Quality improvement interventions boosts cardiac rehabilitation referrals: patient education does it eightfold
Bibliographic record
Abstract
Abstract Background/Introduction Quality improvement interventions often follow frameworks, including Plan-Do-Study-Act (PDSA), Define, Measure, Analyse, Improve, and Control (DMAIC), Six Sigma, Lean, and audit and feedback, which use iterative cycles and data collection to identify gaps, set goals, and test solutions. Such interventions could enhance adherence to clinical practice standards and reduce variability in quality between cardiac rehabilitation services. However, synthesised evidence of the characteristics and effectiveness of quality improvement interventions within cardiac rehabilitation programs is lacking. Purpose This systematic review and meta-analysis aimed to evaluate the effects of quality improvement interventions on the standard of cardiac rehabilitation processes and patient outcomes. Methods Scopus, Cochrane Central Register of Controlled Trials, Medline, Embase, and Cumulative Index to Nursing and Allied Health Literature were searched from January 2000 to November 2024. The review protocol was prospectively registered in PROSPERO. Included studies (1) implemented a quality improvement framework or stated that a "quality improvement" method was used, (2) were conducted in cardiac rehabilitation settings, and (3) aimed to improve patient and/or program outcomes. Screening was done by two independent reviewers. Meta-analyses used Review Manager v5.3, random-effects model. Data were presented as odds ratio (OR) with 95% confidence interval (CI). Studies not suitable for meta-analysis were reported narratively. Results The search yielded 5,100 studies, of which 15 (76,856 participants) were included. Meta-analysis of 11 studies (17,010 participants) showed that quality improvement interventions significantly improved cardiac rehabilitation processes, primarily referral (OR 5.25; CI 3.11–8.87). Subgroup analyses of specific strategies revealed that patient education had the greatest impact (OR 8.37; CI 4.32–16.21), followed by technology inclusion (OR 5.56; CI 2.67–11.58) and workflow changes (OR 5.31; CI 2.53–11.12), among other strategies (staff education, staffing adjustments, auditing, quality and/or performance indicators, and feedback cycles). Narrative synthesis indicated that quality improvement interventions increased program attendance, completion, adherence to prescription of guideline-directed medical therapy, and shortened wait times. Few studies reported on patient outcomes. Conclusion Quality improvement interventions improve referral to cardiac rehabilitation by up to eight times, with patient education being the most effective. These interventions may also reduce wait times and improve program attendance and completion; however, more evidence is needed to draw firm conclusions. Future studies should further explore patient outcomes.
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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 | 0.029 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".