Interventions to prevent heart failure readmissions: The rationale for nurse-led heart failure programs
Bibliographic record
Abstract
Background: Readmission to hospitals for heart failure is one of the greatest economic burdens on Medicare, and has become a major focus of healthcare reform. In an attempt to stem the overwhelming number of readmissions and improve heart failure outcomes, hospitals have employed multiple interventions. Nurse-led heart failure management programs have been an effective strategy in reducing hospital readmissions for heart failure. Purpose: We conducted an integrative review of the literature that assessed the value of interventions to reduce heart failure readmission rates. We focused on the important role of nursing care in successfully implementing many of these interventions. Methods: An integrative review of the literature was performed. A computerized search of PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Cochrane Library (reviews and clinical trials) was performed to locate articles published from 2004 to 2014. Key words used included “heart failure nursing” , “heart failure readmissions”, “heart failure programs” and “interventions for heart failure readmission”. Preference was placed on articles published in the last 10 years. Articles referenced by national heart failure guideline documents and expert consensus statements were given a high priority. Eighty-eight articles were screened initially by two reviewers; these were then screened to leave 40 relevant articles. Conclusions: Several specific interventions have a proven favorable effect in reducing heart failure readmissions. These include optimal medical management, patient education and self-care instruction, and ensuring adequate post-discharge follow-up. Despite this knowledge there remains a wide variation of readmission rates across the U nited S tates . This may be partly due to the variability in the adequate implementation of interventions and/or the absence of a required number of interventions in different centers. Each single intervention in itself has only a very small beneficial effect. The implementation of several interventions is essential to produce a meaningful reduction in heart failure readmissions. The ability to successfully employ numerous interventions together may explain the promising results of structured nurse-led heart failure programs.
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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 | 0.049 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".