Description: Sarah Rockhill, MPH, Geospatial Epidemiologist at CDC will discuss the latest in the Public Health 3.0 model in which leaders serve as Chief Health Strategists, partnering across multiple sectors and leveraging data and resources to address social, environmental, and economic conditions that affect health and health equity.

Description: In this webcast, the presenter will cover mental health in insurance products. Learn about the public’s increased interest in mental health and the ways insurers can meet this demand, how we can embed such offerings in insurance and how to successfully expand mental health coverage to those in need. The second speaker will cover how cancer and the simplified issue insurance market has evolved in Korea. In this session, the speaker will share the ease of selling / onboarding, thus allowing a greater penetration of products.

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: Cover a variety of topics centered around the unsupervised learning technique called 'clustering'. The presenters will take you through a wide array of topics that range from 'What is Clustering?' to 'How can I use this technique to solve problems I didn't even know existed?' In the health care industry, everyone is looking for an edge in reducing total spend-cluster analysis finds the unambiguous in an ambiguous framework of results.

Description: How well can we predict ACA HHS Risk Adjustment Data Validation (HRADV) results? Put another way, how susceptible is the current HRADV process to small sample volatility? What potential remedies are there to the current model's shortcomings? Every plan, regardless of how many members they have above the 1000 minimum, count equally if they qualify for an HRADV with how the result is interpreted, and the subsequent HRADV risk transfer ($330M in 2023).

Description: This session will discuss what is meant by 'bias' and 'fairness', the regulatory environment, how to identify and mitigate bias in models, and proper oversight and governance of model building. The session will include a case study.

Description: Over the last 10 years, Artificial Intelligence (AI) healthcare foundation models have made tremendous strides when it comes to delivering highly accurate predictive results when compared to traditional models. This is especially true for models developed and trained on the ‘language’ of healthcare. When trained on healthcare data sequences (claims, EHR, CPT, ICD, pharmacy, lab, etc.), these models deliver far more accurate results as to WHAT beneficiary-level health events will occur and even WHEN they will occur.

Description: Session 1G: Rethinking Catastrophic Claims - Strategies for Sustainable Stop-Loss Pricing

Description: Session 9C: Adventures in AI - Group Insurance Perspectives

Description: This session will share insights on how to effectively evaluate and price the management high-cost claimants from three views: clinical, payer, and provider. Can predictive analytics help with underwriting? What are effective clinical mitigation strategies? Can VBC arrangements be used to reduce total cost of care, and how can that be priced in?