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: Renal disease is a very heterogenous medical condition whose end state is dialysis, a recurring cost intensive medical condition. Dialysis cost is one of the major medical cost drivers in USA. Insufficient and improper care management in the early stages of CKD (chronic kidney disease) leads to early incidence of end stage renal disease (ESRD) among CKD patients which not only leads to high medical costs, but also reflects poor health quality.

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

Description: Almost half of recent FDA approvals have been for orphan drugs. This trend is expected to continue in 2023. Orphan drugs are priced more than 30 times higher than non-orphan drugs, therefore their cost burden in aggregate has the potential to overwhelm the budgets of payers, especially that of smaller health plans. Hear how the cost impact analysis for smaller health plans require different considerations than for a larger health plan when modeling the impact of rare diseases.

Description: "Risk adjustment is an important aspect of establishing appropriate capitation rates. When a set of services is carved out for specific management under a sub-capitation, establishing appropriate adjustments for varying levels of underlying risk is more complicated. Dive into analytical and operational challenges and resolutions as presenters share more about developing a risk adjustment model specific to the carve-out of behavioral health services through a real-life case study. You will further explore a definition of the use case, availability of historical data sets, selection of the modeling approach, learnings from the iterative modeling process, and the potential application of the results. After attending this session, you will be able to: Understand why a behavioral health specific-risk adjustment model has business value Understand the unique challenges related to developing risk adjustment on member and cost subsegments Understand the impact of trade-offs between model complexity and interpretability in the context of implementation Increase awareness of data nuances and requirements unique to this type of project Describe analytic techniques that can be investigated for use in projects of similar business objectives"

Description: CMS has required hospitals to publish their chargemaster rates, cash discounts and negotiated rates as of January 1, 2021. Our panel is comprised of a data strategist, an executive from a large hospital system and an actuary.

Description: "Explore a general introduction to the opportunity of applying a relatively newer area in machine learning called uplift modeling, which has been successfully used in retail (Wayfair etc). The concepts are parallel to the intervention programs in health care, where we are interested in measuring the treatment effects and ultimately directing the right members to the most effective programs, driving the highest value for the programs both from a financial and health outcomes perspective. Uplift modeling can also be viewed in context of causal modeling where we are interested in assigning cause to a treatment relative to a member not receiving the treatment. Check out an introduction on the concept, seehow it relates and builds on existing work, and use a demo to showcase the potential of integrating this ML approach to current work."

Description: This session will examine bias from a social science perspective. It will discuss the three types of systemic biases using the National Institute of Standards and Technology (NIST) model. The NIST model details the three types as historical bias, societal bias, and institutionalbias. The session will also provide several examples of each type to provide a framework for identifying the social structure of bias.

Description: Session 4B: Impact of GLP-1 Drugs on Health Care Cost Trend and Clinical Outcomes

Description: In this session, attendees will understand the role of AI in risk management and underwriting. You will see insightful case studies highlighting AI-driven techniques for enhancing precision and efficiency in these areas. Additionally, the session will cover advanced AI applications in claim management and fraud detection, showcasing cutting-edge strategies to mitigate risks and streamline operations.