Development of a checklist of safe discharge practices for hospital patients
Bibliographic record
Abstract
BACKGROUND: Discharge from hospital can be a vulnerable period for patients. Multifaceted "discharge bundles" facilitate care transitions and possibly decrease adverse outcomes. We describe a structured approach to discharge planning, starting from admission and proceeding through discharge, using a standardized checklist of tasks to be performed for each hospitalization day. OBJECTIVE: To create an evidence-based checklist of safe discharge practices for hospital patients. METHODS: In the province of Ontario, the Ministry of Health and Long-Term Care convened a panel of expert members from multiple disciplines and across several healthcare sectors. The panel conducted a systematic search of the literature and used a structured approach to review evidence-based practices that ensure efficient, effective, safe, and patient-centered care transitions. A discharge-checklist tool was created to facilitate safe discharge from hospital. RESULTS: The final checklist describes the processes necessary for a safe and optimal discharge and recommended timeline of when to complete each step, starting from the first day of admission. The checklist domains include (1) indication for hospitalization, (2) primary care, (3) medication safety, (4) follow-up plans, (5) home-care referral, (6) communication with outpatient providers, and (7) patient education. CONCLUSIONS: The Checklist of Safe Discharge Practices for Hospital Patients summarizes the sequence of events that need to be completed throughout a typical hospitalization. Standardizing discharge planning and initiating processes early on in a patient's hospital stay may ensure a safe transition home.
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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 | 0.116 | 0.230 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.015 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".