The quality of transitions from hospital to home: A hospital-based cohort study of patient groups with high and low readmission rates
Bibliographic record
Abstract
Introduction The quality of transitions from the hospital to home is critical for preventing readmissions. The aims of this study were to evaluate variations in the quality of transitions across groups of patients and across hospitals with high and low readmission rates and to study the impact of transitions on postdischarge outcomes. Methods A multicenter cohort study was conducted at 12 Flemish hospitals between June 2013 and September 2015 to examine transitions for patients with heart failure, pneumonia, or total hip/knee arthroplasty. Hospitals with high and low readmission rates were selected based on readmission rates in 2008. The quality of the transitions was assessed based on readiness for discharge, patient education, general practitioner contributions to the discharge process, and timeliness and completeness of discharge summaries. Results A total of 233 patients were included in the study. Readiness for discharge was better in patients with total hip/knee arthroplasty than in those with heart failure or pneumonia (mean differences 11.1 (95% CI 5.3–16.9) ( p = 0.001) and 5.8 (95% CI 1.2–10.5) ( p = 0.016), respectively). Heart failure patients had better readiness scores in low readmission rates than in high readmission rates hospitals (mean difference 13.5 (95% CI 2.5–24.5)) ( p = 0.017). Insufficient timeliness of discharge summaries was a risk factor for postdischarge events (OR 10.564; 95% CI 1.476–75.603; p = 0.019). Discussion To improve the quality of transitions from hospital to home, communication with general practitioner s must occur in a timely manner and with a focus on the continuity of care. Particularly, in patients with complex postdischarge needs, preparing patients for discharge is essential to prevent readmissions.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".