Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe increase in healthcare costs is, perhaps, one of the most important issues that governments and
organizations face nowadays. An ageing population and technological advancements are the key
reasons for this phenomenon. In this scenario, proactive measures are very important. This work
aimed to improve the effectiveness of the prevention by helping the identification of the most
probable high-cost users of health services in future years. Data from 2015 to 2019 of approximately
30,000 Central Bank of Brazil’s Health Program’s enrollees were used to train, validate and test four
types of models, considering the kind of high-cost users (simple or cost-bloomers, i.e., non-high-cost
in previous periods) and the time-span between predictors and the dependent variable (none or one
year), an innovation suggested by other authors. Different percentual cut-off points to define highcost
were used, and up to 67% of high-risk users’ expenses could be correctly captured. Results
confirmed the importance of previous costs data for this kind of prediction and showed that costbloomers
and one-year time-span approaches reach good performance, creating opportunities to
improve users’ health outcomes while contributing to the fiscal sustainability of private and public
health systems
