Patient Needs, Required Level of Care, and Reasons Delaying Hospital Discharge for Nonacute Patients Occupying Acute Hospital Beds
Bibliographic record
Abstract
This study aims to determine the proportion of nonacute patients occupying acute care beds and to describe their needs, the appropriate level of alternative care, and reasons preventing discharge. Data from 952 patients hospitalized in an acute care unit for 30 days were obtained from their medical charts and by consulting with the medical team at two tertiary teaching hospitals. Among them, 333 (35%) were determined nonacute on day 30 of hospitalization. According to the Appropriateness Evaluation Protocol (AEP), 55% had no medical, nursing, or patient needs. Among nonacute patients with AEP needs, 88% were related to nursing/life-support services and 12% related to patient condition factors. Regarding alternative level of care, 186 (56%) were waiting for out-of-hospital resources, of which 36% were waiting for palliative care, 33% for long-term care, 18% for rehabilitation, and 12% for home care. For the remaining 147 (44%) nonacute patients, the alternative resources remained undetermined although acute care was no longer required. Main reasons preventing discharge included unavailability of alternative resources, ongoing assessment to determine appropriate resources, ongoing process with community care, and family/patient education/counseling. Available subacute facilities and community-based care would liberate acute care beds and facilitate their appropriate use.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".