Patients’ pathways to the emergency department: a scoping review
Bibliographic record
Abstract
BACKGROUND: Emergency department (ED) crowding is a common healthcare issue with multiple causes. One important knowledge area is understanding where patients arrived from and what care they received prior to ED admission. This information could be used to inform strategies to provide care for low acuity patients outside of the hospital and reduce unnecessary ED admissions. The aim of this scoping review was to provide a comprehensive overview of global published research examining the acute care trajectory of all ED patients. METHODS: The scoping review was performed according to the JBI Manual for Evidence Synthesis and the PRISMA-SCR checklist. A comprehensive literature search was performed to identify studies describing where patients arrived from and/or whose pathway of care was before an ED visit. The search was conducted in MEDLINE, Embase, and the Cochrane Library from inception through December 5th, 2022. Two reviewers independently screened the records. RESULTS: Out of the 6,465 records screened, 14 studies from Australia, Canada, Haiti, Norway, Sweden, Switzerland, Belgium, Indonesia, and the UK met the inclusion criteria. Four studies reported on where patients physically arrived from, ten reported how patients were transported, six reported who referred them, and six reported whether medical care or advice was sought prior to visiting an ED. CONCLUSION: This scoping review revealed a lack of studies describing patients' pathways to the ED. However, studies from some countries indicate that a relatively large proportion of patients first seek care or guidance from a primary care physician (PCP) before visiting an ED. However, further research and published data are needed. To improve the situation, we recommend the development and implementation of a template for the uniform reporting of factors outside the ED, including where the patient journey began, which healthcare facilities they visited, who referred them to the ED, and how they arrived.
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How this classification was reachedexpand
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 | 0.002 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".