Modeling and Optimizing Emergency Department Workflow
Emergency department operations present numerous challenges for systems optimization. The human-centered service environment is complex and volatile, both for patients and care providers. In this paper, a system is described that couples machine learning, simulation and optimization decision support to improve the efficiency and timeliness of care in the emergency department, while reducing avoidable re-admissions. The model allows one to optimize the ED workflow globally, taking into account the uncertainty of human disease characteristics and care patterns, so as to drive the length of stay and wait-time for patients to a minimum.