Gaming National Emergency Access Target performance using Emergency Treatment Performance definitions and emergency department short stay units
Bibliographic record
Abstract
OBJECTIVE: To evaluate potential gaming of the 4 h ED length of stay metric known as the National Emergency Access Target (NEAT) in Australia and Emergency Treatment Performance (ETP) in New South Wales (NSW). METHODS: Descriptive statistical analysis was used to recalculate and compare the scores for NEAT and the NSW ETP using variations in the definitions of their measurement on 32 184 presentations during 2016. A computer simulation using a discrete event model illustrated the effect of the use of ED short stay beds on the ETP scores. RESULTS: Using the timestamp of the intent to discharge a patient, called, 'ready for departure' instead of the time of a patient physically leaving the department, resulted in an apparent 6% performance improvement. A local interpretation of the NSW state definition of the 'transferred' patient resulted in the ETP for 'admitted' patients improving by 16%. The discrete event model demonstrated that without changing patient length of stay, ETP scores can be improved by optimising the time of the admit decision or increasing the number of ED short stay beds. CONCLUSIONS: The opportunity of NEAT may be squandered unless gaming of the definitions and use of ED short stay beds is addressed. We argue that the longstanding issue of 'departure time' should be defined as 'physically leaving' the department, in accordance with the Australasian College for Emergency Medicine (ACEM) definition. Patient occupancy is a real measure of ED resource use and NSW and national recommendations should be adjusted. ACEM accreditation of EDs should include review of their application of NEAT definitions to ensure they truly reflect patient flow processes.
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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 | 0.008 | 0.035 |
| 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 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".