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: You will be reminded how to spot and avoid statistical biases and fallacies that are common to health care actuarial data and problems. We plan to explain various statistical biases and fallacies and for each show one or two examples of how they might pop up in healthcare data and what strategies can be used to avoid/account for them. Likely topics include simpson's paradox, regression to the mean, base rate fallacy, sampling bias, survivor bias, Berkson's fallacy, and others.

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: 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: The presenters give a history lesson on the origin of CMS Medicare star ratings, including why this demonstration was originally created and how the program has worked over the years. The discussion will cover both the evolution of the underlying measures and the financial impact to Medicare Advantage stakeholders. By attending the session, you will have a stronger understanding of how member experience and outcome-based measures can be factored into the funding within a health care system to better align incentives among various stakeholders.