Cost Comparison Between Telemonitoring and Usual Care of Heart Failure: A Systematic Review
Bibliographic record
Abstract
Heart failure (HF) is associated with high direct and indirect costs to the patients and the healthcare system. This systematic review aims to analyze existing economic data to determine whether telemonitoring of patients with HF will result in decreased costs. The Scopus and PubMed databases were searched independently by two reviewers for journal articles that reported on an economic analysis (i.e., calculated monetary amounts or percentage change in costs) of a study using a HF telemonitoring system. Only articles describing telemonitoring systems with a component of home physiological measurements were included. Eleven articles met the inclusion criteria, describing 10 different HF telemonitoring systems. Nine of the 10 studies analyzed the direct costs to the healthcare system. All the studies found cost reductions from telemonitoring compared to usual care, which ranged between 1.6% and 68.3%. Cost reductions were mainly attributed to reduced hospitalization expenditures. Only one study discussed the impact of HF telemonitoring on direct patient costs. The study found a 3.5% lower travel cost for patients using telemonitoring compared to those in the usual care group. The single study that was found for indirect costs described the willingness to pay for telemedicine by patients with HF (55% of the patients with HF were willing to pay $20 to access telemedicine, and 19% were willing to pay $40). Available data from existing studies suggest that although HF telemonitoring will require an initial financial investment, it will substantially reduce costs in the long term, particularly by reducing rehospitalization and travel costs.
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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 | 0.008 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".