Implementing Electronic Discharge Communication Tools in Pediatric Emergency Departments: Multicountry, Cross-Sectional Readiness Survey of Nurses and Physicians (Preprint)
Bibliographic record
Abstract
BACKGROUND Pediatric emergency departments (ED) in many countries are implementing electronic tools such as kiosks, mobile apps, and electronic patient portals, to improve the effectiveness of discharge communication. OBJECTIVE This study aimed to survey nurse and physician readiness to adopt these tools. METHODS An electronic, cross-sectional survey was distributed to a convenience sample of currently practicing ED nurses and physicians affiliated with national pediatric research organizations in Canada, Australia, and New Zealand. Survey development was informed by the nonadoption, abandonment, scale-up, spread, sustainability framework. Measures of central tendency, and parametric and nonparametric tests were used to describe and compare nurse and physician responses. RESULTS Out of the 270 participants, the majority were physicians (61%, 164/270), female (65%, 176/270), and had 5 or more years of ED experience (76%, 205/270). There were high levels of consensus related to the value proposition of electronic discharge communication tools (EDCTs) with 82% (221/270) of them agreeing that they help parents and patients with comprehension and recall. Lower levels of consensus were observed for organizational factors with only 37% (100/270) agreeing that their staff is equipped to handle challenges with communication technologies. Nurses and physicians showed significant differences on 3 out of 21 readiness factors. Compared to physicians, nurses were significantly more likely to report that EDs have a responsibility to integrate EDCTs as part of a modern system (P<.001) and that policies are in place to guide safe and secure electronic communication (P=.02). Physicians were more likely to agree that using an EDCT would change their routine tasks (P=.04). One third (33%, 89/270) of participants indicated that they use or have used EDCT. CONCLUSIONS Despite low levels of uptake, both nurses and physicians in multiple countries view EDCTs as a valuable support to families visiting pediatric ED. Leadership for technology change, unclear impact on workflow, and disparities in digital literacy skills require focused research effort. CLINICALTRIAL
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".