Abstract P-011: IMPLEMENTATION OF A PEDIATRIC MEDICAL EMERGENCY AND OUTREACH TEAM IN A TERTIARY CARE HOSPITAL WITHOUT FUNDING IS FEASIBLE
Bibliographic record
Abstract
Aims & Objectives: Although many pediatric hospitals have implemented Rapid Response Systems (RRS), smaller hospitals with lower rates of critical deterioration outside the Pediatric Intensive Care Unit (PICU) may be unable to justify operational costs. We assessed the feasibility of implementing a RRS with a Medical Emergency Team (MET) that performs follow-up visits in a medium-sized pediatric hospital without additional financial resources. Methods Hospital leadership provided support for an unfunded RRS pilot drawing on existing pediatric critical care resources for staffing. Interdisciplinary champions promoted the concept to hospital staff, developed structured documentation tools, and provided MET training. After 9 months, activations, follow-ups, resource utilization and user satisfaction were evaluated. Results Implementation of the RRS was successful with the MET responding to 74 activations (mean [SD] duration 41 [24] minutes), facilitating 19 urgent unplanned transfers to PICU, providing medical or educational intervention over 80% of the time, and performing 843 outreach visits following PICU discharge for 241 patients. The vast majority of feedback from physicians, nurses, respiratory therapists, and families was positive with 100% of respondents supporting continuation of both MET and outreach / follow-up services. Concerns included nursing workload when having MET responsibilities in addition to a patient assignment; insufficient resources to provide formal education outside the PICU; and limited ability to maintain an electronic database. Conclusions Although workload is increased for critical care personnel with shared responsibilities for maintaining the RRS, implementation of a MET in a medium-sized pediatric hospital is possible without additional financial resources.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.026 | 0.003 |
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".