Description: The session begins with a brief introduction to Gen AI, including “what it is” and how to optimize outputs.Then, we will to provide some insight into what we are seeing within the Actuarial space – this would include the extent to which actuaries are using Gen AI on a day-to-day basis, how they are going about using it, and some predictions as to where this will go. Finally, we will dive into specific actuarial use cases – we have a few bespoke built actuarial demonstrations to show and will supplement this with a discussion on the broader spectrum of actuarial use cases. We plan for this to be the bulk of the session.

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: Actuarial projects have become more intricate, involving external resources and requests beyond the Actuarial team’s purview. Whether a task appears minor or exceptionally complex, adept project and change management play a pivotal role in successful execution. In this session, we explore practical strategies for tailoring implementation management approaches. Our presenters draw from both corporate and consulting experiences, sharing valuable lessons. Additionally, they highlight effective collaboration with the Project Management Office, model developers, and model users

Description: Session 7A: Integrated Analytics for GLP-1 Therapy Management: Optimizing Clinical and Financial Value

Description: Discuss various analytics and use cases using payer published reimbursement contract data under federal mandate of Transparency in Coverage. These contract rates are publicly available in machine readable format (MRF) starting July 1, 2022, and refreshed monthly. By attending the session, you will have a better understanding of the state-of-art development and analytics using MRF data, including the technologies used to extract and analyze huge MRF files, validation and limitations of published rates, and their use cases.

Description: "Discuss research conducted at the Cleveland Clinic to understand levels of anxiety and depression across Northeast Ohio. Specifically, learn how advanced analytical techniques were utilized with social media data from Twitter and Google to estimate the need for psychological support by geography and overtime. The panel will go further to demonstrate how natural language processing (NLP) and machine learning could be used to make operational decisions at a leading health care provider."

Description: The NAIC continues to move ahead with the GOES (generator of economic scenarios) project for the transition to a new real-world economic scenario generator (ESG) prescribed for principle-based reserve and capital calculations. This session will provide a detailed project update from the regulator and practitioner perspective. An overview of model details and methods that have been decided, what remains to be determined, and the projected timeline for implementation will be discussed. Recent developments such as the industry field test and NAIC model office testing to assess the potential impact on reserves and capital will be discussed as will a review of the implementation of the stochastic exclusion ratio test (SERT). We will also discuss the components of the ESG model and the associated scenario sets the NAIC has exposed with respect to target and acceptance criteria. At the conclusion of this session, attendees will understand the current status of the NAIC GOES project and its projected timeline and will be able to describe the components of the model and how the model has been calibrated and validated. Attendees will also appreciate the remaining challenges in bringing the model to full implementation.

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 6D: Breaking Barriers - A Multidimensional Approach to Behavioral Healthcare Access

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."