Low-Cost Rapid Usability Testing for health information systems: is it worth the effort?
Bibliographic record
Abstract
Usability testing is a step of the usability engineering process that focuses on analyzing and improving user interactions with computer systems. This study was designed to determine if an approach known as Low-Cost Rapid Usability Testing can be introduced as a standard part of the system development lifecycle (SDLC) for health information syste ms in a cost effective manner by completing a full cost-benefit analysis of this testing technique. It was found that by introducing this technique into the system development lifecycle to allow for earlier detection of errors in a health information syste m it is possible for a health organization to achieve an estimated 36.5% to 78.5% cost savings compared to the impact of errors going undetected and potentially causing a technology-induced error. Overall it was found that Low-Cost Rapid Usability Testing can be implemented in a cost effective manner to develop health information systems, and computer systems in general, which will have a lower incidence of technology-induced errors.
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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 | 0.065 | 0.211 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".