Usability Checklists for Health Technology: Case Study and Experts’ Opinions
Bibliographic record
Abstract
Application of usability evaluations throughout the health technology lifecycle is necessary to improve the efficiency, safety, and effectiveness of health service delivery. Unfortunately, technology vendors and healthcare organizations may not have funding, time or expertise to conduct usability studies. In this paper, we describe how usability checklists can potentially fill this gap. First, we introduce a case study using a checklist to identify usability issues with a primary care dashboard. Then we provide an expert summary of the strengths and limitations of usability checklists. Findings suggest that checklists are efficient to identify important usability issues. They can be used effectively by project team members - including clinicians - without formal usability training. However, checklists should complement rather than replace usability evaluations with representative users.
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 | 0.207 | 0.413 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.004 |
| 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".