Interventions to Improve Hospital Admission and Discharge Management: An Umbrella Review of Systematic Reviews
Bibliographic record
Abstract
BACKGROUND: The aim of this umbrella review was to summarize the research evidence on programs to improve the transition between ambulatory and hospital care. METHODS: The MEDLINE database and the Cochrane library were searched. Systematic reviews of randomized controlled trials published between January 2000 and September 2018 in English or German were included. Studies were eligible if an assessment or coordination intervention had been evaluated and if patients had been transferred between hospital (defined as internal medicine, surgery, or unspecified hospital setting) and home (defined as any permanent residence). Risk of bias was assessed using the AMSTAR criteria. Results are presented descriptively and in table format. RESULTS: Thirty-nine systematic reviews comprising 492 different studies were included. More than half of these studies were conducted in the United States, the United Kingdom, Canada, and Australia. All studies evaluated strategies to improve discharge management (introduced after patients' arrival at the hospital); no study assessed strategies to improve admission management (initiated in primary care before patients' transition to hospital). The reviews included focused on a specific patient group, a specific intervention type, or a specific outcome. Overall, interventions focusing on elderly patients and high-intensity interventions seemed to be most effective. An overview of classifications of care transition strategies is provided. CONCLUSIONS: Future research should focus on hospital admission management programs.
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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 | 0.025 | 0.088 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.008 |
| Bibliometrics | 0.023 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".