Strategies to improve the quality of nurse triage in emergency departments: A systematic 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
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.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it