Description: On September 14, 2022, The Wall Street Journal hosted its ‘Future of Health’ event, which was a virtual event featuring three sessions on some emerging cutting-edge topics that could impact the future healthcare environment as well as health care actuaries. This report summarizes the event.

Description: The COVID-19 pandemic initially caused some health care services to shift away from in-person transactions to a digital form, including telehealth, e-mails, remote patient monitoring and other forms of communication. If sustained, numerous effects and counter-effects both in the cost and quality of care as well as long-term outcomes for patient health may emerge.

Description: This report explores AI bias in insurance and health data, highlighting the ethical role of actuaries and offering cross-disciplinary guidance for fairer decision-making systems.

Description: This report is presented in the form of a Tableau dashboard and is the sixth public release from this report series and contains the results of an excess mortality analysis for the fourth quarter of 2021. Data from 30 companies representing approximately 69% of the industry’s face amount inforce were included in the analyses in this report. A total of 3.2 million death claims from individual life policies from 2015 through December 31, 2021 make up the basis of the analyses. The report also compares the experience under COVID-19 of the insured population versus the general population.

Description: The use of Risk Adjustment in Medicare Advantage (MA) has come under scrutiny due in part to a higher rate of increase of costs of its participating Health plans relative to traditional Medicare despite statutory requirements that reimbursement for these two programs must be actuarially equivalent.

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 outlines a framework for interpretable machine learning algorithms for detecting fraud in health insurance. It discusses the challenges of using black-box machine learning models in the insurance industry and proposes various interpretable methods to enhance transparency and understanding.

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.