Length of stay benchmarks for inpatient rehabilitation after stroke
Bibliographic record
Abstract
Purpose: In Canada, no standardized benchmarks for length of stay (LOS) have been established for post-stroke inpatient rehabilitation. This paper describes the development of a severity specific median length of stay benchmarking strategy, assessment of its impact after one year of implementation in a Canadian rehabilitation hospital, and establishment of updated benchmarks that may be useful for comparison with other facilities across Canada. Method: Patient data were retrospectively assessed for all patients admitted to a single post-acute stroke rehabilitation unit in Ontario, Canada between April 2005 and March 2008. Rehabilitation Patient Groups (RPGs) were used to establish stratified median length of stay benchmarks for each group that were incorporated into team rounds beginning in October 2009. Benchmark impact was assessed using mean LOS, FIM® gain, and discharge destination for each RPG group, collected prospectively for one year, compared against similar information from the previous calendar year. Benchmarks were then adjusted accordingly for future use. Results: Between October 2009 and September 2010, a significant reduction in average LOS was noted compared to the previous year (35.3 vs. 41.2 days; p < 0.05). Reductions in LOS were noted in each RPG group including statistically significant reductions in 4 of the 7 groups. As intended, reductions in LOS were achieved with no significant reduction in mean FIM® gain or proportion of patients discharged home compared to the previous year. Adjusted benchmarks for LOS ranged from 13 to 48 days depending on the RPG group. Conclusions: After a single year of implementation, severity specific benchmarks helped the rehabilitation team reduce LOS while maintaining the same levels of functional gain and achieving the same rate of discharge to the community.Implications for RehabilitationEfficient post-stroke rehabilitation can help to improve patient outcomes and reduce the financial burden placed on the healthcare system.Yet, unnecessarily long lengths of stay in rehabilitation are not in the best interest of the patient and act to increase the cost of care.This study illustrates how a length of stay benchmarking system can help to promote efficiency in post-stroke rehabilitation and reduce the cost of care without negatively impacting patient recovery.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 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".