When is an ounce of prevention worth a pound of cure? Identifying high-risk candidates for case management

ABSTRACT Case management is a $6 billion industry that employs over 34,000 people in the United States alone. Traditionally, case management has been utilized to help patients navigate the health care system and to coordinate care in the hope of lowering costs and achieving better health outcomes. However, since enrollment into these programs is typically either universal or limited to very sick patients, studies on the cost-effectiveness of case management programs find that their performance is mixed, at best. In this article we posit an opportunity to improve outcomes and lower costs by targeting certain patients for case management and early intervention. Utilizing modern data mining methods, we develop a methodology to identify these patients, who we describe as “jumpers” because their costs are currently low but will increase significantly in the near future. Given the performance of the prediction models, we also show that unless case management can prevent over 7.5% of health care cost increases, it may benefit enrolled members but will not reduce overall costs. The article then introduces a performance bounding methodology that characterizes the best obtainable prediction accuracy on a given data set. The derived upper bound demonstrates that searching for jumpers presents a far more challenging prediction problem than identifying future high-cost members, which is the traditional approach to selecting case management candidates.