Discrete event simulation model of an acute stroke treatment process at a comprehensive stroke center: Determining the ideal improvement strategies for reducing treatment times
Bibliographic record
Abstract
BACKGROUND: Fast treatment is crucial for ischemic stroke patients; the probability of good patient outcomes increases with faster treatment. Treatment times can be improved by making changes to the treatment process. However, it is challenging to identify the benefits of changes prior to implementation. Simulation modelling, which mimics the treatment process, can be used to evaluate changes without patient involvement. This study models the acute stroke treatment process using discrete event simulation (DES) and identifies improvement strategies to reduce treatment times. METHOD: The model was developed for a comprehensive stroke center in Nova Scotia, using Python. All treatment pathways and sub-tasks were identified via an observational time and motion study conducted in the center. Nine process change scenarios were tested individually and in combinations. The primary outcome measures were door-to-CT time (DTCT), door-to-needle time (DNT), and door-to-groin puncture time (DGPT). The model simulated 500 patients 30 times. RESULTS: Collecting patient history on the way to the radiology department (rather than in ED) showed the highest reduction among individual scenarios for DTCT (14.2 vs 12.4 min, p < 0.001). Combining all scenarios in the door-to-CT process resulted in a reduction of the DTCT by approximately 28 %. Thrombolysing patients in the imaging department's waiting area resulted in the lowest DNT (39.4 vs 34.8 min, p < 0.001) among all individual scenarios. The highest reduction in DGPT, among all individual scenarios, was achieved by implementing Rapid angiosuite preparation (67.7 vs 51.4 min, p < 0.001). The combinations of all scenarios resulted in the lowest DTCT (14.2 vs 10.1 min, p < 0.001), DNT (39.4 vs 23.0 min, p < 0.001), and DGPT (67.9 vs 38.5 min, p < 0.001). CONCLUSIONS: The study identified various improvement strategies in the acute stroke treatment process through a discrete-event simulation model. Combining all scenarios resulted in significant reductions for all outcome measures.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".