Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/26728
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dc.contributor.authorRen, Yifan-
dc.contributor.authorPhan, Michael-
dc.contributor.authorLuong, Phillip-
dc.contributor.authorWu, Jamin-
dc.contributor.authorShell, Daniel-
dc.contributor.authorBarras, Christen D-
dc.contributor.authorChryssidis, Steve-
dc.contributor.authorKok, Hong Kuan-
dc.contributor.authorBurney, Moe-
dc.contributor.authorTahayori, Baham-
dc.contributor.authorMaingard, Julian-
dc.contributor.authorJhamb, Ashu-
dc.contributor.authorThijs, Vincent N-
dc.contributor.authorBrooks, Duncan Mark-
dc.contributor.authorAsadi, Hamed-
dc.date2021-06-08-
dc.date.accessioned2021-06-14T23:57:11Z-
dc.date.available2021-06-14T23:57:11Z-
dc.date.issued2021-12-
dc.identifier.citationJournal of medical imaging and radiation oncology 2021; 65(7): 850-857en
dc.identifier.urihttps://ahro.austin.org.au/austinjspui/handle/1/26728-
dc.description.abstractThe 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.en
dc.language.isoeng-
dc.subjectGoogleen
dc.subjectacute ischaemic Strokeen
dc.subjectcomputational modelen
dc.subjectdiscrete event simulationen
dc.subjectendovascular treatmenten
dc.titleApplication of a computational model in simulating an endovascular clot retrieval service system within regional Australia.en
dc.typeJournal Articleen
dc.identifier.journaltitleJournal of Medical Imaging and Radiation Oncologyen
dc.identifier.affiliationNeurologyen
dc.identifier.affiliationInterventional Radiology Service - Department of Radiology, Northern Health, Melbourne, Victoria, Australiaen
dc.identifier.affiliationDeloitte, Sydney, New South Wales, Australiaen
dc.identifier.affiliationDepartment of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australiaen
dc.identifier.affiliationInterventional Radiology Service - Department of Radiology, St Vincent's Hospital, Melbourne, Victoria, Australiaen
dc.identifier.affiliationInterventional Neuroradiology Service - Department of Radiology, Monash Health, Melbourne, Victoria, Australiaen
dc.identifier.affiliationSchool of Medicine - Faculty of Health, Deakin University, Geelong, Victoria, Australiaen
dc.identifier.affiliationStroke Theme, The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australiaen
dc.identifier.affiliationDepartment of Medical Imaging, Flinders Medical Centre, Adelaide, South Australia, Australiaen
dc.identifier.affiliationInterventional Neuroradiology Service - Department of Radiology, Austin Hospital, Melbourne, Victoria, Australiaen
dc.identifier.affiliationMonash Health, Melbourne, Victoria, Australiaen
dc.identifier.affiliationSchool of Science, Computer Science and Information Technology, RMIT University, Melbourne, Victoria, Australiaen
dc.identifier.affiliationFaculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australiaen
dc.identifier.affiliationSouth Australian Health and Medical Research Institute, University of Adelaide, Adelaide, South Australia, Australiaen
dc.identifier.affiliationDepartment of Radiology, Royal Adelaide Hospital, Adelaide, South Australia, Australiaen
dc.identifier.doi10.1111/1754-9485.13255en
dc.type.contentTexten
dc.identifier.orcid0000-0001-6518-6828en
dc.identifier.orcid0000-0001-7121-3597en
dc.identifier.orcid0000-0002-3023-1892en
dc.identifier.orcid0000-0003-1899-1909en
dc.identifier.orcid0000-0003-2475-9727en
dc.identifier.pubmedid34105874-
local.name.researcherAsadi, Hamed
item.grantfulltextnone-
item.openairetypeJournal Article-
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.deptNeurology-
crisitem.author.deptThe Florey Institute of Neuroscience and Mental Health-
crisitem.author.deptRadiology-
crisitem.author.deptRadiology-
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