How to Present Evidence-Based Usability Design Principles Dedicated to Medication-Related Alerting Systems to Designers and Evaluators? Results from a Workshop
Bibliographic record
Abstract
Medication alerting system use errors and lack of adoption are often attributed to usability issues. Previous work has used evidence from the literature to reveal usability principles specific to medication alerting systems and identify potential consequences of violating these principles. The current study sought to explore how best to convey these principles to designers and evaluators of these systems to facilitate their work. To this aim, a workshop with 19 participants was used to generate ideas and opinions on how to deliver these topic-specific design principles in a way that would be most helpful for them. Participants generated ideas for how (e.g., a collaborative, continuously updated forum) and what (e.g., illustrations, checklists, evidence sources and strength, consequences of violations) information is most useful to disseminate usability principles for medication alerting systems. Participants, especially designers, expressed desire to use these principles in practice and avoid previously documented mistakes and therefore make design and evaluation of these systems more effective and efficient. Those insights are discussed in terms of feasibility and logistical challenges to developing the proposed documentation). To move this work forward, a more collaborative approach of Human Factors specialists in medical informatics is necessary.
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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 | 0.132 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".