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Front Neurol. 2019 Jun 27;10:653. doi: 10.3389/fneur.2019.00653. eCollection 2019.

Optimizing Resources for Endovascular Clot Retrieval for Acute Ischemic Stroke, a Discrete Event Simulation.

Huang S1, Maingard J2,3, Kok HK3,4, Barras CD5,6, Thijs V7,8, Chandra RV9,10, Brooks DM2,3,8, Asadi H2,3,8,9.

Author information

1
The Canberra Hospital, Canberra, ACT, Australia.
2
Interventional Neuroradiology Service, Department of Radiology, Austin Health, Heidelberg, VIC, Australia.
3
Faculty of Health, School of Medicine, Deakin University, Waurn Ponds, VIC, Australia.
4
Interventional Radiology Service, Department of Radiology, Northern Health, Epping, VIC, Australia.
5
South Australian Health and Medical Research Institute, The University of Adelaide, Adelaide, SA, Australia.
6
Department of Radiology, Royal Adelaide Hospital, Adelaide, SA, Australia.
7
Stroke Division, Department of Neurology, Austin Health, Melbourne, VIC, Australia.
8
Stroke Division, The Florey Institute of Neuroscience and Mental Health, Parkville, VIC, Australia.
9
Interventional Neuroradiology, Monash Imaging, Monash Medical Centre, Clayton, VIC, Australia.
10
Department of Surgery and Department of Medicine, Monash University, Clayton, VIC, Australia.

Abstract

Objective: Endovascular clot retrieval (ECR) is the standard of care for acute ischemic stroke due to large vessel occlusion. Performing ECR is a time critical and complex process involving many specialized care providers and resources. Maximizing patient benefit while minimizing service cost requires optimization of human and physical assets. The aim of this study is to develop a general computational model of an ECR service, which can be used to optimize resource allocation. Methods: Using a discrete event simulation approach, we examined ECR performance under a range of possible scenarios and resource use configurations. Results: The model demonstrated the impact of competing emergency interventional cases upon ECR treatment times and time impact of allocating more physical (more angiographic suites) or staff resources (extending work hours). Conclusion: Our DES model can be used to optimize resources for interventional treatment of acute ischemic stroke and large vessel occlusion. This proof-of-concept study of computational simulation of resource allocation for ECR can be easily extended. For example, center-specific cost data may be incorporated to optimize resource allocation and overall health care value.

KEYWORDS:

ECR; discrete event simulation (DES); endovascular clot retrieval; mechanical thrombectomy; resource allocation; resource optimization; workflow simulation

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