Description: This article provides an overview of critical illness insurance: history, product description, and the three primary ways to offer critical illness coverage in the United States. This article also contains an illustration of an issue age premium rate calculation, as well as product features and rating applications specific to the various types of critical illness coverage.

Description: Discover the key components of value-based care (VBC) contracts and their impact on provider credibility. Learn about benchmarks, measurement period results, and the unique challenges providers face in managing insurance risk. Gain insights into reducing costs and aligning financial incentives in the US healthcare market.

Description: This article provides a brief overview of the current state of machine learning (ML) and artificial intelligence (AI) as they apply to actuarial science, including discussion around the integration of actuarial judgment with ML and AI. It concludes with an example of explainable ML through a python tool known as SHAP (SHapley Additive exPlanations), which is able to explain the key drivers behind individual ML or AI predictions.

Description: This article is a brief overview of what digital health is, how it is regulated, and who some of the big unicorns are.

Description: Review of events and highlights from the Spring 2019 SOA Health Meeting in Phoenix, AZ.

Description: Review of events and highlights from the Spring 2019 SOA Health Meeting in Phoenix, AZ.

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 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: 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.

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.