Impact of Synchronous Telemedicine Models on Clinical Outcomes in Pediatric Acute Care Settings: A Systematic Review*
Bibliographic record
Abstract
OBJECTIVES: To evaluate the impact of synchronous telemedicine models on the clinical outcomes in pediatric acute care settings. DATA SOURCES: Citations from EBM Reviews, MEDLINE, EMBASE, Global Health, PubMed, and CINAHL. STUDY SELECTION: We identified studies that evaluated the impact of synchronous telemedicine on clinical outcomes between January 2000 and April 2018. All studies involving acutely ill children in PICUs, pediatric cardiac ICUs, neonatal ICUs, and pediatric emergency departments were included. Publication inclusion criteria were study design, participants characteristics, technology type, interventions, settings, outcome measures, and languages. DATA EXTRACTION: Two authors independently screened each article for inclusion and extracted information, including telecommunication method, intervention characteristics, sample characteristics and size, outcomes, and settings. DATA SYNTHESIS: Out of the 789 studies initially identified, 24 were included. The six main outcomes of interest published were quality of care, hospital and standardized mortality rate, transfer rate, complications and illness severity, change in medical management, and length of stay. The use of synchronous telemedicine results improved quality of care and resulted in a decrease in the transfer rate (31-87.5%) (four studies), a shorter length of stay (8.2 vs 15.1 d) (six studies), a change or reinforcement of the medical care plan, a reduction in complications and illness severity, and a low hospital and standardized mortality rate. Overall, the quality of the included studies was weak. CONCLUSIONS: Despite the broad recommendations found for using telemedicine in pediatric acute care settings, high-quality evidence of its impacts is still lacking. Further robust studies are needed to better determine the clinical effectiveness and the associated impacts of telemedicine in pediatric acute care settings.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".