Health Care Utilization and Costs in Ventilator-Dependent Children and Adults Receiving an eHealth Intervention During the COVID-19 Pandemic
Bibliographic record
Abstract
BACKGROUND: During the COVID-19 pandemic, we implemented the Long-term In-Home Ventilator Engagement (LIVE) intervention to provide virtual specialist care. Using a matched home mechanical ventilation control group, we compared publicly funded health-service utilization and costs for ventilator-dependent children and adults receiving the LIVE eHealth intervention. METHODS: LIVE users were matched to controls on age, sex, ventilation type, years on ventilation, and reason for ventilation. The Ventilator Equipment Pool database was linked to health administrative data, which contains medically necessary health care service information on the entire population. We used analysis of covariance and generalized estimating equations to estimate the effect of the LIVE program on health care utilization and costs, controlling for 12-month prior health care utilization. We used Kaplan-Meier curves to compare survival rates. RESULTS: Of the 250 LIVE users, we were able to 1:1 match 178 with home mechanical ventilation controls. Adjusted rate ratios for most outcomes resulted in elevated costs and utilization in the post period attributable to LIVE; however, most did not reach statistical significance. All-cause in-patient admissions (16%), out-patient pulmonology visits (41%), and general practitioner costs (77%) were significantly elevated in LIVE participants in the post period. There was no statistically significant difference in survival between the groups. CONCLUSIONS: LIVE users had higher rates of out-patient pulmonology visits, in-patient admissions, and general practitioner visit costs, but no difference in overall costs or mortality. This study highlights the limitations of evaluating eHealth interventions through observational research and the need for a randomized controlled trial.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".