Improving heart failure transitional care after hospital discharge: A quality improvement project
Bibliographic record
Abstract
Background and objective: The burden of heart failure is growing, affecting more than 6 million Americans and an estimated of 26 million worldwide. Heart failure is the most common cause of hospital readmission in the United States and is identified as a marker of poor health outcomes. Thirty day readmission contribute to more than $30 billion dollars in health care expenditures, underscoring a need for the development and implementation of programs that reduce readmission and improve outcomes for individuals with heart failure. The purpose of this quality improvement project was to implement a heart failure education program to increase attendance to a transitional care clinic and reduce 30-day readmissions.Methods: We included 22 individuals who received heart failure education, focused on symptom management and transitional care. Descriptive and statistical analyses were performed to examine attendance to the transitional care clinic and 30-day readmission.Results: There was a statistical significance between individuals attending follow-up at the designated transitional care clinic and 30-day hospital readmission (p ≤ .05). Of the (N = 22) participants, 64% were not readmitted into the hospital 30 days after discharge.Conclusions: The findings of this project demonstrate that a nurse-led evidence-based heart failure education program can improve attendance to transitional care programs and reduce 30-day readmissions. A well-designed plan for transitional care remains a critical component of patient care necessary to address complications and optimize continuity of care after discharge.
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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 | 0.028 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".