Pharmacogenomic‐based personalized medicine: Multistakeholder perspectives on implementational drivers and barriers in the Canadian healthcare system
Bibliographic record
Abstract
Pharmacogenomics (PGx)-based personalized medicine (PM) is increasingly utilized to guide treatment decisions for many drug-disease combinations. Notably, London Health Sciences Centre (LHSC) has pioneered a PGx program that has become a staple for London-based specialists. Although implementational studies have been conducted in other jurisdictions, the Canadian healthcare system is understudied. Herein, the multistakeholder perspectives on implementational drivers and barriers are elucidated. Using a mixed-method qualitative model, key stakeholders, and patients from LHSC's PGx-based PM clinic were interviewed and surveyed, respectively. Interview transcripts were thematically analyzed in a stepwise process of customer profiling, value mapping, and business model canvasing. Value for LHSC located specialist users of PGx was driven by the quick turnaround time, independence of the PGx clinic, and the quality of information. Engagement of external specialists was only limited by access and awareness, whereas other healthcare nonusers were limited by education and applicability. The major determinant of successful adoption at novel sites were institutional champions. Patients valued and approved of the service, expressed a general willingness to pay, but often traveled far to receive genotyping. This paper discusses the critical pillars of education, awareness, advocacy, and efficiency required to address implementation barriers to healthcare service innovation in Canada. Further adoption of PGx practices into Canadian hospitals is an important factor for advancing system-level changes in care delivery, patient experiences, and outcomes. The findings in this paper can help inform efforts to advance clinical PGx practices, but also the potential adoption and implementation of other innovative healthcare service solutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.032 | 0.018 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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 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".