Economic evaluation of tele-resuscitation intervention on emergency department pediatric visits in the Niagara Region, Canada a pilot study
Bibliographic record
Abstract
Introduction: The use of telemedicine in critical care is emerging, however, there is a paucity of information surrounding the costs relative to health gains in the pediatric population. This study aimed to estimate the cost-effectiveness of a pediatric tele-resuscitation (Peds-TECH) intervention compared to the usual care in five community hospital emergency departments (EDs). Using a decision tree analysis approach with secondary retrospective data from a 3-year time period, this cost-effectiveness analysis was completed. Methods: A mixed methods quasi-experimental design was embedded in the economic evaluation of Peds-TECH intervention. Patients aged <18 years triaged as Canadian Triage and Acuity Scale 1 or 2 at EDs were eligible to receive the intervention. Qualitative interviews were conducted with parents/caregivers to explore the out-of-pocket (OOP) expenses. Patient-level health resource utilization was extracted from Niagara Health databases. The Peds-TECH budget calculated one-time technology and operational costs per patient. Base-case analyses determined the incremental cost per year of life lost (YLL) averted, and additional sensitivity analysis confirmed the robustness of the results. Results: Odds ratio for mortality among cases was 0.498 (95% CI: 0.173, 1.43). The average cost of a patient receiving the Peds-TECH intervention was $2,032.73 compared to $317.45 in usual care. In total, 54 patients received the Peds-TECH intervention. Fewer children died in the intervention group resulting in 4.71 YLL. The probabilistic analysis revealed an incremental cost-effectiveness ratio of $64.61 per YLL averted. Conclusion: Peds-TECH appears to be a cost-effective intervention for resuscitating infants/children in hospital emergency departments.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".