Implementation of a transitional care programme for heart failure and its impact on 30-day readmissions and emergency department visits
Bibliographic record
Abstract
Abstract Background/Introduction Heart failure (HF) is a leading cause of morbidity and mortality, often resulting in repeated hospital admissions. Early post-discharge follow-up is frequently suboptimal, contributing to high 30-day readmission and emergency department (ED) visit rates. A multidisciplinary transitional care programme was developed to bridge this gap, aiming to support patients in the vulnerable period immediately following hospital discharge. Purpose To retrospectively determine whether enrolment in a structured transitional care programme could reduce 30-day hospital readmissions and ED presentations for patients discharged with HF compared to standard of care. Methods Between 2017 and 2024, patients admitted with HF were referred for enrolment in a 30-day transitional care programme. Enrollees received personalised care plans developed by dedicated care managers in collaboration with HF physicians and primary care physicians. Key features included patient education, remote monitoring, 24-hour telephone access, and scheduled follow-up. A matching process using the Health-Based Allocation Model Inpatient Group (HIG) score was applied to compare participants with non-enrolled controls. The primary outcome was a composite of 30-day readmission or ED visits. Secondary outcomes included 30-day readmission and 30-day ED visits analysed separately. Results A total of 511 patients were enrolled and matched 1:1 with controls based on HIG scores. Enrolment in the programme reduced the combined outcome of 30-day readmission or ED visits by 37.6% (odds ratio [OR] 0.62, 95% confidence interval [CI]: 0.47–0.83, p<0.001), with a number needed to treat (NNT) of 8. Secondary analyses revealed 38.4% lower odds of readmission alone (OR 0.616, 95% CI: 0.447–0.848, p=0.003; NNT=14) and a 36.3% reduction in ED presentations (OR 0.637, 95% CI: 0.480–0.845, p=0.002; NNT=15). Conclusion A structured transitional care programme offering multidisciplinary support, patient education, and early post-discharge follow-up was associated with a significant decrease in 30-day readmissions and ED presentations among HF patients. These findings underscore the importance of coordinated care in improving HF outcomes and suggest that similar programmes could be adapted to optimise resource use and reduce healthcare burdens elsewhere.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".