Successful hospital readmission reduction initiatives: Top five strategies to consider implementing today
Bibliographic record
Abstract
Only one quarter of U.S. hospitals demonstrated low enough levels of 30 day readmission rates to avoid penalties imposed by the Hospital Readmissions Reduction Program (HRRP) in 2016. Previous work describes interventions for reducing hospital readmission rates; however, without a comprehensive analysis of these interventions, healthcare leaders cannot prioritize strategies for implementation within their healthcare environment. This comparative study identifies the most effective interventions to reduce unplanned 30-day readmissions. The MEDLINE-PubMed database was used to conduct a systematic review of existing literature about interventions for 30-day readmission reduction published from 2006 through 2017. Data were extracted on hospital type, setting, disease type, intervention type, study sample, and impact level. Of 4,886 citations, 508 articles were reviewed in full-text, and 90 articles met the inclusion criteria. Based on the three analytic methodologies of means, weighted means, and pooled estimated impact level, the most effective interventions to reduce unplanned 30-day admissions were identified as collaboration with clinical teams and/or community providers, post-discharge home visits, telephone follow-up calls, patient/family education, and discharge planning. Commonly, all five interventions identify patient level engagement for success. The findings reveal the need for shared accountability towards desired outcomes among health systems, providers, and patients while providing hospital leaders with actionable strategies that can effectively reduce 30-day readmission rates.
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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 | 0.026 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".