Description: During the past few years, performance, cost, and risk trends for Medigap products have undergone substantial changes. Some of these trends could ease cost pressures on Medigap insurers, while others might increase them. Part 1 of this Issue Brief article discusses current changing trends and considerations around about how they are impacting Medigap.

Description: Actuaries are leveraging predictive modeling and machine learning more and more as they strive to provide greater value to their stakeholders. Actuarial standards of practice provide guidance around modeling and data. The world has changed significantly over the last few years, so it is important these models be tested for fairness as well as any statistical biases. This article addresses some of the key issues actuaries need to be aware of as Corporations and society work through the responsible us of this technology and underlying data.

Description: This is the second article in a four–part series. In this article, the industry's products are examined.

Description: The big event of 2023 is the launch of multiple Humira biosimilars, which began in January with the arrival of Amgen’s Amjevita on the market. The launch of Amjevita kicks off the process of changing the specialty drug marketplace in the United States. The expectation of the full impact of this big event is very hopeful, but there is still uncertainty several months after its beginning. Humira is the highest-selling drug in the world and has been since 2013 (Figure 1)—except for when COVID-19 projects temporarily surpassed it. The world is eagerly awaiting the launch of multiple biosimilar drugs to compete with Humira and hopefully bring more competitive pricing to the benefit of payers and patients everywhere.

Description: Today's provider reimbursement landscape

Description: WeChat is gradually becoming the main force of insurance online marketing for insurance products in China.

Description: Healthcare risk adjustment methodologies have become increasingly complex, largely due to the belief that complexity enhances accuracy. However, a disconnect exists between how healthcare risk is assessed and how payments are adjusted. This article explores the stark difference between measuring the accuracy of a risk assessment model at a member level, and that of a risk adjustment mechanism at the health plan level. I show that a highly simplified approach that does not use diagnosis codes can have similar accuracy on risk adjusted payments to models that do. These findings suggest that accuracy is not a barrier to developing simpler risk adjustment methodologies, which can improve payment accuracy and reduce the financial incentive for coding diagnoses.