Proportion of hospital readmissions deemed avoidable: a systematic review
Bibliographic record
Abstract
BACKGROUND: Readmissions to hospital are increasingly being used as an indicator of quality of care. However, this approach is valid only when we know what proportion of readmissions are avoidable. We conducted a systematic review of studies that measured the proportion of readmissions deemed avoidable. We examined how such readmissions were measured and estimated their prevalence. METHODS: We searched the MEDLINE and EMBASE databases to identify all studies published from 1966 to July 2010 that reviewed hospital readmissions and that specified how many were classified as avoidable. RESULTS: Our search strategy identified 34 studies. Three of the studies used combinations of administrative diagnostic codes to determine whether readmissions were avoidable. Criteria used in the remaining studies were subjective. Most of the studies were conducted at single teaching hospitals, did not consider information from the community or treating physicians, and used only one reviewer to decide whether readmissions were avoidable. The median proportion of readmissions deemed avoidable was 27.1% but varied from 5% to 79%. Three study-level factors (teaching status of hospital, whether all diagnoses or only some were considered, and length of follow-up) were significantly associated with the proportion of admissions deemed to be avoidable and explained some, but not all, of the heterogeneity between the studies. INTERPRETATION: All but three of the studies used subjective criteria to determine whether readmissions were avoidable. Study methods had notable deficits and varied extensively, as did the proportion of readmissions deemed avoidable. The true proportion of hospital readmissions that are potentially avoidable remains unclear.
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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 | 0.031 | 0.156 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| 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".