Is P&T Ready to Add Rapid Cycle Analytics to Formulary?. (October 2021)

• Record Type: Journal Article
• Authors: Altshuler, Diana; Yu, Kenny; Papadopoulos, John; Dabestani, Arash
• Is Part Of: Hospital pharmacy. Volume 56:Number 5(2021)
• Page Start: 430
• Page End: 435
• Publication Date: 2021-10
• Subjects: formulary management/P&T -- benchmarking -- drug/medical use evaluation -- data analytics -- real world evidence -- rapid cycle analytics; Hospital pharmacies -- Periodicals; Pharmacy Service, Hospital; Hospital pharmacies; Periodicals; Periodicals; 615.1
• DOI: 10.1177/0018578720918341
• Languages: English
• ISSNs: 0018-5787

Abstract

Purpose: The intent of this article is to evaluate a novel approach, using rapid cycle analytics and real world evidence, to optimize and improve the medication evaluation process to help the formulary decision making process, while reducing time for clinicians.Summary: The Pharmacy and Therapeutics (P&T) Committee within each health system is responsible for evaluating medication requests for formulary addition. Members of the pharmacy staff prepare the drug monograph or a medication use evaluation (MUE) and allocate precious clinical resources to review patient charts to assess efficacy and value. We explored a novel approach to evaluate the value of our intravenous acetaminophen (IV APAP) formulary admittance. This new methodology, called rapid cycle analytics, can assist hospitals in meeting and/or exceeding the minimum criteria of formulary maintenance as defined by the Joint Commission Standards. In this particular study, we assessed the effectiveness of IV APAP in total hip arthroplasty (THA) and total knee arthroplasty (TKA) procedures. We assessed the correlation to same-stay opioid utilization, average length of inpatient stay and post anesthesia care unit (PACU) time.Conclusion: We were able to explore and improve our organization's approach in evaluating medications by partnering with an external analytics expert to help organize and normalize our data in a more robust, yet time efficient manner. Additionally, we were able to use a significantly larger external data set as a point of reference. Being able to perform this detailed analytical exercise for thousands of encounters internally and using a data warehouse of over 130 million patients as a point of reference in a short time has improved the depth of our assessment, as well as reducing valuable clinical resources allocated to MUEs to allow for more direct patient care. This clinically real-world and data-rich analytics model is the necessary foundation for using Artificial or Augmented Intelligence (AI) to make real-time formulary and drug selection decisions