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: Read the December 2019 issue of Health Watch, published by the Health Section. Practical information for actuaries, articles impact sections, section activities.

Description: On average, contracts receiving their first star rating receive 3.20 stars, compared to existing or established contracts receiving 3.90 stars on average. This 0.70 disparity in quality ratings creates significant hardship on new contracts when this lower star rating decreases their payments in the following calendar year. This article explores the disparity in Medicare Advantage Star Ratings between contracts receiving their first star ratings (“New” contracts) and existing contracts, including: 1. Understanding what happens to Medicare Advantage Payments when contracts receive lower star ratings? – What is the Star Rating cliff and why does it matter to new contracts? 2. Explaining when a contract receives their first star rating and how much enrollment this requires – When does the Star Rating cliff occur? 3. Noting which specific star measures “New” contracts struggle with, including those contracts under large parent organizations, contracts with rapid growth, and all other new contracts. – How can my contract avoid the Star Rating cliff?

Description: Telehealth benefits, obstacles, and delivery systems

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: SOA interview with David Axene

Description: Methods and assumptions used in the valuation of savings by external care vendors

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: Interview of people knowledgeable about risk adjustment and the use of social determinants of health with that context for Medicaid.