The implementation of a precision case management model in a Canadian inpatient rehabilitation center: The 12-months post-implementation findings of a quality improvement project
Bibliographic record
Abstract
Despite recommendations, few have reported on quality improvement initiatives to implement length of rehabilitation stay benchmarks, while actively monitoring functional outcomes. This article describes the development, implementation, and evaluation of a precision case management model across all inpatient rehabilitation client groups in a Canadian facility. To develop the length of rehabilitation-stay (LoRS) benchmarks, patient data was retrospectively analyzed. A severity specific method was used to stratify median length of stay. A target reduction on 8.6 days in LoRS was established. Functional discharge targets were also set and monitored at specific intervals via the Functional Independence Measure (FIM®). The implementation used an incremental quality improvement phased approach. Following 12-months, a statistically significant reduction in mean LoRS of 13.2 days was achieved, along with a small increase in FIM® change across all rehabilitation client groups. A similar pattern was seen across the three main client groups, where a LoRS reduction greater than the target was achieved, along with important improvements in LoRS efficiency. This study demonstrates how the implementation of a precision case management model can assist a facility in markedly reducing LoRS across inpatient groups, without compromising functional change or community discharge rates and begin its transformation to a value-based organization.
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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 | 0.020 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".