Improvement of operation efficiency in Winnipeg Regional Health Authority (WRHA) hemodialysis (HD) units using discrete-event simulation modeling
Bibliographic record
Abstract
Facility based Hemodialysis (HD) treats over 75% of Canadian patients with kidney failure. HD in Winnipeg costs approximately $60,000 per patient annually and predictions indicate this population will double within the next 10 years. This research investigates workflow bottlenecks for improvement in HD units of Winnipeg Health Sciences Centre. A validated discrete-event simulation (DES) model is built to evaluate different strategies in HD care processes for improvement based on proposed alternatives for renal program workflows. Simulation modeling is used to study the behaviour change of entities and resources in HD systems. Proposed alternative scenarios in the simulation model examine key performance indicators (KPIs) including wait-time to start dialysis machines, length of stay, and percentage of times that dialysis machines start within the target time. The application of proposed improvement strategies results in desirable outcomes on KPIs up-to 31%. The solution can be applied to support future HD units’ improvements.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".