Telemedicine in Pediatrics: Systematic Review of Randomized Controlled Trials
Bibliographic record
Abstract
BACKGROUND: Telemedicine modalities, such as videoconferencing, are used by health care providers to remotely deliver health care to patients. Telemedicine use in pediatrics has increased in recent years. This has resulted in improved health care access, optimized disease management, progress in the monitoring of health conditions, and fewer exposures to patients with illnesses during pandemics (eg, the COVID-19 pandemic). OBJECTIVE: We aimed to systematically evaluate the most recent evidence on the feasibility and accessibility of telemedicine services, patients' and care providers' satisfaction with these services, and treatment outcomes related to telemedicine service use among pediatric populations with different health conditions. METHODS: Studies were obtained from the PubMed database on May 10, 2020. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. In this review, we included randomized controlled trials from the last 10 years that used a telemedicine approach as a study intervention or assessed telemedicine as a subspecialty of pediatric care. Titles and abstracts were independently screened based on the eligibility criteria. Afterward, full texts were retrieved and independently screened based on the eligibility criteria. A standardized form was used to extract the following data: publication title, first author's name, publication year, participants' characteristics, study design, the technology-based approach that was used, intervention characteristics, study goals, and study findings. RESULTS: In total, 11 articles met the inclusion criteria and were included in this review. All studies were categorized as randomized controlled trials (8/11, 73%) or cluster randomized trials (3/11, 27%). The number of participants in each study ranged from 22 to 400. The health conditions that were assessed included obesity (3/11, 27%), asthma (2/11, 18%), mental health conditions (1/11, 9%), otitis media (1/11, 9%), skin conditions (1/11, 9%), type 1 diabetes (1/11, 9%), attention deficit hyperactivity disorder (1/11, 9%), and cystic fibrosis-related pancreatic insufficiency (1/11). The telemedicine approaches that were used included patient and doctor videoconferencing visits (5/11, 45%), smartphone-based interventions (3/11, 27%), telephone counseling (2/11, 18%), and telemedicine-based screening visits (1/11, 9%). The telemedicine interventions in all included studies resulted in outcomes that were comparable to or better than the outcomes of control groups. These outcomes were related to symptom management, quality of life, satisfaction, medication adherence, visit completion rates, and disease progression. CONCLUSIONS: Although more research is needed, the evidence from this review suggests that telemedicine services for the general public and pediatric care are comparable to or better than in-person services. Patients, health care professionals, and caregivers may benefit from using both telemedicine services and traditional, in-person health care services. To maximize the potential of telemedicine, future research should focus on improving patients' access to care, increasing the cost-effectiveness of telemedicine services, and eliminating barriers to telemedicine use.
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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 | 0.027 | 0.101 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.018 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".