Home Telehealth for Chronic Disease Management: Selected Findings of a Narrative Synthesis
Bibliographic record
Abstract
BACKGROUND: Chronic disease has become an increasingly important issue for individuals and healthcare organizations across Canada. Home telehealth may have the potential to alleviate the economic and social challenges associated with rising rates of chronic disease. An aim of this review was to gather and synthesize the evidence on the effectiveness of home telehealth in chronic disease management. MATERIALS AND METHODS: We searched the Medline, EMBASE, Web of Science, CINAHL, and PAIS databases for studies published in English from January 1, 2005, and December 31, 2010. Academic publications, white papers, and gray literature were all considered eligible for inclusion, provided an original research element was present. Articles were screened for relevance. RESULTS: One hundred one articles on quantitative or mixed-methods studies reported the effects of home telehealth on disease state, symptoms, and quality of life in chronic disease patients. Studies were consistent in finding that home telehealth was equivalent or superior to usual care. CONCLUSIONS: The literature strongly supports the use of home telehealth as an equally effective alternative to usual care. The circumstances under which home telehealth emerges as significantly better than usual care have not been extensively researched. Further research into factors affecting the effectiveness of home telehealth would support more widespread realization of telehealth's potential benefits.
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".