Accuracy of Computer Simulation to Predict Patient Flow during Mass-Casualty Incidents
Bibliographic record
Abstract
INTRODUCTION: Although most hospitals have an emergency department disaster plan, most never have been implemented in a true disaster or been tested objectively. Computer simulation may be a useful tool to predict emergency department patient flow during a disaster. PURPOSE: The aim of this study was to compare the accuracy of a computer simulation in predicting emergency department patient flow during a mass-casualty incident with that of a real-time, virtual, live exercise. METHODS: History, physical examination findings, and laboratory results for 136 simulated patients were extracted from the disastermed.ca patient database as used as input into a computer simulation designed to represent the emergency department at the University of Alberta Hospital. The computer simulation was developed using a commercially available simulation software platform (2005, SimProcess, CACI Products, San Diego CA). Patient flow parameters were compared to a previous virtual, live exercise using the same data set. RESULTS: Although results between the computer simulation and the live exercise appear similar, they differ statistically with respect to many patient benchmarks. There was a marked difference between the triage codes assigned during the live exercise and those from the patient database; however, this alone did not account for the differences between the patient groups. It is likely that novel approaches to patient care developed by the live exercise group, which are difficult to model by computer software, contributed to differences between the groups. Computer simulation was useful, however, in predicting how small changes to emergency department structure, such as adding staff or patient care areas, can influence patient flow. CONCLUSIONS: Computer simulation is helpful in defining the effects of changes to a hospital disaster plan. However, it cannot fully replace participant exercises. Rather, computer simulation and live exercises are complementary, and both may be useful for disaster plan evaluation.
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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 | 0.000 | 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) | 0.000 | 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".