Dynamics of Patient Flow Under Low Acuity Rerouting: An Agent-Based Prototype for a Digital Twin of an Emergency Department in Canada
Bibliographic record
Abstract
The majority of emergency departments EDs face crowding challenges, particularly caused by low-acuity patients who could often be treated in community care. This study develops an agent-based digital twin prototype of the ED at Rouyn-Noranda hospital in Canada, to examine different management and information-sharing strategies. We simulated patient flow, staff dynamics, and the influence of waiting information transparency through social media. Two management scenarios were tested: (1) “super nurses” managing Canadian Triage and Acuity Scale (CTAS) 4-5 patients inside the ED, (2) redirecting these patients to general practitioners (GPs). Results showed that super nurses reduced waiting times, length of stay (LOS), while GP redirection lowered occupancy but increased waits and LOS due to high-acuity patients' domination in the queue. We also examined the impact of posting waiting information on Facebook. Hourly updates reduced evening overloads, whereas frequent posting caused rebound effects.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".