Emerging Approaches to Evaluating the Usability of Health Information Systems
Bibliographic record
Abstract
It is essential that health information systems are easy to use, meet user information needs and are shown to be safe. However, there are currently a wide range of issues and problems with health information systems related to human-computer interaction. Indeed, the lack of ease of use of health information systems has been a major impediment to adoption of such systems. To address these issues, the authors have applied methods emerging from the field of usability engineering in order to improve the adoption of a wide range of health information systems in collaboration with hospitals and other healthcare organizations throughout the world. In this chapter we describe our work in conducting usability analyses that can be used to rapidly evaluate the usability and safety of healthcare information systems, both in artificial laboratory and real clinical settings. We then discuss how this work has evolved towards the development of software systems (“virtual usability laboratories”) capable of remotely collecting, integrating and supporting analysis of a range of usability data.
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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 | 0.033 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.017 | 0.011 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".