Assessing the Impact of Telemedicine Interventions on Health Care Costs and Utilization: A Scoping Review (Preprint)
Bibliographic record
Abstract
BACKGROUND The utilization of telemedicine has increased notably since the onset of the pandemic. Understanding the influence of telemedicine on health care costs and utilization can contribute to the monitoring and evaluation of telemedicine programs. OBJECTIVE This scoping review aimed to document the potential impact of telemedicine on health care costs and utilization across diverse health care contexts and to offer a summary of the statistical methodological approaches employed in assessing the impact of telemedicine on health care costs and utilization. METHODS A literature search was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Extension for Scoping Reviews guidelines, spanning the last 10 years across 3 electronic databases: PubMed/Medline, Web of Science, and Scopus. The search strategy was in accordance with the PICO criteria; patients were defined as the target population; telehealth or telemedicine was defined as the intervention; and standard care or before-and-after self-comparison was defined as the comparison, with health care costs and utilization as the outcome measures. Additionally, the six different structural layers of the TOAST framework for telehealth services were utilized to characterize the interventions. The findings were synthesized and are presented in tables and figures for clarity. RESULTS Out of a total of 4,454 identified articles, 14 were selected for review, with approximately 36% (n=5) focusing on chronic conditions. The delivery modalities included telephone call, videoconference, web portal, and smartphone applications, mainly spanning teleconsultation, telemonitoring, and teletherapy with clinicians and nursing support or health care team involvement. Approximately 86% of the studies employed standard face-to-face clinical visits for the control group. Six out of the 12 studies evaluating health care costs and four out of the seven studies assessing health care utilization revealed statistically significant improvements in telehealth compared to the control group. In addition, approximately 43% of the studies conducted univariate and multivariable analyses, with half of the studies incorporating adjusted analyses to control for confounding variables. CONCLUSIONS Our scoping review suggested that, in the treatment phase, compared with standard face-to-face clinical visits, telemedicine has the potential to decrease health care costs and optimally utilize health resources. Additionally, a regression model was the most commonly used statistical approach for assessing the impact of telemedicine.
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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 | 0.031 | 0.151 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".