Effect of an education programme on the utilization of a medical emergency team in a teaching hospital
Bibliographic record
Abstract
BACKGROUND: Medical Emergency Teams (MET) have been developed to identify, review and manage acutely unwell ward patients. Previous studies have suggested that there may be obstacles to the utilization and activation of the MET. AIMS: To determine the effect of a detailed education programme on the rate of utilization of the MET system 3.5 years after its introduction in a University teaching hospital. METHODS: Prospective interventional study involving a detailed programme of education, feedback and decision support for nursing and medical staff given before, during and after implementation of a MET system. We measured the number of MET calls per month for both medical and surgical patients for 109 250 consecutive admissions to the acute care campus of Austin Health from August 2000 to June 2004. RESULTS: Overall activation of the MET increased from 25 calls per month to a peak of 79 calls per month over the study period (average increase of one MET call/month). After standardization for monthly admissions, the increase in MET utilization for surgical patients (increase by 1.13 MET/1000 admissions/month) was 4.9-fold greater than for medical patients (increase by 0.23 MET/1000 admissions/month; P < 0.0001). At the peak level of activity (April 2004), the MET was called to review 8.4% of surgical and 2.7% of medical admissions (P < 0.0001). CONCLUSIONS: There was a progressive increase in the utilization of the MET service in the 3.5 years after implementation, with the rate of uptake 4.9 times greater for surgical than for medical patients. Sustained uptake of the MET system is possible, but increased utilization may take several years to develop. Short-term studies testing the efficacy of the MET system are likely to significantly underestimate its effect on reducing adverse events. Intensive care unit resource adjustments will become necessary to meet increased demand.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".