National Trends in Pediatric Ambulatory Telehealth Utilization and Follow-Up Care
Bibliographic record
Abstract
Introduction: As telemedicine becomes standard in pediatrics, further research is required to ensure optimal adoption. This study seeks to characterize visits best suited for telemedicine by analyzing usage trends and encounter attributes associated with immediate in-person follow-up. Methods: Analysis of ambulatory pediatric encounters from the first quarter of 2021 in a nationwide insurance claims database. Results: Telemedicine comprised 9.5% (138,346) of ambulatory encounters. Among telemedicine visits, 7.5% (10,304) yielded in-person follow-up within 3 days. Encounters involving infants and diagnoses of the perinatal period were most frequently followed by in-person visits (11% and 20%, respectively). Mental health visits were least likely to have in-person follow-up. Conclusions: In 2021, telemedicine remained a common modality of care in pediatrics. Varying medical needs still require in-person evaluation, whereas other diagnoses may be conducive to even greater expansion. Insights from this study inform further research into optimization of pediatric telemedicine utilization and development of guidelines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".