Teleconsultation Using Mobile Phones for Diagnosis and Acute Care of Burn Injuries Among Emergency Physicians: Mixed-Methods Study
Bibliographic record
Abstract
BACKGROUND: The referral process in acute care remains challenging in many areas including burn care. Mobile phone apps designed explicitly for medical referrals and consultations could streamline the referral process by using structured templates and integrating features specific to different specialties. However, as these apps are competing with commercial chat services, usability becomes a crucial factor for successful uptake. OBJECTIVE: The aim of this study was to assess the usability of a mobile phone app for remote consultations and referrals of burn injuries. METHODS: A total of 24 emergency doctors and 4 burns consultants were recruited for the study. A mixed-methods approach was used including a usability questionnaire and a think-aloud interview. Think-aloud sessions were video-recorded, and content analysis was undertaken with predefined codes relating to the following 3 themes: ease of use, usefulness of content, and technology-induced errors. RESULTS: The users perceived the app to be easy to use and useful, but some problems were identified. Issues relating to usability were associated with navigation, such as scrolling and zooming. Users also had problems in understanding the meaning of some icons and terminologies. Sometimes, some users felt limited by predefined options, and they wanted to be able to freely express their clinical findings. CONCLUSIONS: We found that users faced problems mainly with navigation when the app did not work in the same way as the other apps that were frequently used. Our study also resonates with previous findings that when using standardized templates, the systems should also allow the user to express their clinical findings in their own words.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".