Lean transformation framework for treatment-oriented outpatient departments
Bibliographic record
Abstract
Long wait times and low resource utilisation are the most critical issues in treatment-oriented outpatient departments. While Lean has been utilised to resolve similar issues in healthcare, the literature provides no structured means of implementing Lean in outpatient departments. This study establishes a Lean transformation framework to identify a balanced patient demand by determining proper patient compositions, to schedule non-specialists to increase resource utilisation, and to level patient schedule throughout the day to reduce wait times. This framework includes a series of activities and Lean tools that are specifically adapted for use in outpatient departments. A case study is presented to illustrate the implementation of the proposed framework and its possible impacts, using data from a community hospital oncology department in Montreal, Canada. Results suggests that this framework reduces patient visit time by 36% and increases daily treatments by 39% and utilisation of chemotherapy chairs by 22%, with a possibility to implement a one-day treatment regime. The proposed framework can assist treatment-oriented outpatient departments to overcome their operational challenges and to serve as an effective guideline in their Lean transformation.
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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 | 0.003 | 0.002 |
| 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 | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".