Utilizing Lean Methodology to Redesign Stroke Rehabilitation Team Rounds to improve Efficiencies.
Bibliographic record
Abstract
Utilizing Lean Methodology to Redesign Stroke Rehabilitation Team Rounds to improve Efficiencies. Background: The need for a highly-coordinated, specialized team, who meet regularly to discuss the rehabilitation goals and progress is a part of Stroke Best Practice Guidelines (Canadian Stroke Best Practices: delivery of inpatient stroke rehabilitation. 2016). Quantitative measures such as Rehabilitation Intensity (RI), FIM efficiency and proportion of patients meeting the RPG length of stay provide quantitative measures of patient centered care. Staff and Patient satisfaction surveys are a method of measuring the qualitative improvements of our day to day work. The rehabilitation unit at the Hamilton Health Sciences (HHS) did not have a consistent process for RPG (Rehabilitation Patient Groupings) availability and team rounds were lasting for over 2 hours which impacted direct patient care.Methods: The team participated in a process mapping exercise, which identified opportunities related to the rounding process. Staff satisfaction surveys and experience based patient questionnaires were conducted. Staff and Patient surveys demonstrated opportunities around pass processes and training. Three sub working groups were formed to address quality improvement opportunities: RPG Processes, Rounds Processes and Pass Processes. Results: Preliminary data shows a change in FIM efficiency from 0.96 to 0.98 as well as an increase in the RPGu2019s available to the team in rounds (54% in 2017/18 Q2 to 80% in 2017/18 Q3). Further measurement to capture the length of time of rounds, RI improvements and the proportion of patients meeting their RPG targets are pending. Conclusion: Improving the efficiency of team rounds processes improves the quantitative and qualitative metrics in an inpatient rehabilitation program.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.058 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.021 | 0.023 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.025 |
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; both teacher heads agree on what is shown here.
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".