Improving Patient Transfer Protocols for Regional Stroke Networks

Currently, stroke patients are transported to the nearest stroke center, following specific protocols. Yet, these protocols do not consider many factors, including the spatial variation in population density, the stroke’s severity, the time since stroke onset, and the congestion level at the receiving stroke center. We develop an analytical framework that enriches the stroke transport decision-making process by incorporating these factors. Our research contributes to the literature of stroke care systems by (i) developing the first analytical framework to determine the optimal primary hospital destination in a regional stroke network and (ii) comparing the impact of incorporating prehospital triaging on health outcomes. To this end, we develop an efficient reformulation for allocation problems with stochastic demand and multiserver system under congestion. We derive data-driven outcome prediction models embedded in mixed integer second-order cone programming formulation. Our framework is applied to two real-life cases: Montreal and Quebec City Stroke Networks. We show that adopting a triage strategy could lead to significantly improved health outcomes, where the magnitude of these improvements varies with the networks’ sizes and congestion levels. In the Montreal case, our proposed policy may increase the ratio of patients for therapeutic intervention eligibility by 12.5% while improving by 69% the number of patients with more than two days of emergency department boarding delays. Our results reveal that it is important to consider the network’s characteristics in making a decision for or against implementing a prehospital triage strategy. Finally, we propose a heuristic policy that provides a promising performance while also being easy to implement.