Geospatial Visualization of Mobile Stroke Unit Dispatches: A Method to Optimize Service Performance

Abstract

Background: Timely treatment of acute ischemic stroke is crucial to optimize outcomes. Mobile stroke units (MSU) have demonstrated ultrafast treatment compared to standard emergency care. Geospatial analysis of the distribution of MSU cases to optimize service delivery has not been reported. Methods: We aggregated all first-year MSU dispatch occurrences and all cases classified by clinical teams as true stroke by zip code and calculated dispatch and true stroke incidence rates. We mapped dispatch and stroke cases and symbolized incidence rates by standard deviation. We confirmed visual impressions of clusters from map inspection by local Moran’s I, boxplot inspection, and t test. We calculated service areas using drive times to meet dispatch and true stroke need. Results: A significant cluster of high dispatch incident rate was confirmed around our MSU base in urban Memphis within a 5-min driving area supporting the initial placement of the MSU based on 911 activation. A significant cluster of high true stroke rate was confirmed to the east of our MSU base in suburban Memphis within a 10-min driving area. Mean incident longitude of cases of true stroke versus disregarded status was significantly eastward (p = 0.001785). Conclusion: Our findings will facilitate determination of socio-spatial antecedents of neighborhood overutilization of 911 and MSU services in our urban neighborhoods and service delivery optimization to reach neighborhoods with true stroke burden.

Improving Prehospital Stroke Services in Rural and Underserved Settings With Mobile Stroke Units

“…MSU operation is influenced strongly by setting—be it urban or rural, or high- or low-income. In metropolitan settings, factors such as traffic congestion and existing emergency response configurations as well as geo- and socio-spatial determinants of emergency service utilization impact transport modeling (122). In LMIC, road conditions impact both transport planning and may necessitate physical upgrades to the MSU vehicle.…”

Associative Analysis of Inefficiencies and Station Activity Levels in Emergency Response

“…These works play a more supportive role by augmenting EMS with decision support tools to better respond to emergencies [16]. Visualizations are used for various purposes, such as training and simulation [34,35], real-time management [36][37][38], and decision support [39,40]. On the knowledge discovery front, valuable data-driven insights are extracted from emergency response data.…”

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.…”

“…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. Phan et al (2019) employ the ggmap interface with the Google Maps API to identify the optimal placement of an MSU in Sydney, Australia.…”