Description: Modern actuarial work, being quite data intensive, provides many opportunities for actuaries to use the statistical skills that are part of their basic training. Furthermore, modern computing allows us to analyze vast quantities of data intensively, and each package will provide statistical 'answers' to any questions the actuary might ask of it.

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: 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: 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: 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 explore governance considerations for insurance carriers leveraging machine learning models. During the session, panelists will share insights on the evolving predictive model and AI regulatory landscape, as well as techniques for developing or enhancing AI governance frameworks that comply with emerging regulatory guidance.

Description: Hear about some applications using quantile regression-derived individual and group claim distributions and how they may be used in various traditional health care modeling situations. The session will not focus on the technical use of quantile regression to obtain estimates of claim distributions, but more so is intended to focus on the uses of the distributions in actuarial modeling and the additive framework in assessing claims distributions. Presenters will show how actuaries can better assess risks by focusing on the individual- and group-specific claims distributions as opposed to traditional point-estimate-centric models.

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: Pandemic modeling and its impact on the insurance and reinsurance business have been the subject of scrutiny during the COVID-19 pandemic. As the World Health Organization (WHO) has declared its end as a global health emergency, the focus of insurers and reinsurers turns to the possibility of future pandemics. Key to considering possible future pandemics is assessing what is needed to model pandemic risks. Our presentation will focus on providing you with the following: - The main principles around creating a well-established pandemic risk modeling framework, what they are and how you should build them into your model (As a reinsurer, this is core to our business, and we have unique global insights into performing it.) - A review of pandemic risk modeling strategies as well as an understanding of the inherent uncertainties of pandemic events - Lessons learned from COVID-19 and how we can apply them in assessing the possibility of future pandemics - An understanding of the link between future pandemic risk and the capital requirements