Low-Cost Rapid Usability Testing: Its Application in Both Product Development and System Implementation
Bibliographic record
Abstract
In recent years there has been considerable discussion around the need for certification and regulation of healthcare information technology (IT). In particular, the usability of the products being developed needs to be evaluated. This has included the application of standards designed to ensure the process of system development is user-centered and takes usability into consideration while a product is being developed. In addition to this, in healthcare, organizations in the United States and Europe have also addressed the need and requirement for product certification. However, despite these efforts there are continued reports of unusable and unsafe implementations. In this paper we discuss the need to not only include (and require) usability testing in the one-time development process of health IT products (such as EHRs), but we also argue for the need to additionally develop specific usability standards and requirements for usability testing during the implementation of vendor products (i.e. post product development) in healthcare settings. It is further argued that health IT products that may have been certified regarding their development process will still require application of usability testing in the process of implementing them in real hospital settings in order to ensure usability and safety. This is needed in order to ensure that the final result of both product development and implementation processes take into account and apply the latest usability principles and methods.
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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 | 0.076 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".