Home telehealth for chronic disease management: A systematic review and an analysis of economic evaluations
Bibliographic record
Abstract
OBJECTIVES: The research objectives were two-fold: first, to systematically review the literature on the cost-effectiveness of home telehealth for chronic diseases, and second to develop a framework for the conduct of economic evaluation of home telehealth projects for patients with chronic diseases. METHODS: A comprehensive literature search identified twenty-two studies (n = 4,871 patients) on home telehealth for chronic diseases published between 1998 and 2008. Studies were reviewed in terms of their methodological quality and their conclusions. RESULTS: Home telehealth was found to be cost saving from the healthcare system and insurance provider perspectives in all but two studies, but the quality of the studies was generally low. An evaluative framework was developed which provides a basis to improve the quality of future studies to facilitate improved healthcare decision making, and an application of the framework is illustrated using data from an existing program evaluation of a home telehealth program. CONCLUSIONS: Current evidence suggests that home telehealth has the potential to reduce costs, but its impact from a societal perspective remains uncertain until higher quality studies become available.
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 | 0.039 | 0.132 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".