Combining training in knowledge translation with quality improvement reduced 30‐day heart failure readmissions in a community hospital: a case study
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: Training programmes in evidence-based practice (EBP) frequently fail to translate their content into practice change and care improvement. We linked multidisciplinary training in EBP to an initiative to decrease 30-day readmissions among patients admitted to a community teaching hospital for heart failure (HF). METHODS: Hospital staff reflecting all services and disciplines relevant to care of patients with HF attended a 3-day innovative capacity building conference in evidence-based health care over a 3-year period beginning in 2009. The team, facilitated by a conference faculty member, applied a knowledge-to-action model taught at the conference. We reviewed published research, profiled our population and practice experience, developed a three-phase protocol and implemented it in late 2010. We tracked readmission rates, adverse clinical outcomes and programme cost. RESULTS: The protocol emphasized patient education, medication reconciliation and transition to community-based care. Senior administration approved a full-time nurse HF coordinator. Thirty-day HF readmissions decreased from 23.1% to 16.4% (adjusted OR = 0.64, 95% CI = 0.42-0.97) during the year following implementation. Corresponding rates in another hospital serving the same population but not part of the programme were 22.3% and 20.2% (adjusted OR = 0.87, 95% CI = 0.71-1.08). Adherence to mandated HF quality measures improved. Following a start-up cost of $15 000 US, programme expenses balanced potential savings from decreased HF readmissions. CONCLUSION: Training of a multidisciplinary hospital team in use of a knowledge translation model, combined with ongoing facilitation, led to implementation of a budget neutral programme that decreased HF readmissions.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 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".