Open Access
Use of an Innovative Pharmaceutical Class Scoring Tool for Prioritized Annual Formulary Review
Author(s) -
Holly Sheldon,
Audrey B Kostrzewa,
Shan L. Werner,
Terry Audley,
Adam Biggs,
Taylor Mancuso,
Mary Frances Picone
Publication year - 2022
Publication title -
innovations in pharmacy
Language(s) - English
Resource type - Journals
ISSN - 2155-0417
DOI - 10.24926/iip.v13i2.4785
Subject(s) - formulary , medicine , class (philosophy) , pharmacy , family medicine , computer science , artificial intelligence
Background: Though The Joint Commission requires health systems perform annual formulary review, guidance for how to perform this review is lacking. Published methods include comprehensive review of all pharmaceutical classes; however, this approach may not be the most efficient or effective option for a health system with a large formulary. Objective: To create a prioritization system for annual formulary review through development of a pharmaceutical class scoring tool. Methods: Drug information pharmacists developed the scoring tool, which used external and internal data to score pharmaceutical classes in 4 categories: safety, efficacy, cost, and utilization. The primary outcome, number of formulary changes resulting from pharmaceutical class review, was compared between the highest-scoring and lowest-scoring class to assess the tool's ability to prioritize high-yield class reviews. Results: The tool calculated scores for 91 pharmaceutical classes, altogether containing 962 medications. After review of the highest-scoring class, corticosteroids, 2 formulary changes were made: one dosage form was removed from formulary, and one medication was restricted to outpatient use only. Zero formulary changes resulted from review of the lowest-scoring class, pharmaceutical adjuvants. Conclusions: The tool described in this study prioritized annual formulary review efforts by identifying a pharmaceutical class with meaningful formulary optimization opportunities as the highest-scoring class, while correctly identifying a class with no optimization opportunities as the lowest-scoring class.