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Matey, Eric

Mayo Clinic

Department of Pharmacy, Mayo Clinic, Rochester, MN, USA.

Citations 1

Pharmacists’ role in supporting the return of over 10,000 preemptive pharmacogenomics results: The Mayo Clinic experience
Mitaly, Serena, Wright, Jessica , Melik, Razan El, Matey, Eric T. , Melik, Razan M. El , El Melik, Razan, Matey, Eric, Wright, Jessica A , El Melik, Razan M

American Journal of Health-System Pharmacy | July 21, 2023 | Volume: 80 | Issue: 23 | Pages: 1733-1742

10.1093/ajhp/zxad159 Get It

Abstract Purpose To develop a pharmacist-driven, exploratory pharmacogenomics implementation model with the goal of creating a process for pharmacists to interpret pharmacogenomics results from RIGHT 10K Study samples and provide electronic consults to providers. Summary A train-the-trainer model program was initiated whereby pharmacogenomics pharmacists developed a documentation template and a quick reference guide as a standard guide to train other pharmacists. Pharmacists completed electronic consults (e-consults) reviewing pharmacogenomics results, with reference to drug-gene interactions, for patients with “semi-urgent” and “clinically actionable” results, defined as those indicating a potential for gene-drug interactions to cause major harm and those indicating a potential for an adverse drug reaction or reduced efficacy, respectively. Outcomes measured included the number of consults over time, number and role of pharmacists involved, average time to complete e-consults over time, and gene-drug pairs for semi-urgent consults per month. A total of 395 pharmacists were trained. The total number of e-consults completed was 2,843: 61 semi-urgent and 2,782 clinically actionable consults. The average time spent per consult was 24 minutes, and the average number of e-consults per pharmacist was 7. CYP2C19-clopidogrel was the most common gene-drug pair targeted in semi-urgent consults. Conclusion Pharmacy leaders planning to implement similar pharmacogenomics programs can utilize this data to estimate hiring needs for future pharmacogenomics implementation, while also considering the potential additional cost of developing resources.