Optimal pricing of employer-based Health Plans

基于雇主的健康计划的最佳定价

• 批准号: 6910150
• 负责人: CAROLINE S CARLIN
• 金额: $ 3.18万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-03-15 至 2006-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6910150
• 关键词: behavioral /social science research tag; clinical research; decision making; health care cost /financing; health care model; health insurance; human data; managed care; model design /development

DESCRIPTION (provided by the applicant): One of the biggest challenges for employers in the management of their health plan options is determining the employee premium contribution structure. Most employers are driven by cost control, and want to create an incentive for employees to enroll in efficient plans. However, pricing schemes typically ignore the fact that efficiency cannot be determined by a simple comparison of average per-person costs across plans, because of the influence of patient selection. For example, the impact of selection may cause a closed-panel HMO to appear disproportionately efficient, as healthy people migrate to this low-cost option. This research will explore the interrelationships between pricing, employee choice and the resulting average costs, in an effort to separate the selection impact from the influence of plan efficiency on average costs. Specifically, the applicant will: 1. Develop a utility-based choice model to predict the patient's choice of plan, given contribution structure; 2. Develop a cost model to explore how patterns of patient choice influence average cost by plan, allowing the influence of selection to be separated from other plan cost drivers; 3. Model the impact of different pricing philosophies in both self-insured and fully-insured scenarios. These models will be based on three years of enrollment data and two years of claims data from a large employer with four plan options. Detailed claims data allows the use of the Johns Hopkins Adjusted Clinical Groups (ACG) Case-Mix software to develop measures of the health status of each individual, to be used as a covariate in the modeling. Bayesian inference with Gibbs sampling will be used to estimate a multinomial probit choice model for health plan elections. The applicant will evaluate two hierarchical cost models that take into account the selection effect of seeking care, with random effects to capture the influence of correlation within families. Using these models as tools, the stability of the plan offerings will be explored under various employer pricing philosophies: percent of premium contribution, fixed dollar contribution, and fixed dollar contribution adjusted for the plans' health risk mix. In the fully-insured scenario, the problem extends to solving the insurers' Nash equilibrium problem, as they set their premium levels, given knowledge of the employer's contribution strategy.

描述（由申请人提供）：雇主在其健康计划方案管理方面面临的最大挑战之一是确定员工的保费贡献结构。 大多数雇主都受成本控制的驱动，并希望为员工提供有效计划的动力。 但是，定价方案通常会忽略这样一个事实，即由于患者选择的影响，效率不能通过对整个计划的平均每人成本进行简单比较来确定。 例如，随着健康的人迁移到这种低成本的选择，选择的影响可能会导致封闭式HMO显得不成比例。 这项研究将探讨定价，员工选择和由此产生的平均成本之间的相互关系，以努力将选择影响与计划效率对平均成本的影响分开。 具体而言，申请人将： 1。开发一个基于公用事业的选择模型，以预测患者对计划的选择，给定贡献结构； 2。开发一个成本模型，以探讨患者选择模式如何影响计划的平均成本， 使选择的影响与其他计划成本驱动力分开； 3。建模不同的定价哲学在自保和全面保险中的影响 方案。 这些模型将基于三年的注册数据和来自有四个计划选择的大型雇主的两年索赔数据。 详细的索赔数据允许使用Johns Hopkins调整后的临床组（ACG）案例混合软件来开发每个人的健康状况的度量，并将其用作建模中的协变量。 使用Gibbs采样的贝叶斯推断将用于估计健康计划选举的多项式概率选择模型。 申请人将评估两个层次成本模型，这些模型考虑了寻求护理的选择效果，并随机效应以捕获家庭内部的相关性影响。 使用这些模型作为工具，将在各种雇主定价理念中探索计划产品的稳定性：保费贡献的百分比，固定美元捐款以及针对计划的健康风险组合调整的固定美元捐款。 在充分保险的情况下，鉴于对雇主的贡献策略的了解，问题扩展到解决保险公司的NASH均衡问题，因为他们设定了溢价水平。