Emerging Approaches to Usability Evaluation of Health Information Systems: Towards In-Situ Analysis of Complex Healthcare Systems and Environments
Bibliographic record
Abstract
The effective evaluation of health information technology (HIT) is currently a major challenge. It is essential that applications we develop are usable, meet user information needs and are shown to be safe. Furthermore, to provide appropriate feedback to designers of systems new methods for both formative and summative evaluation are needed as applications become more complex and distributed. To ensure system usability a variety of methods have emerged from the area of usability engineering that have been adapted to healthcare. The authors have applied methods of usability engineering, working with hospitals and other healthcare organizations designing and evaluating a range of HIT applications. We describe how our approach to doing portable low-cost usability testing has evolved to the use of clinical simulations conducted in-situ, within real hospital and clinical units to rapidly evaluate the usability and safety of healthcare information systems both before and after system release. We discuss how this approach was extended to development of methods for conducting in-situ clinical simulations in a range of clinical settings.
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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 | 0.077 | 0.129 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".