Strategies to improve the quality of nurse triage in emergency departments: A systematic review
Bibliographic record
Abstract
AIM: This systematic review aimed to assess the impact of implementation strategies for nursing triage on quality outcomes and to examine barriers and facilitators to their implementation in the emergency department (ED). DATA SOURCES: Embase, PubMed, CINAHL, Cochrane Library, Web of Science, PsycINFO and ProQuest Dissertations & Theses. METHODS: This systematic review included quantitative and qualitative studies published from January 1990 to April 2024 that evaluated strategies to improve ED triage. Study quality was assessed with the Mixed Methods Appraisal Tool (MMAT). The benefits of the strategies were reported using descriptive statistics (quantitative studies) and themes and subthemes (qualitative studies). Barriers and facilitators were identified using the Behavior Change Wheel framework. RESULT: Three main implementation strategy categories to improve the quality of nursing triage were identified: education (64%), technology (30%), and audit and feedback (6%). All strategies demonstrated short-term benefits, including increased triage accuracy and improved triage knowledge and skills. The most frequently reported barriers were workload and overcrowding, while facilitators included nurses' experience, interprofessional collaboration, and a culture of continuous improvement. CONCLUSION: Comprehensive approaches, including education, technology, and regular audits with feedback, are associated with improved triage quality outcomes. Continuous training, active nurse participation in tool development, and the use of validated audit tools are essential. These measures could ensure rigorous nursing triage in EDs and enhance care safety by optimizing patient prioritization as they enter healthcare systems. This review underscores the need for further research on implementation strategies to enhance effective and safe patient prioritization in the ED.
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.
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 | 0.024 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".