Googling Service Boundaries for Endovascular Clot Retrieval Hub Hospitals in a Metropolitan Setting

Abstract

Background and Purpose— There is great interest in how endovascular clot retrieval hubs provide services to a population. We applied a computational method to objectively generate service boundaries for such endovascular clot retrieval hubs, defined by traveling time to hub. Methods— Stroke incidence data merged with population census to estimate numbers of stroke in metropolitan Melbourne, Australia. Traveling time from randomly generated addresses to 4 endovascular clot retrieval–capable hubs (Royal Melbourne Hospital [RMH], Monash Medical Center [MMC], Alfred Hospital [ALF], and Austin Hospital [AUS]) estimated using Google Map application program interface. Boundary maps generated based on traveling time at various times of day for combinations of hubs. Results— In a 2-hub model, catchment was best distributed when RMH was paired with MMC (model 1a, RMH 1765 km 2 and MMC 1164 km 2 ) or with AUS (model 1c, RMH 1244 km 2 and AUS 1685 km 2 ), with no statistical difference between models ( P =0.20). Catchment was poorly distributed when RMH was paired with ALF (model 1b, RMH 2252 km 2 and ALF 676 km 2 ), significantly different from both models 1a and 1c (both P <0.05). Model 1a had the greatest proportion of patients arriving within ideal time of 30 minutes followed by model 1c ( P <0.001). In a 3-hub model, the combination of RMH, MMC, and AUS was superior to that of RMH, MMC, and ALF in catchment distribution and travel time. The method was also successfully applied to the city of Adelaide demonstrating wider applicability. Conclusions— We provide proof of concept for a novel computational method to objectively designate service boundaries for endovascular clot retrieval hubs.