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 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".