Implementation and Evaluation Strategies for Pharmacogenetic Testing in Hospital Settings: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Pharmacogenetic testing in clinical settings has improved the safety and efficacy of drug treatment. There is a growing number of studies evaluating pharmacogenetic implementation and identifying barriers and facilitators. However, no review has focused on bridging the gap between identifying barriers and facilitators of testing and the clinical strategies adopted in response. This review was conducted to understand the implementation and evaluation strategies of pharmacogenetic testing programs. METHODS: A PRISMA-compliant scoping review was conducted. The included studies discussed pharmacogenetic testing programs implemented in a hospital setting. Quantitative, qualitative, and mixed design methods were included. RESULTS: A total of 232 of the 7043 articles that described clinical pharmacogenetic programs were included. The most common specialties that described pharmacogenetic implementation were psychiatry (26%) and oncology (16%), although many studies described institutional programs implemented across multiple specialties (19%). Different specialties reported different clinical outcomes, but all reported similar program performance indicators, such as test uptake and the number of times the test recommendations were followed. There were benefits and drawbacks to delivering test results through research personnel, pharmacists, and electronic alerts, but active engagement of physicians was necessary for the incorporation of pharmacogenetic results into clinical decision making. CONCLUSIONS: Further research is required on the maintenance and sustainability of pharmacogenetic testing initiatives. These findings provide an overview of the implementation and evaluation strategies of different specialties that can be used to improve pharmacogenetic testing.
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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 | 0.158 | 0.318 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.025 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| 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".