Application of Strategic Transport Model and Google Maps to Develop Better Clot Retrieval Stroke Service

Frontiers in Neurology

Velandai Srikanth¹  Atousa Tajaddini²  Hai L. Vu²  Graham Currie²  Richard Beare⁴  Thanh G. Phan⁶  Henry Ma⁶ 

[1] Central Clinical School, Monash University, Melbourne, VIC, Australia;Department of Civil Engineering, Institute of Transport Studies, Monash University, Melbourne, VIC, Australia;Department of Medicine, Frankston Hospital, Peninsula Health, Melbourne, VIC, Australia;Developmental Imaging, Murdoch Children's Research Institute, Melbourne, VIC, Australia;Stroke Unit, Monash Health, Melbourne, VIC, Australia;Stroke and Aging Research Group, Medicine, School of Clinical Sciences, Monash University, Melbourne, VIC, Australia;

关键词: stroke;    transport;    optimization;    Google Maps;    endovascular clot retrieval;   

DOI  :  10.3389/fneur.2019.00692

来源: DOAJ

【 摘 要 】

Background and purpose: Two hubs are designated to provide endovascular clot retrieval (ECR) for the State of Victoria, Australia. In an earlier study, Google Maps application programming interface (API) was used to perform modeling on the combination of hospitals optimizing for catchment in terms of current traveling time and road conditions. It is not known if these findings would remain the same if the modeling was performed with a large-scale transport demand model such as Victorian Integrated Transport Model (VITM). This model is developed by the Victorian State Government Transport has the capability to forecast travel demand into the future including future road conditions which is not possible with a Google Maps based applications. The aim of this study is to compare the travel time to potential ECR hubs using both VITM and the Google Maps API and model stability in the next 5 and 10 years.Methods: The VITM was used to generate travel time from randomly generated addresses to four existing ECR capable hubs in Melbourne city, Australia (i.e., Royal Melbourne Hospital/RMH, Monash Medical Center/MMC, Alfred Hospital/ALF, and Austin Hospital/AUS) and the optimal service boundaries given a delivering time threshold are then determined.Results: The strategic transport model and Google map methods were similar with the R2 of 0.86 (peak and off peak) and the Nash-Sutcliffe model of efficiency being 0.83 (peak) and 0.76 (off-peak travel). Futures modeling using VITM found that this proportion decreases to 82% after 5 years and 80% after 10 years. The combination of RMH and ALF provides coverage for 74% of cases, 68% by 5 years, and 66% by 10 years. The combination of RMH and AUS provides coverage for 70% of cases in the base case, 65% at 5 years, and 63% by 10 years.Discussion: The results from strategic transport model are similar to those from Google Maps. In this paper we illustrate how this method can be applied in designing and forecast stroke service model in different cities in Australia and around the world.

【 授权许可】

Unknown