Using Discrete Event Simulation to Improve Performance At Two Canadian Emergency Departments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Emergency Departments' (EDs) critical role in patient care and their complex process flow contribute to them being one of the most frequently modelled systems in healthcare Operations Research (OR). The goal of this research was to develop models of two EDs that could diagnose bottlenecks and evaluate performance improvement approaches using a generalized approach. We used Discrete Event Simulation (DES) to model two EDs in Toronto, Canada, based on existing processes and empirical data. Model outputs include wait times, treatment times, and selected process durations. Management of both EDs used the models to evaluate performance and preview the effects of staffing and flow changes before committing to the improvement measures. The examples of successful performance improvement opportunities include a new triage flow for patients arriving by ambulance, merging of the treatment zones, and increases in staffing levels.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it