Simulation of a Hospital Disaster Plan: A Virtual, Live Exercise
Bibliographic record
Abstract
INTRODUCTION: Currently, there is no widely available method to evaluate an emergency department disaster plan. Creation of a standardized patient database and the use of a virtual, live exercise may lead to a standardized and reproducible method that can be used to evaluate a disaster plan. PURPOSE: A virtual, live exercise was designed with the primary objective of evaluating a hospital's emergency department disaster plan. Education and training of participants was a secondary goal. METHODS: A database (disastermed.ca) of histories, physical examination findings, and laboratory results for 136 simulated patients was created using information derived from actual patient encounters. The patient database was used to perform a virtual, live exercise using a training version of the emergency department's information system software. RESULTS: Several solutions to increase patient flow were demonstrated during the exercise. Conducting the exercise helped identify several faults in the hospital disaster plan, including outlining the important rate-limiting step. In addition, a significant degree of under-triage was demonstrated. Estimates of multiple markers of patient flow were identified and compared to Canadian guidelines. Most participants reported that the exercise was a valuable learning experience. CONCLUSIONS: A virtual, live exercise using the disastermed.ca patient database was an inexpensive method to evaluate the emergency department disaster plan. This included discovery of new approaches to managing patients, delineating the rate-limiting steps, and evaluating triage accuracy. Use of the patient timestamps has potential as a standardized international benchmark of hospital disaster plan efficacy. Participant satisfaction was high.
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.
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 | 0.002 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".