A Systematic Review of the Key Indicators for Assessing Telehomecare Cost-Effectiveness
Bibliographic record
Abstract
Telehomecare is considered one of the most successful applications of telehealth. However, despite increasing evidence of telehomecare benefits, the diffusion of these services is still limited. Decision-makers need strong evidence in order to expand the development of telehomecare to various populations, regions, and health conditions. The objective of this review is to provide a basis for decision-making by identifying common indicators from the literature on telehomecare. A comprehensive review of the literature on the cost-effectiveness of telehomecare was conducted in specialized bibliographic databases. A total of 23 studies met the inclusion criteria. First, selected studies were analyzed to identify and classify the indicators that better addressed the cost-effectiveness impacts of telehomecare projects. Then, a synthesis of the evidence was done by exploring the relative cost-effectiveness of telehomecare applications. The analyses show that there is fair evidence of cost-effectiveness for many telehomecare applications. However, the heterogeneity among cost-effectiveness indicators in the applications reviewed and the methodological limitations of the studies impede the possibility of generalizing the findings. This suggests the need for a set of common indicators that could be applied for assessing the costeffectiveness of telehomecare projects. This review provides knowledge on the indicators available for assessing cost-effectiveness in telehomecare projects. It appears that the specific context in which the projects take place, meaning different patients, environments, technologies, and healthcare systems, should be taken into account when selecting indicators for assessing telehomecare cost-effectiveness.
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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 | 0.036 | 0.168 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.016 |
| Bibliometrics | 0.025 | 0.025 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".