Quality Management in Virtual Cardiac Rehabilitation: Global Frameworks and Key Considerations
Bibliographic record
Abstract
Cardiac rehabilitation (CR) is a comprehensive, multidisciplinary program essential for the secondary prevention of cardiovascular disease. Despite its clinical benefits, participation in CR remains suboptimal due to barriers such as limited accessibility, time constraints, socioeconomic challenges, and structural inequities. In response, virtual and remote CR models have gained attention, accelerated by the advancement of digital health technologies and the COVID-19 pandemic. To ensure the effectiveness and safety of these alternative models, robust quality standards and indicators are crucial. This review examines national CR quality management frameworks in the UK, USA, Australia, and Canada. These countries have developed standardized core components and multidimensional quality indicators at the system, program, and patient levels. Based on these, the review explores how existing quality frameworks can be adapted to virtual CR models, where additional considerations such as patient eligibility, digital platform certification, real-time monitoring, data privacy, and interoperability are needed. In addition, other factors for successful implementation of virtual CR include personalized care, user-friendly technology, Artificial intelligence (AI)-driven data management, and supportive health policies including reimbursement systems. Establishing a standardized quality framework tailored to digital settings will be critical to ensure equitable access, clinical effectiveness, and sustainable delivery of virtual CR services.
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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 | 0.053 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".