Regular in-situ simulation training of paediatric Medical Emergency Team leads to sustained improvements in hospital response to deteriorating patients, improved outcomes in intensive care and financial savings
Bibliographic record
Abstract
AIM OF THE STUDY: The introduction of a paediatric Medical Emergency Team (pMET) was accompanied by weekly in-situ simulation team training. Key ward staff participated in team training, focusing on recognition of the deteriorating child, teamwork and early involvement of senior staff. Following an earlier study [1], this investigation aimed to evaluate the long-term impact of ongoing regular team training on hospital response to deteriorating ward patients, patient outcome and financial implications. METHODS: Prospective cohort study of all deteriorating in-patients in a tertiary paediatric hospital requiring admission to paediatric intensive care (PICU) the year before, 1year after and 3 years after the introduction of pMET and team training. RESULTS: Deteriorating patients were recognised more promptly (before/1year after/3years after pMET; median time 4/1.5/0.5h, p<0.001), more often reviewed by consultants (45%/76%/81%, p<0.001) and more rapidly escalated to PICU (median time 10.5/5/3.5h, p=0.02). There was a significant reduction in associated PICU admissions (56/51/32, p=0.02) and PICU bed days (527/336/193, p<0.001). The total annual cost of training (£74,250) was more than offset by savings from reduced PICU bed days (£801,600 per annum). Introduction of pMET coincided with significantly reduced hospital mortality (p<0.001). CONCLUSION: These results indicate that lessons learnt by ward staff during team training led to sustained improvements in the hospital response to critically deteriorating in-patients, significantly improved patient outcomes and substantial savings. Integration of regular in-situ simulation training of medical emergency teams, including key ward staff, in routine clinical care has potential application in all acute specialties.
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".