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Frequent emergency department (ED) users impose a significant burden on the healthcare system. Case management (CM) can target potential frequent users to reduce their ED utilization. As CM is costly, it is essential to enroll individuals who will achieve improved health outcomes. We present a novel machine learning framework for effectively selecting enrollees for CM. Unlike traditional methods that only target current frequent users, our approach selects members for enrollment based on their likelihood of frequent use and their potential benefit from the program. We develop predictive models for two types of future frequent users—“jumpers” whose current ED usage is low but will increase significantly in the future, and “repeaters” whose ED usage remains consistently high. We propose a strategy to select optimal combinations of these two types of users, and compare the cost effectiveness. We demonstrate …