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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".