Snowball Group Usability Testing for Rapid and Iterative Multisite Tool Development: Method Development Study
Bibliographic record
Abstract
Background: Usability testing is valuable for assessing a new tool or system's usefulness and ease-of-use. Several established methods of usability testing exist, including think-aloud testing. Although usability testing has been shown to be crucial for successful clinical decision support (CDS) tool development, it is often difficult to conduct across multisite development projects due to its time- and labor-intensiveness, cost, and the skills required to conduct the testing. Objective: Our objective was to develop a new method of usability testing that would enable efficient acquisition and dissemination of results among multiple sites. We sought to address the existing barriers to successfully completing usability testing during CDS tool development. Methods: We combined individual think-aloud testing and focus groups into one session and performed sessions serially across 4 sites (snowball group usability testing) to assess the usability of two CDS tools designed for use by nurses in primary and urgent care settings. We recorded each session and took notes in a standardized format. Each site shared feedback from their individual sessions with the other sites in the study so that they could incorporate that feedback into their tools prior to their own testing sessions. Results: The group testing and snowballing components of our new usability testing method proved to be highly beneficial. We identified 3 main benefits of snowball group usability testing. First, by interviewing several participants in a single session rather than individuals over the course of weeks, each site was able to quickly obtain their usability feedback. Second, combining the individualized think-aloud component with a focus group component in the same session helped study teams to more easily notice similarities in feedback among participants and to discuss and act upon suggestions efficiently. Third, conducting usability testing in series across sites allowed study teams to incorporate feedback based on previous sites' sessions prior to conducting their own testing. Conclusions: Snowball group usability testing provides an efficient method of obtaining multisite feedback on newly developed tools and systems, while addressing barriers typically associated with traditional usability testing methods. This method can be applied to test a wide variety of tools, including CDS tools, prior to launch so that they can be efficiently optimized.
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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 | 0.122 | 0.128 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".