Soroush Saghafian, Assistant Professor of Public Policy - Harvard Kennedy School, Harvard University
Soroush Saghafian, Assistant Professor of Public Policy - Harvard Kennedy School, Harvard University
Helping Emergency Rooms
Helping Emergency Rooms
Crisis level overcrowding conditions in Emergency Departments (EDs) have led hospitals to seek out new patient flow designs to improve both responsiveness and safety. I present the result of several years of collaboration with a few hospitals in an effort to improve the patient flow in EDs using operations management techniques. I first present a list of resulted innovations, and then focus on two specific ones. The first innovation that has attracted attention and experimentation in the emergency medicine community is a system in which ED beds and care teams are segregated and patients are “streamed" based on predictions of whether they will be discharged or admitted to the hospital. I present a combination of queueing analysis, Markov decision processes, and high-fidelity simulation models calibrated with hospital data to determine whether such a streaming policy can improve ED performance, where it is most likely to be effective, and how it should be implemented for maximum performance. The results suggest that the concept of streaming can indeed improve patient flow, but only in some situations. First, ED resources must be shared across streams rather than physically separated. This leads to propose a new “virtual-streaming” patient flow design for EDs. Second, to take full advantage of streaming, physicians assigned to admit patients should prioritize upstream (new) patients, while physicians assigned to discharge patients should prioritize downstream (old) patients. In the second innovation, I show that the current practice of prioritizing patients solely based on urgency is less effective than a new ED triage system that adds an up-front estimate of patient medical complexity to the conventional urgency-based classification. Using a combination of analytic models (queueing analysis under misclassification and Markov decision processes), and simulation analysis using hospital data, I show that the proposed complexity-based triage can substantially improve both patient safety (i.e., reduce the risk of adverse events) and operational efficiency (i.e., shorten the average length of stay). Finally, examining different ED patient flow designs, I show that streaming patients based on complexity information and prioritizing them based on urgency level is better than streaming them based on urgency and prioritizing them based on complexity. Separating simple and complex patients via streaming also facilitates the application of lean methods that can further amplify the benefit of complexity-based triage.