Description: This article provides an overview of behavioral economic theory as it relates to health and welfare insurance plans and provides summaries of two sample applications of the theories: enrollment in health insurance exchanges created by the Patient Protection and Affordable Care Act, and product design considerations in light of vast health illiteracy.

Description: Learn about key actuarial and analytic tools and methodologies used by commercial health insurance to identify and quantify drivers of healthcare cost and utilization (including wasteful and low-value ones) and risks. Use case scenarios in which these tools and methodologies apply, such as underwriting, reinsurance, medical management for population health and chronic diseases, managed care contracting, insurance market reform, provider payment reform, etc., will also be covered. Experience from the U.S., China and other parts of the world will be shared.

Description: The prospects for China's health insurance has increased significantly in 2018, incentivized by a series of favorable government policies to support the development of commercial health insurance. However, the market still lacks relatively mature products and services. Overall there is a common understanding in the industry that good coverage plus service is the foundation for health insurance future development, and product innovation plus care management is key for an insurance company to maintain sustainable development in the competition. In this presentation, Dr. Yang will discuss the market trend both at home and abroad with respect to health management as well as its impact on product design, pricing and cost control. With the development of internet and big data, today’s health actuary has a special role in taking advantage of the digital transformation and achieving a high-quality and efficient health care service model.

Description: Subtitle: My real-life connection to (almost) every supplemental product Data is king, but maybe it should be dethroned once in a while so we can better understand the "why" behind the data.