Vertical Patient Streaming in Emergency Departments

Tackling hospital emergency department (ED) overcrowding is a paramount challenge for healthcare systems. To combat this issue, an innovative approach is to identify patients who can be served vertically (i.e., in a seated position) and route them to a dedicated area termed the vertical processing pathway (VPP). Successfully implementing this design requires understanding which patients should be routed to the VPP and when. Currently, the decision to leverage the VPP is made in an ad hoc fashion. To assist our partner hospital and other EDs in capturing the value of the VPP, we develop a machine learning model that provides personalized risk scores predicting whether each arriving patient needs an ED bed. We use these scores as input to a stochastic patient flow model and analytically characterize the optimal VPP policy that minimizes length of stay. We then combine our results to derive an interpretable VPP patient streaming protocol suitable for implementation in practice and conduct a before-and-after experiment in which we leverage empirical analyses to evaluate the impact of integrating it into practice. The implemented protocol led to an 11-minute (4.2%) reduction in ED length of stay in our partner hospital without any adverse effect on quality-of-care outcomes. This effect was statistically significant and remained robust after controlling for various confounding factors and potential sources of endogeneity. Our VPP protocol can also be utilized in other EDs, offering operational improvements without requiring additional resources.

This paper was accepted by Stefan Scholtes, healthcare management.

Funding: S. Saghafian received financial support from the NSF [Grant CMMI-15 62645], “Data-Driven Management of Post-Transplant Medications,” which partially enabled this work, as well as funding and support from the Harvard Data Science Initiative and the Harvard Mossavar-Rahmani Center for Business and Government.

Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.07517.