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.

Description: Article discussing how the pursuit of value-based care as a consequence of health care reform laws such as the Affordable Care Act (ACA) and the Medicare Modernization Act has accelerated the use of risk adjustment and what it means to actuaries.

Description: Article reviews the data a year into the unwinding of continuous coverage provisions in Medicaid, the impact on enrollment counts, how capitation rates have been adjusted, and compares those results to what has been modeled with the SOA Mediciad Unwinding model.

Description: This article is an update on the No Surprises Act, which is a U.S. Act aimed to reduce the risk that a person unexpectedly receives out of network charges for their health care visits. This is Part 2; Part 1 was featured in Health Watch's Sept 2024 issue.

Description: There is a big wave of changes coming to the pharmacy market. The extent is not yet fully known, but there are trends we can follow that help us predict. Biosimilar uptake is key to driving savings, but it can take a while to ramp up. Biosimilars bring competition, and this coming wave will bring unprecedented Biosimilar competition.

Description: In 2018, the Financial Accounting Standards Board (FASB) adopted significant revisions to the U.S. GAAP insurance accounting literature, via Accounting Standards Update (ASU) 2018-12. The title of ASU 2018-12 is “Targeted Improvements to the Accounting for Long-Duration Contracts,” and as a result a common acronym for ASU 2018-12 is LDTI (long-duration targeted improvements).

Description: Savings from biosimilars have accelerated significantly in the past few years. There has been a number of relatively recent biosimilar launches that can help to shape our expectations of the financial impact that could result from this year’s big event. Savings from biosimilars have accelerated significantly in the past few years. There has been a number of relatively recent biosimilar launches that can help to shape our expectations of the financial impact that could result from this year’s big event.

Description: Introduction to additional actuarial research into development of and regulatory impact, economic impact and consumer impact of prescription drugs in the United States. Intended to further educate actuaries and others who work in this area and motivate more actuarial involvement to increase transparency, increase competition, better align stakeholder incentives and better mitigate total cost of care increases

Description: Social determinants of health (SDoH) have been a topic of growing interest in recent years, yet consensus about what the phrase represents and how health-related social needs should be studied or addressed by employers, health plans, providers and other stakeholders in the business of health care have been lacking. For many, low-income populations come to mind when considering the topic, yet SDoH include a wide range of factors beyond income. Many types of data are available, yet each presents distinct challenges and opportunities for analysis. In this article, we describe some of the underlying context for considering SDoH, as well as some of the potential data sources to consult and challenges to grapple with on this important topic.

Description: This Q&A article discusses the use of machine learning to analyze unstructured data. Supriya Ramachandra, a data scientist at SIKKA, explains how her team uses natural language processing techniques, technical aspects of neural networks, LSTM modeling, and natural language processing. Machine learning allows for the classification of large amounts of unorganized data, providing actionable information for actuaries.