Cardiac Rehabilitation Quality Improvement
Bibliographic record
Abstract
PURPOSE: Despite evidence of the effectiveness of cardiac rehabilitation (CR), there is wide variability in programs, which may impact their quality. The objectives of this review were to (1) evaluate the ways in which we measure CR quality internationally; (2) summarize what we know about CR quality and quality improvement; and (3) recommend potential ways to improve quality. METHODS: For this narrative review, the literature was searched for CR quality indicators (QIs) available internationally and experts were also consulted. For the second objective, literature on CR quality was reviewed and data on available QIs were obtained from the Canadian Cardiac Rehabilitation Registry (CCRR). For the last objective, literature on health care quality improvement strategies that might apply in CR settings was reviewed. RESULTS: CR QIs have been developed by American, Canadian, European, Australian, and Japanese CR associations. CR quality has only been audited across the United Kingdom, the Netherlands, and Canada. Twenty-seven QIs are assessed in the CCRR. CR quality was high for the following indicators: promoting physical activity post-program, assessing blood pressure, and communicating with primary care. Areas of low quality included provision of stress management, smoking cessation, incorporating the recommended elements in discharge summaries, and assessment of blood glucose. Recommended approaches to improve quality include patient and provider education, reminder systems, organizational change, and advocacy for improved CR reimbursement. An audit and feedback strategy alone is not successful. CONCLUSIONS: Although not a lot is known about CR quality, gaps were identified. The quality improvement initiatives recommended herein require testing to ascertain whether quality can be improved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.064 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".