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

Description: Read the November 14 2019 issue of Health Watch, published by the Health Section. Practical information for actuaries, articles impact sections, section activities.

Description: According to the Actuarial Standards Board’s Actuarial Standard of Practice No. 49 (ASOP 49), actuarially sound Medicaid managed care capitation rates will include a provision for underwriting gain in order to provide compensation for the risks assumed by an MCO. However, there is no generally accepted method for quantifying underwriting gain in managed Medicaid capitation rates. This article discusses a new model developed to address this void and calculate underwriting gain. The model was developed by researching existing financial literature and performing a statistical analysis of industry data to build out a framework that can be leveraged to estimate underwriting gain.

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

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: Article explores why many integrated delivery systems are struggling and what actions they need to take to succeed.

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 discusses the advantages, logistical concerns, and pricing formulas of implementing a TNHP, in which members pay less for treatment from providers who meet higher quality and cost-effectiveness standards.

Description: SOA interview with David Axene