Improving Patient Flow with Lean Methodology: A Case Study at the Montreal General Hospital Colorectal Department
Bibliographic record
Abstract
Quebec healthcare institutions are facing an increase in patients’ request and asked to do more with less, impacting the healthcare staff by working harder and longer shifts. Despite efforts, waiting lists keep growing in number resulting in patients waiting long periods of time for a specific treatment. Lean methodologies, originally developed in the manufacturing industry, offer an alternative to do more with less. Lean focuses efforts on eliminating activities that do not add value from the patient perspective and builds more efficient processes to perform an activity. This thesis proposes the use of Lean methodologies to improve the patient flow throughout the colorectal department at the Montreal General Hospital located in Montreal Quebec. A detail examination of the current processes of the department is analyzed and a proposed system is discussed with the use of value stream mapping and Lean principles. After rigorous data collection and analysis, initial improvements in the capacity of the department will increase in the common flow and colonoscopy loop by 20 patients per week and 60 patients per week respectively. In addition, Lead time will significantly decrease; up to 25% in short procedures, 20% in colonoscopies and 10% in surgeries.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".