Usability Across Health Information Technology Systems: Searching for Commonalities and Consistency
Bibliographic record
Abstract
Usability of health information technology (HIT) remains a predominant concern - one often exacerbated by clinicians' need to access information created by many different professionals in different settings, often using very dissimilar EHRs or even different configurations of the same EHR. Because of these variations, we argue that we must no longer think of usability as anchored in one setting, one EHR, one data standard, or one type of clinician. Rather, usability must be understood as a collective and constantly evolving process. This paper seeks to address that reality by 1) substantially expanding our previously-developed conceptual matrix of the wide range of settings and interfaces comprising modern HIT and 2) presenting actual examples of EHR usability issues with similar data but very different displays or processes.
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 | 0.127 | 0.319 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.017 | 0.011 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| 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".