Evaluating post‐earthquake functionality and surge capacity of hospital emergency departments using discrete event simulation
Bibliographic record
Abstract
Past earthquakes have illustrated the impacts of reduced hospital functionality due to physical damage resulting in a health service deficit immediately after a major seismic event. In this article, a methodology was developed to quantify the deficit in health care anticipated due to a loss of functionality of a hospital emergency department (ED) and a surge in demand due to regional damage in an earthquake scenario. Earthquake‐induced patient arrivals were calculated using multi‐severity casualty estimation for the catchment area of the hospital. The surge in patients (demand) was then compared to the ability of the hospital to treat patients (capacity) based on anticipated functionality. Nonlinear response history analysis of the hospital building was performed using simplified structural models, and the structural and non‐structural component damage was estimated based on FEMA P‐58. Expected damage was linked to the post‐earthquake functionality of the ED service areas on each floor by incorporating the fault‐tree analysis method. Finally, discrete event simulation was used to evaluate the ED surge capacity, providing hospital performance metrics, such as wait times (WTs) and length of stay (LOS) for patients of ranging acuity. A case study of a hospital in the City of Vancouver subjected to an M w 9.0 Cascadia Subduction Zone scenario earthquake was presented. Emergency rooms (ERs) were identified as the ED bottleneck during the emergency response. The mean ER WT exceeded its limit of 2 h and reached up to 17 h in the most unfavorable simulation. Likewise, the mean LOS nearly doubled from 6.5 to 12 h, also exceeding the established target of 10 h. The deployment of field hospitals for less severe patients as an emergency plan to mitigate the ED overcrowding was also analyzed to demonstrate that the methodology can be used as a decision support tool to improve healthcare disaster planning.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".