Clinical pharmacogenetics: A feasibility study of pharmacy students using a clinical decision support system during their didactic training
Bibliographic record
Abstract
Background: Pharmacogenetic testing in clinical practice is feasible and cost-effective, and positively impacts healthcare quality and costs. Integrating pharmacogenetics into the pharmacy setting has shown promise in improving medication safety, effectiveness, and efficiency. Implementation across health care settings, however, continues to be limited. Pharmacy students’ ability to utilize pharmacogenetic information in their decision-making processes may be enhanced with a software-based clinical decision support system (CDSS). Aims: To evaluate the feasibility of incorporating a pharmacogenetic CDSS into pharmacy student education. To evaluate the impact of a CDSS compared with usual decision-making methods (UDM) on appropriateness of medication selection and efficiency of pharmacy students’ medication decision-making. Methods: A cross-sectional study design was employed, including a controlled crossover trial with two intervention arms, CDSS and UDM, and a nested qualitative study to explore student perceptions. Fifty-one third-year pharmacy students were recruited and participated in two clinical scenarios, alternating between CDSS and UDM methods. Performance outcomes were assessed based on the appropriateness of therapeutic recommendations and the efficiency of therapeutic decision making. An online survey was conducted to gather students’ feedback on using the CDSS. Results: In Scenario 1 (antiplatelet therapy), 89% of students in the CDSS group selected the optimal drug (ticagrelor) compared to 39% in the UDM group (p<0.001). In Scenario 2 (behavioral health therapy), 50% of students in the CDSS group chose the optimal drug versus 41% in the UDM group, and students in the CDSS group chose fewer drugs (4 drugs) than students in the UDM group (10 drugs). For both scenarios, students in the CDSS group used only the CDSS as a resource, while the UDM group used multiple resources. The survey revealed high student satisfaction, with 94% of the students finding the CDSS useful. Conclusions: The study demonstrates that, compared to UDM, a CDSS improves the appropriateness and efficiency of pharmacy students’ therapeutic recommendations. Integrating a CDSS into pharmacy education can enhance students’ competency in utilizing pharmacogenetic information, advancing personalized medicine and optimizing patient care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".