The majority of health care expenditures are incurred by a small portion of the population. Care management or intervention programs may help reduce medical costs, especially those of extremely high-cost members. For these programs to be effective, however, the insurer must identify and select potential high-cost members to be assigned to an intervention before they incur those costs. Because high medical costs are often connected to an accident or traumatic event that cannot be anticipated, it can be difficult to predict who will be high-cost in the future. In this article, we explore the use of machine learning in predicting high-cost members. Specifically, we use the extreme gradient boosting algorithm to develop risk scores for members based on demographic, medical, and financial histories. To select members for intervention, we develop asymmetric cost matrices that account for potentially unequal savings or losses for assigning interventions to members. We show how these matrices can be reduced to a function of the expected savings per dollar of intervention, which is easily used to optimize the risk score threshold at which members are assigned an intervention. These techniques, which can be tailored to the specific needs of an insurer, may help insurers select the optimal members for intervention programs, reduce overall costs, and improve member health outcomes.

大多数医疗保健支出是由一小部分人承担的。护理管理或干预计划可能有助于降低医疗费用，尤其是成本极高的会员的医疗费用。但是，为了使这些计划有效，保险人必须在选择产生费用的潜在高成本成员之前确定并选择他们。由于高昂的医疗费用通常与无法预料的事故或创伤事件有关，因此很难预测谁将在未来成为高费用。在本文中，我们探索了机器学习在预测高成本成员中的用途。具体来说，我们使用极端梯度提升算法根据人口，医学和财务历史为会员制定风险评分。要选择成员进行干预，我们开发了不对称的成本矩阵，可以解释为成员分配干预措施时潜在的不平等节省或损失。我们展示了如何将这些矩阵简化为预期的每美元干预成本节省的函数，可以轻松地将其用于优化为成员分配干预的风险评分阈值。这些技术可以根据保险公司的特定需求进行定制，可以帮助保险公司为干预计划选择最佳成员，降低总体成本并改善成员健康状况。它很容易用于优化为成员分配干预措施的风险评分阈值。这些技术可以根据保险公司的特定需求进行定制，可以帮助保险公司为干预计划选择最佳成员，降低总体成本并改善成员健康状况。它很容易用于优化为成员分配干预措施的风险评分阈值。这些技术可以根据保险公司的特定需求进行定制，可以帮助保险公司为干预计划选择最佳成员，降低总体成本并改善成员健康状况。