Implementation of Pharmacogenomics Testing in Daily Clinical Practice: Perspectives of Prescribers from Two Canadian Armed Forces Medical Clinics
Bibliographic record
Abstract
Background/Objectives: While there is mounting scientific evidence supporting the effectiveness of PGx (pharmacogenomics)-guided medical treatment, its implementation into clinical care is still lagging. Stakeholder buy-in, in particular from prescribers, will be key in the implementation efforts. Previous implementation studies have primarily focused on prescriber attitudes or have used hypothetical scenario methodology in a variety of healthcare settings. Real-world studies provide better insight into prescriber experience and needs. In this prospective observational qualitative research study, we report the perspectives of prescribers working in military medical care after a one-year PGx implementation trial. Methods: At the end of the PGx implementation period, thirteen prescribers participated in a semi-structured interview. The interview was designed based on the Technology Acceptance Model and queried their perceptions of effectiveness and ease of use of the PGx innovation. Results: Three main themes emerged from the qualitative data: (1) the knowledge required for PGx testing, (2) the integration of the testing into the existing workflow and (3) the perceived clinical utility of the PGx results. Prescribers had educational and training opportunities prior to the study but still encountered difficulty with the interpretation of the test results. They generally managed well the workflow changes occasioned by the testing. They reported that the clinical value came primarily from an increased confidence in prescribing safe medications and improving the therapeutic alliance with their patients. There was uncertainty about which patient population would most benefit from the testing. Conclusions: Our results lend support to the general ongoing challenges identified in PGx implementation studies conducted in other clinical settings and using other methodologies. They also revealed specific factors that the prescribers found of value and areas that needed improvement to support future implementation efforts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".