Assessing the Impact of Telemedicine Interventions on Health Care Costs and Utilization: A Scoping Review
Bibliographic record
Abstract
Background: The impact of telemedicine on health care costs and utilization has not been comprehensively assessed across diverse health care settings. This scoping review aimed to explore these impacts, focusing on the variations in intervention types. Methods: A literature search followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines, covering the last 10 years in PubMed/Medline, Web of Science, and Scopus. The Population, Intervention, Comparison, Outcome framework was employed to define the population (patients), intervention (telemedicine/telehealth), comparator (standard care or pre-telemedicine), and outcomes (health care costs and utilization). Both randomized controlled trials and observational studies were included in the search. The search focused specifically on health care institutions or hospitals as the level of inquiry. Telemedicine interventions were characterized using the TOAST framework’s six layers, while the four phases of the health care process (prevention, diagnosis, treatment, and recovery) were incorporated to further contextualize the interventions. Studies were synthesized and presented in tables and figures to provide an organized summary of the findings. Results: From 4,454 articles, 14 met inclusion criteria, with 12 examining costs and seven utilization. Six studies reported significant cost reductions with telemedicine compared with standard care. In utilization, four out of seven studies showed significant improvements. Conclusion: This review indicates that telemedicine may reduce health care costs and enhance resource utilization during the treatment phase compared to traditional in-person visits.
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 | 0.034 | 0.138 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.024 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".