Clinical predictors of protracted length of stay in Ontario Complex Continuing Care hospitals
Bibliographic record
Abstract
BACKGROUND: Post-acute care hospitals are often subject to patient flow pressures because of their intermediary position along the continuum of care between acute care hospitals and community care or residential long-term care settings. The purpose of this study was to identify patient attributes associated with a prolonged length of stay in Complex Continuing Care hospitals. METHODS: Using information collected using the interRAI Resident Assessment Instrument Minimum Data Set 2.0 (MDS 2.0), a sample of 91,113 episodes of care for patients admitted to Complex Continuing Care hospitals between March 31, 2001 and March 31, 2013 was established. All patients in the sample were either discharged to a residential long-term care facility (e.g., nursing home) or to the community. Long-stay patients for each discharge destination were identified based on a length of stay in the 95th percentile. A series of multivariate logistic regression models predicting long-stay patient status for each discharge destination pathway were fit to characterize the association between demographic factors, residential history, health severity measures, and service utilization on prolonged length of stay in post-acute care. RESULTS: Risk factors for prolonged length of stay in the adjusted models included functional and cognitive impairment, greater pressure ulcer risk, paralysis, antibiotic resistant and HIV infection need for a feeding tube, dialysis, tracheostomy, ventilator or a respirator, and psychological therapy. Protective factors included advanced age, medical instability, a greater number of recent hospital and emergency department visits, cancer diagnosis, pneumonia, unsteady gait, a desire to return to the community, and a support person who is positive towards discharge. Aggressive behaviour was only a risk factor for patients discharged to residential long-term care facilities. Cancer diagnosis, antibiotic resistant and HIV infection, and pneumonia were only significant factors for patients discharged to the community. CONCLUSIONS: This study identified several patient attributes and process of care variables that are predictors of prolonged length of stay in post-acute care hospitals. This is valuable information for care planners and health system administrators working to improve patient flow in Complex Continuing Care and other post-acute care settings such as skilled nursing and inpatient rehabilitation facilities.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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, 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".