Addressing hospital emergency department (ED) overcrowding is a critical challenge for many healthcare systems worldwide. To address this challenge, many hospitals have been experimenting with innovative patient flow designs. 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 unit, also known as the Rapid Medical Assessment (RMA) unit.  While this can potentially increase operational efficiency by addressing the problem of bed availability, it can degrade performance if patients are not correctly routed through the system.  Successful implementation of this design, thus, significantly depends on understanding which patients should be routed to the RMA unit. To assist our partner hospital, we developed a machine learning model trained on large-scale data capable of providing a personalized risk score for each arriving patient on whether 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 RMA unit. We find that the optimal protocol depends not only on the predicted risk scores but also on the machine learning model's accuracy and ED characteristics. Finally, we use simulation analyses to compare the performance of our recommended RMA-based design with more traditional ED flow approaches such as a "fast track" or a "physician in triage" based design. Our results suggest that following the RMA design under our recommended protocol can bring several advantages to EDs. It outperforms traditional patient flow designs due to the dynamic and efficient use of ED resources, especially in settings with a higher prevalence of high-acuity patient cases. Overall, this work provides a roadmap to healthcare systems that seek to implement data-driven patient flow systems to improve ED operations.