Use of mobile devices in the emergency department: A scoping review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Electronic health records are increasingly used in regional health authorities, healthcare systems, hospitals, and clinics throughout North America. The emergency department provides care for urgent and critically ill patients. Over the past several years, emergency departments have become more computerized. Tablet computers and Smartphones are increasingly common in daily use. As part of the computerization trend, we have seen the introduction of handheld computers, tablets, and Smartphones into practice as a way of providing health professionals (e.g. physicians, nurses) with access to patient information and decision support in the emergency department. In this article, we present a scoping review and outline the current state of the research using mobile devices in the emergency departments. Our findings suggest that there is very little research evidence that supports the use of these mobile devices, and more research is needed to better understand and optimize the use of mobile devices. Given the prevalence of handheld devices, it is inevitable that more decision support, charting, and other activities will be performed on these devices. These developments have the potential to improve the quality and timeliness of care but should be thoroughly evaluated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it