Utilizing a Quality Improvement Approach to Improve Access for Stroke Patients to Outpatient Neuro Rehab Services at Trillium Health Partners--author request (from registration)
Bibliographic record
Abstract
BackgroundCanadian Stroke Best Practice Recommendations state that stroke survivors referred to community-based rehabilitation should begin their program between 48-72 hours of discharge. A recent study examining wait times for community rehabilitation indicated that individuals with chronic conditions have excessive wait times for outpatient and community OT and PT services in Ontario, particularly if those individuals are waiting for hospital-based outpatient services. Preliminary data from Trillium Health Partners Outpatient Neuro Rehab Services revealed stroke patients were waiting an average 58 days from referral to admission to the program. A Quality Improvement approach to waitlist management was used in an effort to improve access for stroke patients to outpatient therapy by targeting length of stay, process for waitlist management, education of patients prior to admission to service, and education of referring partners on intake criteria. Methods This work is part of a Quality Improvement project with the Improving and Driving Excellence Across Sectors (IDEAS) Advanced Learning Program. Several improvement cycles were initiated in an attempt to improve access to the service.ResultsPreliminary results indicate improving patientu2019s knowledge of the outpatient program, preparing them to participate in the program, refining the inpatient referral process and changing referral criteria decreased time spent on the waitlist. Our poster will include outcome data.Conclusions There is limited information in the Canadian context of waitlist management for stroke care in the community (outpatient) setting. This Quality Improvement approach identifies several strategies that may improve stroke patientsu2019 access to outpatient therapy.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".