2016 Wagner Prize Finalist - Mayo Clinic

Data-driven Optimization For Multi-disciplinary Staffing In Mayo Clinic Improves Patient Experience

Emergency Department (ED) patient volumes fluctuate throughout the day leading to delays. Therefore, it is critical to match the staff capacity to the patient demand. A data-driven approach applied regression trees to system-generated data to produce an ideal patient volume representing ED load under optimal staffing conditions. The ideal patient volume was then used to optimize multi-disciplinary staffing levels. The new shift design significantly improved several patient-centered metrics.