Characteristics of short general internal medicine hospital stays: a multicentre cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Short hospital stays may represent opportunities to avert unnecessary admissions or expedite inpatient care. To inform the design of interventions that target patients with potentially avoidable hospital admissions or brief stays, we examined the patient, physician and situational characteristics associated with short stays among patients admitted to general internal medicine wards and describe the use of hospital resources by these patients. METHODS: This was a multicentre cross-sectional study conducted between Apr. 1, 2012, and Mar. 31, 2015, at 5 teaching hospitals in Toronto. We included all general internal medicine admissions through the emergency department. We examined patient, physician and situational predictors of a short hospital stay, which was defined as the patient's being discharged home alive in 2 possible time windows: less than 24 hours, or 72 hours or less. RESULTS: The final study sample included 56 055 admissions and 37 700 unique patients. Patients discharged in less than 24 hours and in 72 hours or less accounted for 4245 (7.6%) and 13 442 (31.6%) admissions, respectively. After we controlled for patient factors, patients of female physicians were less likely than those of male physicians to have stays lasting less than 24 hours (adjusted odds ratio [OR] 0.80, 95% confidence interval [CI] 0.74-0.86) or 72 hours or less (adjusted OR 0.82, 95% CI 0.79-0.86). Patients admitted at night or on a weekday were significantly more likely than those admitted at other times to have stays lasting less than 24 hours (night: adjusted OR 2.73, 95% CI 2.44-3.06; weekday: adjusted OR 1.26, 95% CI 1.17-1.36) or 72 hours or less (night: adjusted OR 1.29, 95% CI 1.22-1.37, weekday: adjusted OR 1.05, 95% CI 1.01-1.10). Among stays lasting less than 24 hours and 24-72 hours, intravenously administered medications were ordered for 2788 (65.7%) and 10 722 (79.8%) patients, respectively, and computed tomography scans were performed for 1561 (36.8%) and 5354 (39.1%) patients, respectively. INTERPRETATION: Short general internal medicine hospital stays were common and were associated with patient, physician and situational factors. Interventions to avert hospital admission or reduce length of stay may be more effective if they are accessible outside typical working hours and provide access to intravenous therapy and radiological investigations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".