Patients’ pathways to the emergency department: a scoping review
Bibliographic record
Abstract
Abstract Background Emergency Department (ED) crowding is a common healthcare issue. The causes are multifactorial, and some causes may be found by analyzing patient trajectories prior to ED visits. The aim of this scoping review is to identify and examine studies that describe patient trajectories prior to ED arrival. Methods The scoping review was performed according to the JBI Manual for Evidence Synthesis and the PRISMA-SCR checklist. A literature search was done to identify studies describing where patients come from and/or their pathway of care before the ED visits. The search was conducted in MEDLINE, Embase, and the Cochrane Library from inception up to March 17th, 2022 and updated on December 5th, 2022. Two reviewers independently screened the records at all stages of the review process. Results Out of 6,465 records screened, 14 papers from Australia, Canada, Haiti, Norway, Sweden, Switzerland, Belgium, Indonesia and the UK met the inclusion criteria. Four studies reported on where patients originated from. Seven studies reported on who referred them. Ten reported how patients were transported and five reported if alternative care or advice was sought prior to visiting an ED. Data were sparse for these categories of information; not all studies reported the full spectrum of patients within each category. Conclusion There are knowledge gaps when it comes to describing patients’ pathways to the emergency department. The data reported provided limited insight, and the lack of uniform data prohibits comparisons across studies. Further studies that comprehensively describe patient trajectories prior to an ED visit are paramount to help understand the reasons for the increased patient influx and ED crowding.
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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 | 0.027 | 0.121 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.022 | 0.027 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".