Peer Review #2 of "Lean thinking by integrating with discrete event simulation and design of experiments: an emergency department expansion (v0.2)"
Bibliographic record
Abstract
Background.Many management tools, such as Discrete Event Simulation (DES) and Lean Healthcare, are efficient to support and assist health care quality.In this sense, the study aims at using Lean Thinking (LT) principles combined with DES to plan a Canadian emergency department (ED) expansion and at meeting the demand that comes from small care centers closed.The project`s purpose is reducing the patients' Length of Stay (LOS) in the ED.Additionally, they must be assisted as soon as possible after the triage process.Furthermore, the study aims at determining the ideal number of beds in the Short Stay Unit (SSU).The patients must not wait more than 180 minutes to be transferred. Methods.For this purpose, the hospital decision-makers have suggested planning the expansion, and it was carried out by the simulation and modeling method.The emergency department was simulated by the software FlexSim Healthcare®, and, with the Design of Experiments (DoE), the optimal number of beds, seats, and resources for each shift was determined.Data collection and modeling were executed based on historical data (patients' arrival) and from some databases that are in use by the hospital, from April 1 st , 2017 to March 31 st , 2018.The experiments were carried out by running 30 replicates for each scenario.Results.The results show that the emergency department cannot meet expected demand in the current state.Only 17.2% of the patients were completed treated, and LOS was 2213.7 (average), with a confidence interval of (2131.8-2295.6)minutes.However, after changing decision variables and applying LT techniques, the treated patients' number increased to 95.7% (approximately 600%).Average LOS decreased to 461.2, with a confidence interval of (453.7-468.7)minutes, about 79.0%.The time to be attended after the triage decrease from 404.3 minutes to 20.8 (19.8-21.8)minutes, around 95.0%, while the time to be transferred from bed to the SSU decreased by 60.0%.Moreover, the ED reduced human resources downtime, according to Lean Thinking principles.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".