A systematic review of computer simulation modelling methods in optimizing acute ischemic stroke treatment services
Bibliographic record
Abstract
Background: The rapid and resource-intensive nature of acute ischemic stroke (AIS) treatment demands ongoing optimization. Simulation modeling offers an effective approach for investigating these complex systems by simulating care processes in a virtual environment, enabling outcomes evaluation without direct patient engagement. Objective: This systematic review identified and analyzed studies utilizing simulation modeling to optimize AIS treatment. We assessed the models and identified areas for improvement to inform future development of AIS care services simulation models. Methodology: The review spanned from 2012 to 2022 incorporating searches on PubMed, Medline, Google Scholar, and conducting a backward citation search. Data extraction included hospital information, care settings, data sources, inputs, outputs, simulation type, model type, and simulation features. Results: The review included 27 studies with 81.5% utilizing discrete-event simulation models. The primary focus was on service design (77.8%), optimizing care components across various stages: acute, stroke unit, rehabilitation, and post-acute. Cost analysis models (14.8%) showed benefits of strategies like expanding comprehensive stroke centers, centralizing thrombolysis facilities, and assessing thrombolysis use. Capacity planning models (7.4%) demonstrated advantages in bed increases and resource pooling between acute and rehabilitation settings. Although most studies transparently reported essential model elements, almost half did not report stakeholder and expert engagement. Nonetheless, the majority provided validation and verification details, aiding real-world implementation. Conclusions: 27 studies have used simulation modeling to optimize the AIS treatment workflow; however future studies should consider stringent reporting of model elements, enhanced stakeholder and expert engagement, reusable model development, and effective integration of model findings into real-world healthcare systems.
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 | 0.024 | 0.099 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.013 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".