A Strategy to Reduce Heart Failure Readmissions and Inpatient Costs
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to evaluate the effect of a disease management intervention on rehospitalization rates in hospitalized heart failure (HF) patients. METHODS: Patients treated with the TEACH-HF intervention that included Teaching and Education, prompt follow-up Appointments, Consultation for support services, and Home follow-up phone calls (TEACH-HF) from January 2010 to January 2012 constituted the intervention group (n = 548). Patients treated from January 2007 to January 2008 constituted the usual care group (n = 485). RESULTS: Group baseline characteristics were similar with 30-day readmission rates significantly different (19% usual care vs. 12% for the intervention respectively (P = 0.003)). Patients in the usual care group were 1.5 times more likely to be hospitalized (95% CI: 1.2 - 1.9; P = 0.001) compared to the intervention group. A savings of 641 bed days with potential revenue of $640,000 occurred after TEACH-HF. CONCLUSIONS: The TEACH-HF intervention was associated with significantly fewer hospital readmissions and savings in bed days.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".