  Problem Definition. Increasing costs of healthcare highlight the importance
of effective disease prevention. However, decision models for allocating
preventive care are lacking.
  Methodology/Results. In this paper, we develop a data-driven decision model
for determining a cost-effective allocation of preventive treatments to
patients at risk. Specifically, we combine counterfactual inference, machine
learning, and optimization techniques to build a scalable decision model that
can exploit high-dimensional medical data, such as the data found in modern
electronic health records. Our decision model is evaluated based on electronic
health records from 89,191 prediabetic patients. We compare the allocation of
preventive treatments (metformin) prescribed by our data-driven decision model
with that of current practice. We find that if our approach is applied to the
U.S. population, it can yield annual savings of $1.1 billion. Finally, we
analyze the cost-effectiveness under varying budget levels.
  Managerial Implications. Our work supports decision-making in health
management, with the goal of achieving effective disease prevention at lower
costs. Importantly, our decision model is generic and can thus be used for
effective allocation of preventive care for other preventable diseases.
