Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/26728
Title: Application of a computational model in simulating an endovascular clot retrieval service system within regional Australia.
Austin Authors: Ren, Yifan;Phan, Michael;Luong, Phillip;Wu, Jamin;Shell, Daniel;Barras, Christen D;Chryssidis, Steve;Kok, Hong Kuan;Burney, Moe;Tahayori, Baham;Maingard, Julian;Jhamb, Ashu;Thijs, Vincent N ;Brooks, Duncan Mark ;Asadi, Hamed 
Affiliation: Neurology
Interventional Radiology Service - Department of Radiology, Northern Health, Melbourne, Victoria, Australia
Deloitte, Sydney, New South Wales, Australia
Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia
Interventional Radiology Service - Department of Radiology, St Vincent's Hospital, Melbourne, Victoria, Australia
Interventional Neuroradiology Service - Department of Radiology, Monash Health, Melbourne, Victoria, Australia
School of Medicine - Faculty of Health, Deakin University, Geelong, Victoria, Australia
Stroke Theme, The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia
Department of Medical Imaging, Flinders Medical Centre, Adelaide, South Australia, Australia
Interventional Neuroradiology Service - Department of Radiology, Austin Hospital, Melbourne, Victoria, Australia
Monash Health, Melbourne, Victoria, Australia
School of Science, Computer Science and Information Technology, RMIT University, Melbourne, Victoria, Australia
Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia
South Australian Health and Medical Research Institute, University of Adelaide, Adelaide, South Australia, Australia
Department of Radiology, Royal Adelaide Hospital, Adelaide, South Australia, Australia
Issue Date: Dec-2021
Date: 2021-06-08
Publication information: Journal of medical imaging and radiation oncology 2021; 65(7): 850-857
Abstract: The global demand for endovascular clot retrieval (ECR) has grown rapidly in recent years creating challenges to healthcare system planning and resource allocation. This study aims to apply our established computational model to predict and optimise the performance and resource allocation of ECR services within regional Australia, and applying data from the state of South Australia as a modelling exercise. Local geographic information obtained using the Google Maps application program interface and real-world data was input into the discrete event simulation model we previously developed. The results were obtained after the simulation was run over 5 years. We modelled and compared a single-centre and two-centre ECR service delivery system. Based on the input data, this model was able to simulate the ECR delivery system in the state of South Australia from the moment when emergency services were notified of a potential stroke patient to potential delivery of ECR treatment. In the model, ECR delivery improved using a two-centre system compared to a one-centre system, as the percentage of stroke patients requiring ECR was increased. When 15% of patients required ECR, the proportion of 'failure to receive ECR' cases for a single-centre system was 17.35%, compared to 3.71% for a two-centre system. Geolocation and resource utilisation within the ECR delivery system are crucial in optimising service delivery and patient outcome. Under the model assumptions, as the number of stroke cases requiring ECR increased, a two-centre ECR system resulted in increased timely ECR delivery, compared to a single-centre system. This study demonstrated the flexibility and the potential application of our DES model in simulating the stroke service within any location worldwide.
URI: https://ahro.austin.org.au/austinjspui/handle/1/26728
DOI: 10.1111/1754-9485.13255
ORCID: 0000-0001-6518-6828
0000-0001-7121-3597
0000-0002-3023-1892
0000-0003-1899-1909
0000-0003-2475-9727
Journal: Journal of Medical Imaging and Radiation Oncology
PubMed URL: 34105874
Type: Journal Article
Subjects: Google
acute ischaemic Stroke
computational model
discrete event simulation
endovascular treatment
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