Approaches to Demonstrating the Effectiveness and Impact of Usability Testing of Healthcare Information Technology
Bibliographic record
Abstract
In recent years the usability of health information systems has come to the fore as a major issue, with many reported examples of problems with the usability of systems such as electronic health records and other health information technologies (HIT). In response a range of usability engineering methods have emerged to help in the design and evaluation of HIT. Many studies have shown the importance of usability testing methods that include full video recording of user interactions, such as the method known as low-cost rapid usability testing. However, such approaches have been considered by many as being too costly to carry out and some have argued that they may take too long to be used for practical input into improving applications and systems. In this paper we demonstrate several approaches we have taken for proving the cost-effectiveness and benefit of conducting principled usability testing. It is argued that such studies are needed to inform system design and evaluation and for proving to healthcare management the need for properly conducting such studies before releasing HIT.
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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 | 0.305 | 0.503 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.015 | 0.008 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.006 | 0.005 |
| 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".