The implementation of a comprehensive discharge bundle to improve the discharge process: a quasi-experimental study.
Bibliographic record
Abstract
BACKGROUND: Hospitalised patients are especially vulnerable in times of transitions in care. Structured discharge planning might improve patient outcomes. We implemented and assessed the effect of a multidisciplinary discharge bundle to reduce 30-day readmission. METHODS: A pre-post-test design study with a follow-up of one month at four internal medicine wards in a Dutch university teaching hospital. Eligible patients were 18 years and older, acutely admitted and hospitalised for at least 48 hours. The discharge bundle consisted of (1) planning the date of discharge within 48 hours after admission, (2) a discharge checklist, (3) a personalised patient discharge letter, and (4) multidisciplinary patient education. The primary outcome measure was unplanned 30-day readmission. RESULTS: Participants in the post-test group (n = 204) did not have a lower rate of unplanned hospital readmission than those receiving usual care (n = 224) (12.9 vs. 13.2%, p = 0.93). The medical discharge summaries were sent to the general practitioner faster in the post-test period (median of 14 days pre-test vs. 5 days post-test, p < 0.001) and this group also had a trend towards a longer time to first readmission (14 vs. 10 days, p = 0.06). Patient satisfaction was high in both groups (7.5 and 7.4 points, (p = 0.49)). CONCLUSIONS: The comprehensive discharge bundle was not effective in reducing the rate of readmission and increasing patient satisfaction, but medical discharge summaries were sent faster to the general practitioner and a trend to a longer time to readmission was present.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".