Delayed discharge after major surgical procedures in Ontario, Canada: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Addressing nonmedical reasons for delays in hospital discharge is important for improving the flow of patients through acute care hospital beds. Because this problem is understudied among adult surgical patients, we examined the incidence of and identified factors associated with delayed hospital discharge after major elective and emergency surgical procedures in acute care institutions. METHODS: Using health administrative data, we retrospectively compared adults with and without delayed discharge after 18 major elective and emergency surgical procedures between 2006 and 2016 in Ontario hospitals. We identified delayed discharge using the alternate level of care code, applied to patients who are medically fit for discharge but remain in an acute care hospital bed. We used hierarchical logistic regression modelling to determine factors associated with delayed discharge. RESULTS: Our cohort included 595 782 patients who underwent elective procedures and 180 478 who underwent emergency procedures. Delayed discharge accounted for 635 607 hospital days, of which 81.7% were related to admissions for emergency surgery. Delayed discharge affected 3.1% of patients who underwent elective surgery and 19.6% of those who underwent emergency procedures. Days attributed to delayed discharge formed about one-third of patients' total hospital stay for both surgical groups. The rate of delayed discharge across surgical specialties showed high variability (from 0.9% for lung resection or nephrectomy to 9.3% for peripheral arterial disease procedures in the elective surgery group, and from 3.8% for cardiac procedures to 33.8% for peripheral arterial disease procedures in the emergency surgery group). Risk factors for delayed discharge were older age, female sex, chronic disease burden and increasing hospital size. INTERPRETATION: Delayed discharge for nonmedical reasons was more common after emergency surgery than after elective surgery, and rates varied across surgery type. Optimizing early discharge planning, evaluating the variation in delayed discharge at the hospital level and improving local access to community care services could be next steps to addressing this problem.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".