A Case-Control Study of the Sub-Acute Care for Frail Elderly (SAFE) Unit on Hospital Readmission, Emergency Department Visits and Continuity of Post-Discharge Care
Bibliographic record
Abstract
OBJECTIVES: In Canada, alternate-level-of-care (ALC) beds in hospitals may be used when patients who do not require the intensity of services provided in an acute care setting are waiting to be discharged to a more appropriate care setting. However, when there is a lack of care options for patients waiting to be discharged, it contributes to prolonged hospital stays and bottlenecks in the health care system manifested as "hallway medicine." We examined the effectiveness of a function-focused transitional care program, the Sub-Acute care for Frail Elderly (SAFE) Unit, in reducing the length of stay (LOS) in hospital, as well as post-discharge acute care use and continuity of care. DESIGN: Case-control study. SETTING AND PARTICIPANTS: A 450-bed nursing home located in Ontario, Canada, where the SAFE Unit is based. The study population included frail, older patients aged 60 years and older who received care in the SAFE Unit between March 1, 2018, and February 28, 2019 (n = 153) to controls comprising of other hospitalized patients (n = 1773). METHODS: We linked facility-level to provincial health administrative databases on hospital admissions and emergency department (ED) visits, and the Ontario Health Insurance Plan claims database for physician billings to investigated the LOS during the index hospitalization, 30-day odds of post-discharge ED visits, hospital readmission, and follow-up with family physicians. RESULTS: SAFE patients had a median hospital LOS of 13 days [interquartile range (IQR): 8-19 days], with 75% having fewer than 1 day in an ALC bed. In comparison, the median LOS in the control group was 15 days (IQR: 10-24 days), with one-third of those days spent in an ALC bed (median: 5 days, IQR: 3-10 days). SAFE patients were more likely (64.1%) to be discharged home than control patients (46.3%). Both groups experienced similar 30-day odds of ED visits, hospital readmission and follow-up with a family physician. CONCLUSIONS AND IMPLICATIONS: Frail older individuals in the SAFE Unit experienced shorter hospital stays, were less likely to be discharged to settings other than home and had similar 30-day acute care outcomes as control patients post-discharge.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".