Health interventions for the reduction of hospital readmission within 30 days in clinical patients: An integrative review
Bibliographic record
Abstract
Study with the objective of analysing the evidence available in the scientific literature on the interventions used to reduce hospital readmissions within 30 days in clinical patients who were discharged from the hospital to the home. An integrative review was carried out on the online Medical Literature Analysis and Retrieval System and Latin American and Caribbean Literature in Health Sciences databases. Intervention research, published between January 2009 and April 2020, in Portuguese, English and Spanish, was included. The sample consisted of 71 articles. The most frequently performed interventions were telephone contact after discharge (73.2%), health education after discharge (71.8%) and health education during hospitalization (67.6%). Identification of readmission risk (12.9%), home visits after discharge (26.8%) and discharge planning (28.2%) were the least mentioned. The interventions were performed predominantly by a multidisciplinary team (39.5%). There was a significant reduction in readmissions in 50.7% of the studies. It was found that the interventions are aimed at preparing the patient during hospitalization for the return home and post-discharge monitoring to reinforce the care plans and clarify doubts, this important combination of different actions by the multiprofessional team impacts 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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".