Enhancing Drug Safety: Designing Solutions To Meet Prescribers’ Information Needs
Bibliographic record
Abstract
Background: Better integration of pharmacovigilance evidence into clinical practice may reduce preventable adverse drug events. Objectives: To describe prescribers' experiences seeking and using drug information; Design a web application to present drug safety information to prescribers. Methods: We conducted a qualitative systematic literature review with thematic analysis to examine prescriber’s challenges in accessing and using drug safety information. These insights informed the development of a prototype application called DrugSafety. The Design Thinking approach guided the design of DrugSafety. Results: Review of 15 studies highlighted prescribers’ need for accessible, valid, reliable, current, and credible sources, as they report difficulties in locating and applying the information. DrugSafety addresses these needs by offering summarized, clinically relevant information from medical journals, performing real-time data analysis, and displaying results via visualizations. Conclusion: We identified barriers prescribers face and introduced an informatics solution to reduce the knowledge-to-practice gap. Future steps include iterative refinements and usability 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 | 0.046 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".