Impact of the Four-Hour Rule in Western Australian hospitals: Trend analysis of a large record linkage study 2002-2013
Bibliographic record
Abstract
BACKGROUND: In 2009, the Western Australian (WA) Government introduced the Four-Hour Rule (FHR) program. The policy stated that most patients presenting to Emergency Departments (EDs) were to be seen and either admitted, transferred, or discharged within 4 hours. This study utilised de-identified data from five participating hospitals, before and after FHR implementation, to assess the impact of the FHR on several areas of ED functioning. METHODS: A state (WA) population-based intervention study design, using longitudinal data obtained from administrative health databases via record linkage methodology, and interrupted time series analysis technique. FINDINGS: There were 3,214,802 ED presentations, corresponding to 1,203,513 ED patients. After the FHR implementation, access block for patients admitted through ED for all five sites showed a significant reduction of up to 13.2% (Rate Ratio 0.868, 95%CI 0.814, 0.925) per quarter. Rate of ED attendances for most hospitals continued to rise throughout the entire study period and were unaffected by the FHR, except for one hospital. Pattern of change in ED re-attendance rate post-FHR was similar to pre-FHR, but the trend reduced for two hospitals. ED occupancy was reduced by 6.2% per quarter post-FHR for the most 'crowded' ED. ED length of stay and ED efficiency improved in four hospitals and deteriorated in one hospital. Time to being seen by ED clinician and Did-Not-Wait rate improved for some hospitals. Admission rates in post-FHR increased, by up to 1% per quarter, for two hospitals where the pre-FHR trend was decreasing. CONCLUSIONS: The FHR had a consistent effect on 'flow' measures: significantly reducing ED overcrowding and access block and enhancing ED efficiency. Time-based outcome measures mostly improved with the FHR. There is some evidence of increased ED attendance, but no evidence of increased ED re-attendance. Effects on patient disposition status were mixed. Overall, this reflects the value of investing resources into the ED/hospital system to improve efficiency and patient experience. Further research is required to illuminate the exact mechanisms of the effects of FHR on the ED and hospital functioning across Australia.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".