A systematic review of economic analyses of home-based telerehabilitation
Bibliographic record
Abstract
Telerehabilitation, or the delivery of rehabilitation using information and communication technologies, may improve timely and equitable access to rehabilitation services at home. A systematic literature review was conducted of studies that formally documented the costs and effects of home-based telerehabilitation versus in-person rehabilitation across all health conditions. Six electronic databases were searched from inception to 13 July 2021 (APA, PsycInfo, CINAHL, Embase, EmCare, Medline (Ovid), and PubMed) using a protocol developed by a medical librarian. A quality appraisal of full economic evaluation studies was conducted using the Drummond 10-point quality checklist. Thirty-five studies were included in this review covering various rehabilitation types and diverse populations. The majority were published in the last six years. Available evidence suggests that telerehabilitation may result in similar or lower costs as compared to in-person rehabilitation for the health care system and for patients. However, the impact of telerehabilitation on long-term clinical outcomes and health-related quality of life remains unclear. More high quality and robust economic evaluations exploring the short- and long-term costs and other impacts of telerehabilitation on patients, caregivers, and health care systems across all types of patient populations are still required.Implications for rehabilitationHome-based telerehabilitation may reduce barriers in access to care for individuals living in the community.Economic analyses can inform health care system decision-making by evaluating the costs and effects associated with telerehabilitation.This study found that telerehabilitation may result in similar or lower costs as in-person rehabilitation; however, its impact on health-related quality of life is unclear. Home-based telerehabilitation may reduce barriers in access to care for individuals living in the community. Economic analyses can inform health care system decision-making by evaluating the costs and effects associated with telerehabilitation. This study found that telerehabilitation may result in similar or lower costs as in-person rehabilitation; however, its impact on health-related quality of life is unclear.
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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 | 0.026 | 0.127 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".