Improving the Safety of Medicines via Digital Technology: An Assessment of the Scope and Quality of Risk Minimization Websites in the United States and United Kingdom
Bibliographic record
Abstract
EHealth holds tremendous promise for enhancing drug safety initiatives known as risk minimization programs. Little is known, however, regarding the scope and quality of existing risk minimization websites. Two publicly accessible repositories, REMS@FDA [1] and Electronic Medicines Compendium [2], were reviewed to identify all regulatorily approved risk minimization programs in the United States (US) and United Kingdom (UK) with websites. Website quality was evaluated using the Enlight Quality Assessment tool, a psychometrically validated instrument that addresses seven quality domains. Ninety-three websites were identified: 59 for healthcare professionals (7 UK/52 US), and 34 for patients (5 UK/29 US). The websites functioned chiefly as archives for electronic copies of educational materials; a subset (31/93) had additional features. Mean quality ratings for Usability (mean 4.70, SD 0.59), Visual Design (mean 4.03, SD 0.87) and Content (mean 4.31, SD 0.82) were good. General Subjective Evaluation was fair (mean 3.15, SD 1.21). Mean scores for Therapeutic Alliance and Therapeutic Persuasiveness were poor (mean 2.62, SD 1.47; and mean 2.50, SD 1.48, respectively); those for User Engagement were very poor (mean 2.25, SD 1.03). No differences were found by target audience but several were identified based on region. Risk minimization websites are easy to navigate and well organized. Few, however, incorporate eHealth design elements that facilitate user engagement, build therapeutic alliance and exert therapeutic persuasiveness. Such elements can enhance program uptake and effectiveness. Results highlight opportunities for improving the quality of risk minimization websites and their ability to bridge pharmaceutical and healthcare systems. A risk minimization program is a type of drug safety measure to ensure that a medicine’s benefits outweigh its risks. Electronic versions of these risk minimization programs (websites) offer new ways to reach and educate patients and healthcare professionals. We conducted a study to examine the quality of these websites. We reviewed all approved risk minimization programs in the United States (US) and the United Kingdom (UK) using two publicly available repositories, REMS@FDA and Electronic Medicines Compendium, to identify risk minimization websites. We assessed website quality using the Enlight Quality Assessment tool. We found 93 websites: 59 for healthcare professionals (7 UK/52 US) and 34 for patients (5 UK/29 US). Our analysis showed that the websites were well organized and easy to search. Few, however, used specific electronic design elements that can promote trust in and engagement with the content of the website, and can encourage users to follow the recommended actions for safe and appropriate use of the medicine. In conclusion, there are multiple ways that the design of risk minimization websites could be improved in order to make them more effective as drug safety measures.
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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 | 0.019 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".