A comparison of telehomecare delivery models for congestive heart failure in three Canadian jurisdictions
Bibliographic record
Abstract
The quality of health care is suffering as Canadians are experiencing a rise in chronic diseases, adding pressure to the existing inefficient health care delivery system and contributing to the increase in health care costs. Advances in information technology and e-Health initiatives have enabled the creation of Remote Patient Monitoring (RPM), which assists individuals in self-managing their own chronic diseases; this paper discusses three Canadian RPM implementations in Ontario, British Columbia and Alberta that support self-management for patients suffering from congestive heart failure (CHF). The Ontario Telemedicine Network (OTN) acts as project manager to a province wide Telehomecare Expansion Project; the project allows Local Health Integrated Networks (LHINs) to run a telehomecare (THC) program through appropriate host organizations, such as hospitals or Community Care Access Centres (CCACs). In British Columbia (B.C.), the Vancouver Island Health Authority (VIHA) plans, manages and delivers Home Health Monitoring (HHM) to residents living in the region. HHM is a THC collaboration between the VIHA Home and Community Care and the Heart Health Departments. Both the OTN THC Expansion Project and the B.C. HHM programs utilize Telus Health RPM as a solution to enable home care for patients with CHF. In Alberta, health care is planned and provided through Alberta Health Services. In partnership with GE Health Solutions, Alberta Health Services established the MyHomeHealth Program to remotely monitor patients with CHF. The OTN and VIHA THC projects yielded significant decreases in hospitalizations, length of stay and improved patient satisfaction and selfmanagement abilities. The MyHomeHealth Program has made no progress since its establishment. THC is an effective way for people with CHF to self-manage their chronic conditions, and improve their quality of life. The evaluations discussed in this paper provide evidence that THC leads to reductions in provincial health care costs; however, independent evaluations of actual costs should be conducted to provide transparency on funding allocations. For these reasons, all Canadian jurisdictions should investigate implementing a THC Delivery Model that is suitable to their health care system framework.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".