Stakeholder Views Regarding Patient Discharge from Intensive Care: Suboptimal Quality and Opportunities for Improvement
Bibliographic record
Abstract
OBJECTIVE: To provide the first description of intensive care unit (ICU) discharge practices from the perspective of Canadian ICU administrators, and ICU providers from Canada, the United States and the United Kingdom. METHODS: The authors identified 140 Canadian ICUs and administered a survey to ICU administrators (unit manager, director) to obtain an institutional perspective. Also surveyed were members of professional critical care associations in Canada, the United States and the United Kingdom, using membership distribution lists, to obtain a provider perspective. RESULTS: A total of 118 ICU administrators (114 ICUs [81%]) and 737 ICU providers (denominator unknown) responded to the survey. Administrator and provider respondents reported that ICU physicians are primarily responsible for determining the timing (70% and 77%, respectively) and safety (94% and 96%) for patients discharged from ICU. The majority of respondents indicated that patient summaries (87% and 85%) and medication reconciliation (78% and 79%) were part of their institutions' discharge process. One-half of respondents reported the use of discharge protocols, while a minority indicated that checklists (46% and 44%), electronic tools (19% and 28%) or outreach follow-up (44% and 33%) were used. The majority of respondents rated current ICU discharge practices to be of medium quality (57% and 58% scored 3 on a five-point scale). Suggested opportunities for improvement included the information provided to patients and families (71% and 59%) and collaboration among hospital units (65% and 66%). CONCLUSION: Findings from the present study revealed the complexity of the ICU discharge process, considerable practice variation, perception of only medium quality and several proposed opportunities for improvement.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".