Hybrid usability methods: practical techniques for evaluating health information technology in an operational setting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Health information technologies (HIT) including electronic health records (EHRs), bio-medical device interfaces, and a broad array of clinical software applications suffer from usability flaws that impact patient safety, clinician efficiency, and health outcomes . In response, informaticians, systems engineers, and human factors experts have hard fought to raise awareness among stakeholders including clinicians, health administrators, policy makers. The design of health information technology, are often neglected or abbreviated in a misguided effort to reduce production costs, close functionality gaps, or keep pace with software development schedules. The usability specialists (1) assess the UX maturity of their organization; (2) evangelize the importance of evidence-based design; (3) become conversant in a variety of usability techniques and (4) strategically apply hybrid strategies throughout the software design lifecycle. One such problem that affects the industry as it heads toward a paper-less environment is ensuring that decision support tools in the electronic medical record are both safe and effective.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it