Utilizing Lean Methodology to Streamline the Alternate Level of Care Discharge Planning Process Within the Integrated Stroke Program of a Regional Stroke Centre
Bibliographic record
Abstract
Background: Stroke is the leading cause of adult disability in Canada. 11% of patients are left with severe disability that prevents them from returning to their previous living environment and requiring transfer to a long term care (LTC) or complex continuing care (CC). Delays in admission to LTC or CC results in patients who require an alternate level of care (ALC) occupying beds on the acute stroke unit. In FY 2016 u2013 2017, the Regional Stroke Centre acute stroke unit had a proportion of ALC days to acute days of 29.7% and 8.5 ALC patient bed equivalents. There was no standardized discharge planning process for ALC patients.Purpose: To streamline the ALC processes to achieve a 10% reduction from baseline by September 2017 in the: 1) proportion of ALC days to acute days; 2) number of ALC days and 3) number of ALC patient beds days. Methods: A process map of the patient journey from admission to discharge for the three commonly used ALC pathways was completed. Pre/post staff knowledge of and satisfaction with the ALC processes were undertaken. Pre/post patient and family feedback was sought via experience based design.Results: Standardized processes, communication tools and consistent management were implemented. This resulted in a decrease of the: 1) proportion of ALC days from 29.7% to 25.1%;2) number of ALC days from 2825 to 1952 and 3) ALC bed equivalents 8.5 to 6.1. Conclusions: Utilizing lean methodology led to standardization of the ALC discharge planning process that significantly reduced the ALC rates on the unit.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".