Description: This report is a discussion of the significant challenges involved in attempting to research with robust analysis the impact of disparities in health care access, cost and quality of care on financial wellness across racial, ethnic, socioeconomic and other demographic groups in the U.S. for reasons other than or in addition to income, wealth or employment status.

Description: This study evaluates how integrating social determinants of health (SDOH) into Medicaid risk adjustment models impacts financial outcomes for ACOs/MCOs, offering foundational insights for policymakers and actuaries.

Description: This report examines the demographic, medical, and pharmaceutical profiles of HIV-positive individuals in Medicare and how their needs differ from the general Medicare population.

Description: A multi-author collection exploring how catastrophe modeling can be adapted to life and health insurance, particularly in response to the 2025 Los Angeles wildfires.

Description: Summary of an expert roundtable on generative AI in healthcare and health insurance, focusing on use cases, governance, trust, risks, and evolving actuarial skills.

Description: In this presentation, actuaries and data scientists from Cleveland Clinic and Milliman discuss the development of a sepsis prediction model using machine learning. Presented with a unique challenge of handling massive amounts of data for near real-time risk scoring, the team took their model development to the cloud and delivered a product in less than a week through a unique virtual engagement.

Description: When designing and pricing a healthcare product, it is not only important to know how to model frequency and severity, but it is essential to understand human behavior. Of course, the issue of anti-selection in medical insurance is well known as individuals know more about their health and prior expenditures than an insurer does. This type of behavior is certainly rational, and to be expected.

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: Inspired by other financial savings products available to Canadians, this article describes a potential solution for financing future health expenses with the creation of a Registered Health Savings Account (RHSA). This solution has been thought on the basis of four premises : 1- Individual Accountability for Health Expenses; 2- Early Savings Leads to More Accumulation; 3-PreferredTax Treatment for Personal Health Savings; 4- Complementing current public and private health systems; Main elements of the RHSA are described (Contributions, Accumulation, Utilization of account, Management by financial institutions).RHSA'S expected balances and some other results under three scenarios (a person in good health, one in poor health, and one with unexpected death at age 45) are summarized in a table. Health expenses deducted yearly from RHSA's balance were estimated using total, per capita, provincial government health expenditures by age-band for Canada, in 2020, from the Canadian Institute of Heath Information’s (CIHI), which account for 71.8% of total expenditures, that is public + private. Thus 28.2% of total expenditures were used for private health expenditures, then forecasted to 2024 and projected using a 4% annual trend. Some objections to the solution explored were anticipated as well as some refutations to these objections.