Efficacy of Personal Pharmacogenomic Testing as an Educational Tool in the Pharmacy Curriculum: A Nonblinded, Randomized Controlled Trial


Personal genomic educational testing (PGET) has been suggested as a strategy to improve student learning for pharmacogenomics (PGx), but no randomized studies have evaluated PGET’s educational benefit. We investigated the effect of PGET on student knowledge, comfort, and attitudes related to PGx in a nonblinded, randomized controlled trial. Consenting participants were randomized to receive PGET or no PGET (NPGET) during 4 subsequent years of a PGx course. All participants completed a pre-survey and post-survey designed to assess (1) PGx knowledge, (2) comfort with PGx patient education and clinical skills, and (3) attitudes toward PGx. Instructors were blinded to PGET assignment. The Wilcoxon Rank Sum test was used to compare pre-survey and post-survey PGx knowledge, comfort, and attitudes. No differences in baseline characteristics were observed between PGET (n = 117) and NPGET (n = 116) participants. Among all participants, significant improvement was observed in PGx knowledge (mean 57% vs. 39% correct responses; p < 0.001) with similar results for student comfort and attitudes. Change in pre/post-PGx knowledge, comfort, and attitudes were not significantly different between PGET and NPGET groups (mean 19.5% vs. 16.7% knowledge improvement, respectively; p = 0.41). Similar results were observed for PGET participants carrying a highly actionable PGx variant versus PGET participants without an actionable variant. Significant improvement in Likert scale responses were observed in PGET versus NPGET for questions that assessed student engagement (p = 0.020) and reinforcement of course concepts (p = 0.006). Although some evidence of improved engagement and participation was observed, the results of this study suggest that PGET does not directly improve student PGx knowledge, comfort, and attitudes.

Clinical and Translational Science
Heidi E. Steiner
Heidi E. Steiner
Senior Clinical Data Scientist

I love coding in R, biostatistics, and reproducible clinical research.