McGuire Thomas G, Zink Anna L, Rose Sherri
Health Economics, Department of Health Care Policy, Harvard Medical School.
Health Policy at Harvard University.
Am J Health Econ. 2021 Fall;7(4):497-521. doi: 10.1086/716199. Epub 2021 Oct 4.
Modifications of risk-adjustment systems used to pay health plans in individual health insurance markets typically seek to reduce selection incentives at the individual and group levels by adding variables to the payment formula. Adding variables can be costly and lead to unintended incentives for upcoding or service utilization. While these drawbacks are recognized, they are hard to quantify and difficult to balance against the concrete, measurable improvements in fit that may be achieved by adding variables to the formula. This paper takes a different approach to improving the performance of health plan payment systems. Using the HHS-HHC V0519 model from the Marketplaces as a starting point, we constrain fit at the individual and group level to be as good or better than the current payment model while the number of variables in the model. We introduce three elements in the design of plan payment: reinsurance, constrained regressions, and machine learning methods for variable selection. The fit performance of our alternative formulas with many fewer variables is as good or better than the current HHS-HHC V0519 formula.
用于支付个人健康保险市场中健康计划的风险调整系统的修改通常旨在通过在支付公式中添加变量来减少个人和团体层面的选择诱因。添加变量可能成本高昂，并导致编码升级或服务利用方面的意外诱因。虽然这些缺点是公认的，但它们难以量化，并且难以与通过在公式中添加变量可能实现的具体、可衡量的拟合改善相平衡。本文采用不同的方法来提高健康计划支付系统的性能。以市场中的HHS-HHC V0519模型为起点，我们将个人和团体层面的拟合限制为与当前支付模型一样好或更好，同时限制模型中的变量数量。我们在计划支付设计中引入三个要素：再保险、约束回归和用于变量选择的机器学习方法。我们具有少得多变量的替代公式的拟合性能与当前的HHS-HHC V0519公式一样好或更好。
