Alternatives to direct emergency department conveyance of ambulance patients: a scoping review of the evidence
Bibliographic record
Abstract
BACKGROUND: The role of ambulance services is shifting, due in part to more intermediate, non-urgent patients who do not require direct emergency department conveyance, yet who cannot be safely left at home alone. Evidence surrounding the safety, effectiveness and efficiency of alternate care routes is not well known. METHODS: This scoping review sought to identify all studies that examined alternate routes of care for the non-urgent "intermediate" patient, as triaged on scene. Search terms for the sample (ambulances, paramedics, etc.) and intervention (e.g. referrals, alternate care route, non-conveyance) were combined. Articles were systematically searched using four databases and grey literature sources (February 2020). Independent researchers screened title-abstract and full text stages. RESULTS: Of 16,037 records, 41 examined alternate routes of care after triage by the on-scene paramedic. Eighteen articles considered quantitative patient data, 12 studies provided qualitative perspectives while 11 were consensus or opinion-based articles. The benefits of alternative schemes are well-recognised by patients, paramedics and stakeholders and there is supporting evidence for a positive impact on patient-centered care and operational efficiency. Challenges to successful use of schemes included: patient safety resulting from incorrect triage decisions, inadequate training, lack of formal partnerships between ambulance and supporting services, and insufficient evidence to support safe implementation or continued use. Studies often inaccurately defined success using proxies for patient safety (e.g. decision comparisons, rates of secondary contact). Finally, patients expressed willingness for such schemes but their preference must be better understood. CONCLUSIONS: This broad summary offers initial support for alternate routes of care for intermediate, non-urgent patients. Even so, most studies lacked methodologically rigorous evidence and failed to evaluate safe patient outcomes. Some remedies appear to be available such as formal triage pathways, targeted training and organisational support, however there is an urgent need for more research and dissemination in this area.
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 | 0.023 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".