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When we encounter an unexpected critical health problem, a hospital’s emergency department (ED) becomes our vital medical resource. Improving an ED’s timeliness of care, quality of care, and operational efficiency while reducing avoidable readmissions, is fraught with difficulties, which arise from complexity and uncertainty. In this paper, we describe an ED decision support system that couples machine learning, simulation, and optimization to address these improvement goals. The system allows healthcare administrators to globally optimize workflow, taking into account the uncertainties of incoming patient injuries and diseases and their associated care, thereby significantly reducing patient length of stay. This is achieved without changing physical layout, focusing instead on process consolidation, operations tracking, and staffing. First implemented at Grady Memorial Hospital in Atlanta, Georgia, the system helped reduce length of stay at Grady by roughly 33 percent. By repurposing existing resources, the hospital established a clinical decision unit that resulted in a 28 percent reduction in ED readmissions. Insights gained from the implementation also led to an investment in a walk-in center that eliminated more than 32 percent of the nonurgent-care cases from the ED. As a result of these improvements, the hospital enhanced its financial standing and achieved its target goal of an average ED length of stay of close to seven hours. ED and trauma efficiencies improved throughput by over 16 percent and reduced the number of patients who left without being seen by more than 30 percent. The annual revenue realized plus savings generated are approximately $190 million, a large amount relative to the hospital’s $1.5 billion annual economic impact. The underlying model, which we generalized, has been tested and implemented successfully at 10 other EDs and in other hospital units. The system offers significant advantages in that it permits a comprehensive analysis of the entire patient flow from registration to discharge, enables a decision maker to understand the complexities and interdependencies of individual steps in the process sequence, and ultimately allows the users to perform system optimization.

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