S135 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: James, Gerald J St.; Duckworth, Deborah L; Bochenek, Samantha H; Rhudy, Christian; Zeltner, Matthew; Tagavi, Anthony B; Platt, Thom L 3 of 3

Abstract

This article examines the impact of applying a specialty pharmacy care model to patients receiving nonspecialty medications within a health system. The study compared medication adherence, measured by proportion of days covered (PDC), before and after transferring patients from a health-system retail pharmacy to a health-system specialty pharmacy (HSSP) between April 2020 and June 2021. Results from 163 patients showed a significant 7.0% increase in mean PDC post-transfer, along with higher proportions of patients achieving adherence thresholds of 80% and 90%, fewer shipments per patient, more medications per shipment, and reduced patient copays. The findings suggest that the specialty pharmacy care model—which includes services such as benefits investigation, financial assistance, and proactive refill reminders—may improve adherence and reduce costs for nonspecialty medication patients, though further research is needed to identify which medication classes and patient populations benefit most.

Additional Information

  • Source:American Journal of Health-System Pharmacy. 2023/12, Vol. 80, pS135
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2023
  • ISSN:1079-2082
  • DOI:10.1093/ajhp/zxad040
  • Accession Number:173808266
  • 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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