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: Response to March 2021 Practice Note on ASOP 6

Description: Actuaries are currently familiar with the impact of new technologies used primarily by clinicians and health care providers, such as new drugs and devices. The common theme emerging from many of the articles points to a technological revolution in health care accelerated by the COVID-19 crisis this year. Actuaries need to understand these technologies, their impact, learn how to drive this technological change and guide the response of their profession and their own health care organizations.

Description: This article focuses on mandatory supplemental benefits offered by general enrollment plans and Dual Eligible Special Needs Plans (D-SNPs) Medicare Advantage plans from 2017 to 2021.

Description: The coming wave of biosimilars could have a major impact on prescription drug spend in coming years. The big event of Humira biosimilar launches in 2023 will be significant in determining that ultimate impact. Payers are making coverage decisions. The more biosimilars are utilized, the greater the potential savings. How will biosimilar adoption be maximized?

Description: In this paper, we shine a light on the global public health epidemic of intimate partner violence (IPV). This first paper presents background information on the problem with a focus on health and economic impacts for the actuarial audience

Description: Python has become the fastest-growing major programming language in the world. It has surged in popularity over the last decade, following the development of its industry-leading capabilities in data science, machine learning and artificial intelligence, and it is now one of the most globally popular and widely used languages. In this article, I discuss some of the reasons for its popularity and explain why many actuaries and insurers have started using Python.

Description: CMS provides ACOs with monthly claims data for members, including their medical visits and ACO program eligibility. This data can be divided into Membership and Claims tables through the ETL process. However, when calculating PMPY at specific levels of detail using these tables, issues arise due to unrelated dimensions, leading to inaccurate and understated results. This article presents a solution to this problem by utilizing Tableau's data blending capabilities.

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: 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.