Exploring the Relationship between Usability and Technology-Induced Error: Unraveling a Complex Interaction
Bibliographic record
Abstract
The effective evaluation of the usability of health information systems is currently a major challenge. It is essential that the applications we develop are not only usable, but that they are also shown to be safe and do not inadvertently introduce errors. Furthermore, to provide appropriate feedback to designers of systems new methods for evaluation are needed as applications become more complex and distributed. To ensure system usability and safety a variety of methods have emerged from the area of usability engineering that have been adapted to healthcare. The authors have applied and adapted methods of usability engineering, working with hospitals and other healthcare organizations for designing and evaluating a range of health information systems over a number of years. We describe a methodological framework for considering some of these advances and show how a range of usability evaluations can be used to evaluate both the usability and safety of healthcare information systems both in artificial mocked up and real clinical settings using in-situ testing approaches. We conclude with a discussion of recent trends in the area of usability engineering in healthcare that have potential for improving the safety of healthcare information systems.
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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 | 0.026 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".