Emergency Department Navigator Interventions and Outcome Measures: A Scoping Review
Bibliographic record
Abstract
INTRODUCTION: Emergency department (ED) patient navigators are increasingly used, but a lack of understanding of how ED navigator interventions are designed, described, and evaluated creates gaps in our ability to understand, monitor and improve care. The purpose of this scoping review is to identify how the literature describes and evaluates ED patient navigator interventions for older people transitioning to a primary care setting. METHODS: A scoping review was conducted following the Johanna Briggs Institute updated methodological guidance for the conduct of scoping reviews. We searched three databases: MEDLINE, EMBASE and CINAHL. We included English language articles without any restrictions on study designs that two reviewers screened. All articles focused on distinct ED navigator roles to facilitate transitions for older people from the ED to primary care were included. Data extraction was completed by the primary reviewer and validated by two secondary reviewers. We report study characteristics in a table. Descriptive content analysis was used to analyse the main findings. RESULTS: A total of 10 studies were included out of 2102 articles identified. All studies used quantitative designs except one, which used a qualitative research design. Four studies were conducted in the United States, two in Australia and the UK and one in Canada and Belgium. Twenty unique outcome measures were identified, with hospital admission rate, frequency of ED presentations and ED/hospital length of stay being the most common. We identified six intervention components: assessment, consultation, liaison, development of care plan, referral and follow-up. Interventions using 4 or more components more commonly reported positive outcomes. Outcome measures used to evaluate interventions were often not tracked across care settings, potentially obscuring the impact of ED navigator interventions across the care continuum. CONCLUSION: Future research should examine which patients benefit from ED navigation and which outcome measures might help contextualise intervention effectiveness across care settings.
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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 | 0.070 | 0.250 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.028 | 0.029 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".