Abstract 233: Trends in Referral to Post-Acute Care Services after an Acute Stroke Hospitalization in the U.S.: Findings from the Get With The Guidelines-Stroke Program
Bibliographic record
Abstract
Background: Despite proven benefits of post-acute rehabilitation care, prior studies had suggested overall use of stroke rehabilitation was declining, largely in response to payment reform. We examined more contemporary trends in discharge to stroke inpatient rehabilitation facilities (IRF), skilled nursing facilities (SNF), or home with home health referral (HH) over the last decade of Medicare prospective payment reform. Methods: We examined trends in discharge destinations (IRF, SNF, HH, or home without services) for 849,780 patients age >18 years with acute ischemic or hemorrhagic stroke at 1,687 hospitals participating in the Get With The Guidelines-Stroke (GWTG-Stroke) program. Multivariable analysis was used to identify factors associated with discharge to any post-acute care (IRF, SNF or HH) vs. discharge home without services. Results: Between 2003 and 2011, 43.7% of acute stroke patients were discharged home without services, 25.4% to IRF, 19.5% to SNF, and 11.5% to HH. Discharge to post-acute care increased by 2.1% (p=0.001) for all patients over the period. This included an increase for IRF (by 6.9%, p<0.0001) and HH (by 3.6%, p<0.0001) while discharge to SNF decreased 8.3% (p<0.0001). Discharge disposition and trends varied both by age and stroke severity. In a multivariable-adjusted model we found that older patients (≥65 vs. <65) were more likely to be discharged to post-care care every calendar year with similar ORs, ranging from 2.6 to 2.8. Other factors independently associated with discharge to any post-acute services versus none are shown in Table 1. Conclusions: Since 2003 there was a slight increase in the proportion of stroke patients discharged to post-acute care among hospitals participating in GWTG-Stroke. However, 4 in 10 patients are discharged home without services. Over the last decade of payment reform, there has been modest change in types but not the overall referral to post-acute services for stroke patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".