Comparison of emergency department time performance between a Canadian and an Australian academic tertiary hospital
Bibliographic record
Abstract
OBJECTIVE: To compare performance and factors predicting failure to reach Ontario and Australian government time targets between a Canadian (Sunnybrook Hospital) and an Australian (Austin Health) academic tertiary-level hospitals in 2012, and to assess for change of factors and performance in 2016 between the same hospitals. METHODS: This was a retrospective, observational study of patient administrative data in two calendar years. The main outcome measure was reaching Ontario and Australian ED time targets for admissions, high and low urgency discharges. Secondary outcomes were factors predicting failure to reach these targets. RESULTS: Between 2012 and 2016, Sunnybrook and Austin experienced increased patient volume of 10.2% and 19.2%, respectively. Bed capacity decreased at Sunnybrook (-10.8%) but increased at the Austin (+30.3%). For both years, Austin failed to achieve the Australian time target, but succeeded for all Ontario targets except for low urgency discharges. Sunnybrook failed all targets irrespective of year. The top factors for failing Ontario ED length-of-stay targets for both hospitals in 2012 and 2016 were bed request greater than 6 h, access block greater than 1 h, use of cross-sectional imaging, consultation and waiting for the emergency physician greater than 2 h. CONCLUSION: Austin outperformed Sunnybrook for Ontario and Australian government time targets. Both hospitals failed the Australian targets. Factors predicting failure to achieve targets were different between hospitals, but were mainly clinical resources. Sunnybrook focussed on increasing human resources. Austin focussed on increasing human resources, observation unit and hospital beds. Intrinsic hospital characteristics and infrastructure influenced target success.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".