Implementing Home Health Monitoring for Chronic Disease Management in British Columbia
Bibliographic record
Abstract
Home health monitoring (HHM) enables health care providers to monitor a patient’s health status remotely using digital technology. In 2013, the government of British Columbia (BC) partnered with a telecommunications provider and invested $52M in HHM programs for patients with complex care needs. Telehealth has evolved over the past two decades to improve access and delivery of care, with HHM emerging as a strategy for proactive chronic disease management. The goals of the provincial HHM initiative are to reduce acute care utilization, reduce health system costs and improve patient self-management of chronic conditions. Following a series of successful pilot projects, funding for expanded HHM initiatives was made available through a provincial Strategic Investment Fund and continued support from Canada Health Infoway. Evaluations of several HHM programs demonstrated high levels of patient satisfaction, reduced emergency department visits, health system cost savings, and improved patient self-care and quality of life. Optimizing referral rates and expanding HHM programs to include a wider range of chronic conditions are opportunities for future growth, with sustainability dependent on securing long-term funding sources.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".