A systematic review of computer simulation modelling methods in optimizing acute ischemic stroke treatment services

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.