Improving discharge planning communication between hospitals and patients
Bibliographic record
Abstract
BACKGROUND: A potential barrier to patient discharge from hospital is communication problems between the treating team and the patient or family regarding discharge planning. AIM: To determine if a bedside 'Leaving Hospital Information Sheet' increases patient and family's knowledge of discharge date and destination and the name of the key clinician primarily responsible for team-patient communication. METHODS: This article is a 'before-after' study of patients, their families and the interdisciplinary ward-based clinical team. Outcomes assessed pre-implementation and post-implementation of a bedside 'Leaving Hospital Information Sheet' containing discharge information for patients and families. Patients and families were asked if they knew the key clinician for team-patient communication and the proposed discharge date and discharge destination. Responses were compared with those set by the team. Staff were surveyed regarding their perceptions of patient awareness of discharge plans and the benefit of the 'Leaving Hospital Information Sheet'. RESULTS: Significant improvement occurred regarding patients' knowledge of their key clinician for team-patient communication (31% vs 75%; P = 0.0001), correctly identifying who they were (47% vs 79%; P = 0.02), and correctly reporting their anticipated discharge date (54% vs 86%; P = 0.004). There was significant improvement in the family's knowledge of the anticipated discharge date (78% vs 96%; P = 0.04). Staff reported the 'Leaving Hospital Information Sheet' assisted with communication regarding anticipated discharge date and destination (very helpful n = 11, 39%; a little bit helpful n = 11, 39%). CONCLUSIONS: A bedside 'Leaving Hospital Information Sheet' can potentially improve communication between patients, families and their treating team.
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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 | 0.007 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".