A Scoping Review of Patient Discharge From Intensive Care
Bibliographic record
Abstract
BACKGROUND: We conducted a scoping review to systematically review the literature reporting patient discharge from ICUs, identify facilitators and barriers to high-quality care, and describe tools developed to improve care. METHODS: We searched Medline, Embase, CINAHL, and the Cochrane Central Register of Controlled Trials. Data were extracted on the article type, study details for research articles, patient population, phase of care during discharge, and dimensions of health-care quality. RESULTS: From 8,154 unique publications we included 224 articles. Of these, 131 articles (58%) were original research, predominantly case series (23%) and cohort (16%) studies; 12% were narrative reviews; and 11% were guidelines/policies. Common themes included patient and family needs/experiences (29% of articles) and the importance of complete and accurate information (26%). Facilitators of high-quality care included provider-patient communication (30%), provider-provider communication (25%), and the use of guidelines/policies (29%). Patient and family anxiety (21%) and limited availability of ICU and ward resources (26%) were reported barriers to high-quality care. A total of 47 tools to facilitate patient discharge from the ICU were identified and focused on patient evaluation for discharge (29%), discharge planning and teaching (47%), and optimized discharge summaries (23%). CONCLUSIONS: Common themes, facilitators and barriers related to patient and family needs/experiences, communication, and the use of guidelines/policies to standardize patient discharge from ICU transcend the literature. Candidate tools to improve care are available; comparative evaluation is needed prior to broad implementation and could be tested through local quality-improvement 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 | 0.033 | 0.127 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.038 | 0.031 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 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".