Data-Driven Allocation of Preventive Care With Application to Diabetes Mellitus Type II
This work addresses cost-effective disease prevention for healthcare managers, offering a scalable solution that is incremental in applying existing techniques to a new domain.
The authors tackled the problem of allocating preventive care for diabetes mellitus type II by developing a data-driven decision model that combines counterfactual inference, machine learning, and optimization, resulting in potential annual savings of $1.1 billion if applied to the U.S. population.
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.