A meta‐analysis of hospital 30‐day avoidable readmission rates
Bibliographic record
Abstract
RATIONALE AND OBJECTIVES: Urgent readmission to hospital is commonly used to measure hospital quality of care. Hospitals that measure the proportion of urgent readmissions judged avoidable need to know previously published rates for comparison. In this study, we generated a literature-based estimate for the proportion of 30-day urgent readmissions deemed avoidable for hospitals to use to gauge their performance in avoidable readmissions. METHODS: We searched the Medline and Embase databases to identify published studies that reported the proportion of 30-day urgent readmissions deemed avoidable. We then modelled the overall proportion of 30-day urgent readmissions deemed avoidable. RESULTS: We included 16 studies that used a wide variety of patients and a diverse range of methods to classify readmissions as avoidable. Studies reported a broad range for the proportion of urgent 30-day readmissions deemed avoidable. Overall, 848 of 3669 readmissions (23.1%, 95% confidence interval, 21.7-24.5) of 30-day urgent readmissions were classified as avoidable. This proportion varied significantly based on hospital teaching status and number of reviewers for each case [teaching hospitals: with one reviewer, 9.3% (4.2-19.3); with >1 reviewer, 21.6% (13.2-33.3); non-teaching hospital: with one reviewer, 32.2% (11.4-63.9); with >1 reviewer, 39.9% (37.6-42.2)]. Significant heterogeneity remained between studies even after clustering studies by these covariates. CONCLUSIONS: Less than one in four readmissions were deemed avoidable. Health system planners need to use caution in interpreting all cause readmission statistics as they are only partially influenced by quality of care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.061 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.049 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".