What is the impact of systems of care for heart failure on patients diagnosed with heart failure: a systematic review
Bibliographic record
Abstract
BACKGROUND: Hospital admissions for heart failure are predicted to rise substantially over the next decade placing increasing pressure on the health care system. There is an urgent need to redesign systems of care for heart failure to improve evidence-based practice and create seamless transitions through the continuum of care. The aim of the review was to examine systems of care for heart failure that reduce hospital readmissions and/or mortality. METHOD: August 2015. Inclusion criteria for studies were: English language, randomised controlled trials, non-randomised trials and cohort studies of systems of care for patients diagnosed with heart failure and aimed at reducing hospital readmissions and/or mortality. Three reviewer authors independently assessed articles for eligibility based on title and abstract and then full-text. Quality of evidence was assessed using Newcastle-Ottawa Scale for non-randomised trials and GRADE rating tool for randomised controlled trials. RESULTS: We included 29 articles reporting on systems of care in the workforce, primary care, in-hospital, transitional care, outpatients and telemonitoring. Several studies found that access to a specialist heart failure team/service reduced hospital readmissions and mortality. In primary care, a collaborative model of care where the primary physician shared the care with a cardiologist, improved patient outcomes compared to a primary physician only. During hospitalisation, quality improvement programs improved the quality of inpatient care resulting in reduced hospital readmissions and mortality. In the transitional care phase, heart failure programs, nurse-led clinics, and early outpatient follow-up reduced hospital readmissions. There was a lack of evidence as to the efficacy of telemonitoring with many studies finding conflicting evidence. CONCLUSION: Redesigning systems of care aimed at improving the translation of evidence into clinical practice and transitional care can potentially improve patient outcomes in a cohort of patients known for high readmission rates and mortality.
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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 | 0.015 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.014 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".