Facilitating safe patient transition of care: A qualitative systematic review
Bibliographic record
Abstract
Background: Failure to appropriately plan for a safe and effective transition to the next level of care leads to greater use of hospital and emergency services, often measured by rates of readmission. Despite a focus to develop programs to reduce readmissions, the 30-day all-cause readmission rate for Medicare patients in 2011 remained essentially unchanged. Purpose: The objective of this qualitative systematic review was to synthesize the evidence for interventions aimed at reducing readmissions through a transition of care program. Methods: We searched PubMed and Medline (OVID) with search terms including home care services, continuity of patient care, patient discharge, patient-centered care, health planning, and patient readmission. Selection criteria included quantitative studies, qualitative studies, and expert opinion articles in which a transition of care intervention, was implemented. The outcome of interest was readmission rates. Results: Thirty-three articles met inclusion criteria. The data were synthesized into two categories: primary studies in which the readmission rate was measured as an outcome, and studies that systematically reviewed interventions aimed at improving the discharge process. In all studies reviewed, a transitional care intervention resulted in a statistically significant reduction in readmission rate, or a rate trending lower, or the rate remained the same. Several studies evaluating an intervention occurring during and after hospitalization demonstrated significant results. Conclusion: There is value in reconfiguring discharge processes toward interventions that are more likely to reduce readmissions. The discharge process should incorporate a multidisciplinary, multicomponent transition of care intervention that involves hospital and home-care follow-up.
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 | 0.075 | 0.150 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".