Abstract T P259: What Should Really be Happening to Our Stroke Patients Post-Acute Care?: A System Best Practice Model for Inpatient Rehab
Bibliographic record
Abstract
Background: A regional Stroke Report Card identified poor performance on system efficiency, effectiveness, and integration of stroke best practice. This engaged regional funders and 17 organizations (11 acute, 6 rehab) to collaborate in stroke system planning. The focus included stroke unit care and access to timely and appropriate rehabilitation, including increased access for severe stroke. Changes in acute care, including pre-hospital, have facilitated access to stroke unit care in the city. A model of patient flow from acute care was needed to understand other system capacity needs. Purpose: To use best practice and benchmarks to delineate post-acute patient flow and facilitate alignment of resources for inpatient rehabilitation. Methods: Administrative data from national reporting and local rehab referral system databases were used to review current system usage from acute care. A model of proportional distribution of cases from acute, specifically to inpatient rehab, was established using provincial benchmarks, evidence informed targets, and organization market share of total inpatient rehab system capacity. Iterative discussions were required to confirm the organizations’ commitment to stroke best practice. New volume and case mix changes were applied to determine capacity and resource planning needs across organizations. Results: The best practice model, approved by all stakeholders, proposes 40% of stroke patients discharged alive from acute care should access inpatient, 13% outpatient rehabilitation and 6% to Complex Continuing Care and Long Term Care. Current practice is 26%, <5% and 13% respectively. A projected volume increase of 278 patients is distributed across 5/6 rehab providers. This results in a total proportional system shift from 20% (n=160) to 41.5% (n =446) of severe patients receiving access to high intensity rehab. A reduction in the overall proportion of moderate and mild stroke patients from 65% (519) to 49.5% (n=534) and 15% (n=119) to 9% (n=96) respectively. Conclusion: Significant investment/redistribution of resources within the system is required to support patient flow and provide care in the right place at the right time. System funder support is critical to create a quality of care (best practice) system.
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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 | 0.029 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".