Description: This session will cover the current and potential use of artificial intelligence (AI) in the health care space. Participate in this interactive session and learn how AI is being used today and discuss its potential to reshape and revolutionize health care. You will get an opportunity to learn how AI can influence patient experience in every phase of healthcare delivery and talk about the challenges and limitations that arise with the use of this technology.

Description: Model design is an important part of being an actuary, and the SOA highlights the importance of the actuarial control cycle of model design, evaluation and monitoring. What tools are at our disposal for these steps? When should we be concerned or excited about model performance? Can we, and should we, use machine learning tools to assist with model improvement or development?

Description: Reference Based Pricing (RBP) efforts have increased in popularity as tools to reduce the cost of care. Now with the ability to use CMS health plan transparency information, organizations can estimate the impact of savings from the use of RBP. This session will share the results of an analysis of this in the form of a case study.

Description: Get up to date on the current and potential use of artificial intelligence (AI) in the health care space. In this interactive session, you will learn how AI is being used today and discover its potential to reshape and revolutionize health care. Take advantage of the opportunity to find out how AI can influence patient experience in every phase of health care delivery, and join the conversation about the challenges and limitations that arise with the use of this technology.

Description: As health insurance self-funding becomes more popular among smaller group sizes, risk is evolving for the MGUs, stop loss writers, and reinsurers that cover them. Assessing more small groups at lower attachment points means rethinking traditional stop loss underwriting techniques.

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: "Risk adjustment (RA) is a key component for CMS Medicare Advantage program and California Medicaid Managed Care program. While both RA programs follow generally accepted basic principles at a high level, their underlying methodologies and assumptions are also quite different. Due to recent events such as COVID-19 Public Health Emergency and Medicaid redetermination delay and restart, it adds complexity to Risk Adjustment. This is a hot topic in the industry and has significant impact to health plans. Keeping current with risk adjustment program changes and understanding the commonalities and differences of these two government risk adjustment programs is important to our health actuaries. Compare these two risk adjustment programs so you can learn key elements of a risk adjustment program, similarities and differences in methodology and assumptions , as well as special considerations due to serving different populations."

Description: Much of the work performed by disability insurance actuaries depends on the availability of accurate and detailed data, which is not always as simple as it sounds. Explore a variety of data challenges faced by disability actuaries, along with potential solutions. Potential topics include finding relevant data, the impact of poor data on valuation work, the impact of new privacy rules, and the potential bias in the data used to develop predictive models.