Emergency care workload units: A novel tool to compare emergency department activity
Bibliographic record
Abstract
INTRODUCTION: Funding bodies have traditionally used attendance figures as a way of determining the allocation of funding for resources in the EDs. Using attendance figures only might not accurately reflect the funding and resources required. The need to create an easily implemented tool to compare workload and resources required was identified. Using the Australasian Triage Scale, a tool was developed to estimate staffing requirements and resource use within each ED. This, although currently not validated, provides a promising start in finding a way to accurately determine ED workload. METHODS: Existing data on patient acuity, disposition, numbers of patients and the individual costing of each presentation was used to estimate and define the workload of an ED in emergency care workload units (ECWU). The tool is applied to six de-identified hospitals within Queensland to demonstrate its potential use for equitable budget and staffing allocation. RESULTS: The tool was applied to a selection of de-identified EDs within Queensland hospitals. An increased number of ECWU is generated for a patient with a more urgent triage category reflecting a higher resource consumption and workload. DISCUSSION: Although a few studies have been completed in Canada linking workload, resource consumption and cost to triage category, this tool will need to be validated before its use can be fully appreciated. CONCLUSION: This tool provides a simple method to calculate equitable distribution of staffing and budget allocation based on workload across the different EDs within Australia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 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 teacher head, 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".