Pediatric Tele–Home Care Compared to Usual Care: Cost-Minimization Analysis
Bibliographic record
Abstract
BACKGROUND: Although home hospitalization has been a well-known and widespread practice for some time in the adult population, it has not been the same case in the pediatric setting. Simultaneously, telemedicine tools are a facilitator of the change in the health care model, which is increasingly focused on home care. In a pioneering way in Spain, the in-home hospitalization program of the Hospital Sant Joan de Déu in Barcelona allows the child to be in their home environment at the time they are being monitored and clinically followed by the professionals. Besides being the preferred option for families, previous experience suggests that pediatric home hospitalization reduces costs, primarily thanks to savings on the structural cost of the stay. OBJECTIVE: The aim of this study is to compare the average cost of a discharge by tele-home care with the usual care and to analyze the main drivers of the differential costs of both care models. METHODS: A cost-minimization analysis is conducted under a hospital's perspective, based on observational data, and estimated retrospectively. A historical control group of similar patients in terms of clinical casuistry to children hospitalized at home was used for comparison. RESULTS: A 24-hour stay at the hospital costs US $574.19, while the in-home hospitalization costs US $301.71 per day, representing a saving of almost half (48%) of the cost compared to usual care. The main saving drivers were the personnel costs (US $102.83/US $284.53, 35.5% of the total), intermediate noncare costs (US $6.09/US $284.53, 33.17%), and structural costs (US $55.16/US $284.53, 19.04%). Home hospitalization involves a total stay 27.61% longer, but at almost half the daily cost, and thus represents a saving of US $176.70 (9.01%) per 24-hour stay. CONCLUSIONS: The cost analysis conducted under a hospital perspective shows that pediatric tele-home care is 9% cheaper compared to regular hospital care. These results motivate the most widespread implementation of the service from the point of view of economic efficiency, adding to previous experiences that suggest that it is also preferable from the perspective of user satisfaction.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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".