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Addressing hospital emergency department (ED) overcrowding is a critical challenge for many healthcare systems worldwide. Many hospitals (including our partner hospital) have been experimenting with innovative patient flow designs to address this challenge. A promising new design is to separate patients who can be served vertically (e.g., on a regular chair as opposed to horizontally on an ED bed) and route them to a different area termed the Vertical Processing Pathway (VPP) unit. While this can potentially increase operational efficiency by removing the burden caused by a main ED bottleneck—lack of bed availability—it can degrade performance if patients that are routed to the VPP unit need to be sent back to be served in an ED bed, or if some patients that could have been served in the VPP unit end up occupying an ED bed. Successful implementation of this design, thus, significantly depends on understanding which patients should be routed to the VPP unit and when. To assist our partner hospital and other EDs, we develop a machine learning model trained on large-scale data capable of providing a personalized risk score for each arriving patient on whether or not they will eventually need an ED bed. We then feed these risk scores to an analytical model of patient flow to characterize the optimal protocol for utilizing the VPP unit. We find that the optimal protocol depends not only on the predicted risk scores but also on the machine learning model’s accuracy as well as some of the main ED characteristics (e.g., patient arrival intensity and congestion level). To gain deeper insights, we make use of simulation analyses calibrated with hospital data and compare the performance of our recommended VPP-based patient streaming design with more traditional ED flow approaches such as “fast track” or “physician in triage.” Our results suggest that following the VPP design under our recommended protocol can bring several advantages to EDs, allowing them to significantly improve their operations.