Will alternative immediate care services reduce demands for non-urgent treatment at accident and emergency?
Bibliographic record
Abstract
OBJECTIVES: To estimate the potential of general practice, minor injury units, walk in centres and NHS Direct to reduce non-urgent demands on accident and emergency (A&E) departments taking into account the patient's reasons for attending A&E. METHODS: A questionnaire survey and notes review of 267 adults presenting to the A&E department of a large teaching hospital in Sheffield, England, triaged to the two lowest priority treatment streams, was conducted over seven weeks. Using defined criteria, patients were classified by the suitability of the presenting health problem to be managed by alternative immediate care services or only by A&E, and also by the likelihood, in similar circumstances, of patients presenting to other services given their reasons for seeking A&E care. RESULTS: Full data were obtained for 96% of participants (255 of 267). Using objective criteria, it is estimated that 55% (95% CI 50%, 62%) of the health problems presented by a non-urgent population attending A&E are suitable for treatment in either general practice, or a minor injury unit, or a walk in centre or by self care after advice from NHS Direct. However, in almost one quarter (24%) of low priority patients who self referred, A&E was not the first contact with the health services for the presenting health problem. The reason for attending A&E cited most frequently by the patients was a belief that radiography was necessary. The reason given least often was seeking advice from a nurse practitioner. Taking into account the objective suitability of the health problem to be treated elsewhere, and the reasons for attending A&E given by the patients, it is estimated that, with similar health problems, as few as 7% (95% CI 3%, 10%) of the non-urgent A&E population may be expected to present to providers other than A&E in the future. CONCLUSIONS: The increasing availability of alternative services offering first contact care for non-urgent health problems, is likely to have little impact on the demand for A&E services.
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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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 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".