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.

翻译：问题定义：医疗成本的持续增长凸显了疾病预防有效性的重要性。然而，目前缺乏用于分配预防性护理的决策模型。方法/结果：本文开发了一种基于数据的决策模型，用于确定对高风险患者进行具有成本效益的预防性治疗方案分配。具体而言，我们结合反事实推断、机器学习与优化技术，构建了一个可扩展的决策模型，能够利用高维医疗数据（如现代电子健康档案中的数据）。该决策模型基于89,191名糖尿病前期患者的电子健康档案进行验证。我们比较了该数据驱动决策模型分配的预防性治疗方案（二甲双胍）与现行实践方案的差异。研究发现，若将本方法应用于美国人群，每年可节省11亿美元。最后，我们分析了不同预算水平下的成本效益。管理启示：本研究为健康管理决策提供了支持，旨在以更低成本实现有效的疾病预防。重要的是，我们的决策模型具有通用性，因此可应用于其他可预防疾病的预防性护理有效分配。