Googling Location for Operating Base of Mobile Stroke Unit in Metropolitan Sydney

Abstract

Background and purpose: The recent advances in stroke therapy have placed focus on delivering care within the first hour after stroke onset (golden hour), principally through the use of Mobile Stroke Unit (MSU) to bring the hospital to the patient. The aim of this project is to search the location of MSU hub in Sydney, Australia, optimizing for catchment, transport to nearest thrombolysis and endovascular clot retrieval (ECR)/thrombectomy capable hospital and population at risk. Methods: Traveling time was performed using ggmap package in R to interface with Google Maps application program interface (API). This analysis estimates the travel time from the centroids of each suburbs to five potential MSU hubs (Royal Prince Alfred, Prince of Wales, Royal North Shore, Liverpool, and Westmead hospitals) and eight thrombolysis capable hospitals. It is proposed that the MSU should be deployed at ECR hub to cover the suburbs, not well-covered by thrombolysis and ECR capable hospitals. This step was performed by assigning membership to hospitals within 30 min traveling time to the ECR hub. The base hub of the MSU was proposed as the closest hub (providing ECR) to the least well-served suburbs. The population serviceable by MSU was estimated using stroke incidence studies in Melbourne and Adelaide. Results: The largest population, serviceable by MSU within 30 min (4,606 cases), 45 min radius (8,918 cases), and 60 min (10,084 cases), was Royal North Shore followed by Royal Prince Alfred, Liverpool, Westmead, and Prince of Wales hospitals. Prince of Wales hospital has the smallest catchment within 30 min (3,078 cases), 45 min (7,721 cases), and 60 min (9,984 cases). Suburbs at the edge of metropolitan Sydney such as the Northern Suburbs are less well-served by thrombolysis and ECR capable hospitals. There are 10 suburbs within 30 min travel of one hospital. The remainders are within 30 min of two or more hospitals. Conclusions: Any of the five endovascular clot retrieval capable hospitals are capable of serving as a hub for MSU. We provide a method to identify the hub based on location of suburbs less well-served by other hospital.

How to Improve the Management of Acute Ischemic Stroke by Modern Technologies, Artificial Intelligence, and New Treatment Methods

“…Accurate knowledge of realtime traffic information optimizes patient transport, and the precise GPS location of the emergency ambulance allows the stroke team to be ready before the patient's arrival at the center. In such a case, hospital workflow is blocked for a relatively limited period [31,32].…”

“…It is extremely critical in high-density population areas such as metropolitan areas. Such a logistic system is currently used in Sydney for the selection of the nearest center according to arrival time, not according to the real distance between the GPS of the ambulance and the hospital [31].…”

Mobile stroke units for acute stroke care in the south of sweden

“…Al Fatah et al (2018) use agent-based simulation to evaluate two stroke transport policies, i.e., nearest hospital and nearest hospital towards the stroke center, concerning where to transport potential stroke patients for diagnosis. Sarraj et al (2020) introduce two optimization models, which they deploy in four states of the USA, in order to assess the same policies that Al Fatah et al study. There are some recent studies on the optimal placement of an MSU in a geographical region, where the impact of placing an MSU for inhabitants of urban (Phan et al, 2019;Rhudy Jr. et al, 2018) or rural areas (Mathur et al, 2019) are investigated. Rhudy et al (2018) optimize emergency service delivery for stroke patients in the city of Memphis, USA, using geospatial analysis of the distribution of stroke cases.…”

“…Rhudy et al (2018) optimize emergency service delivery for stroke patients in the city of Memphis, USA, using geospatial analysis of the distribution of stroke cases. Phan et al (2019) employ the ggmap interface with the Google Maps API to identify the optimal placement of an MSU in Sydney, Australia. Dahllöf et al (2018) use expected value optimization in order to identify the optimal location of an MSU in the Skåne county of Sweden.…”

Impact of mobile stroke units

“…Computational modelling of potential MSU missions can be supportive in consideration of the most efficient placement of future MSU programmes. 69 70 For this aim, even smartphone applications have been developed ( https://gntem3.shinyapps.io/ambmc/ ). Conditional probability modelling studies may also provide important information about the potential superiority of the MSU approach over other approaches, such as the drip-and-ship strategy, in a given region.…”