Optimizing Emergency Department Care for People With Bleeding Disorders: A Scoping Review of Barriers and Interventions for Improvement
Bibliographic record
Abstract
ABSTRACT Background Emergency department (ED) care is critical for managing acute bleeding events in people with bleeding disorders. Despite international guidelines recommending haemostatic treatment within 30–60 min, delays and deviations from best practices are common and associated with poorer outcomes. Objective Identify barriers to guideline‐concordant ED management of bleeding disorders, evaluate interventions, assess clinical impact, and highlight knowledge gaps to inform future research. Methods Five databases (MEDLINE, Embase, Scopus, Web of Science, Cochrane Library) were searched from inception to 21 March 2025, following established scoping review guidelines and a pre‐registered protocol. Eligible studies examined barriers or interventions for improving ED care. Data were independently screened and extracted by two reviewers, then synthesized using descriptive statistics and narrative synthesis. Results Seventeen studies out of 3541 met inclusion criteria. Common barriers included electronic medical record (EMR) limitations, absence of standardized protocols, limited healthcare professional education, and inadequate communication with haematology. Delayed time to therapy ( n = 8, range 1.4–5.6 h) was frequently reported; additional impacts included failure to administer haemostatic therapy for confirmed/suspected bleeding, non‐indicated diagnostic testing, and patient mortality. Most interventions combined EMR enhancements with education. All interventions were associated with improved outcomes, including reduced time to therapy ( n = 4, range 0.4–2.5 h). Perspectives of people with bleeding disorders and caregivers were infrequently incorporated. Conclusion This review provides the first comprehensive synthesis of barriers and interventions in ED care for people with bleeding disorders, identifying critical gaps in timely treatment, interdisciplinary coordination, and stakeholder engagement. These findings provide a foundation for future quality improvement research.
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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 | 0.021 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".