Pre- and Post-Redesign Usability Assessment of a Telemedicine Interface Based on Subjective Metrics
Bibliographic record
Abstract
Usability problems in the interaction between patients and telemedicine platforms has been recognized as a deterrent in the public's acceptance and use of this alternative healthcare delivery method. Therefore, evaluating the usability of telemedicine provider websites, with a focus on potential patients' first interaction with telemedicine, is a critical research inquiry. To this end, a novel survey was developed to conduct an unmoderated remote usability test (URUT) of the Teladoc website. Teladoc is one of the largest providers of Direct-to-Consumer (DTC) telemedicine. The Teladoc Website Usability Survey (TWUS) instrument collected both objective task completion success metrics and subjective user feedback. A codebook was developed to categorize design features and user interface aspects that affected usability. The TWUS and codebook demonstrated value in identifying usability problems with the Teladoc interface and can be applied in other telemedicine or Health Information Technology (HIT) usability studies. Identifying and addressing usability issues is an important approach to increase the widespread acceptance and adoption of these healthcare delivery technologies.
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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 | 0.012 | 0.037 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".