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: Actuaries historically modeled new launches in pharmaceuticals by anticipating the expected change to the market basket. This became more complicated with the recent launches of gene and cell therapies as they address rare diseases. Rare conditions have low total member volume to study and some do not currently have a diagnosis code to identify them within claims data. Addressing this modeling challenge has been growing more important in the recent years with the number of therapies launching. What has the health care industry, and more specifically health care, done to enhance modeling and how should this be altered for the future? Additionally, historic modeling has sometimes incorporated a level of direct medical treatment cost offsets where studies have shown lower treatment costs are achievable. Can gene and cell financial modeling include cost offsets as well? And if so, what are suggested precautions or limitations to this effort?

Description: Enjoy an in-depth walkthrough of the facets of the lab testing world in this webcast. The agenda will include a discussion on genetic testing, which focuses on its general background, current trends and tests in the pipeline, as well as a focus on the approaches to achieve savings in lab testing. Finally, expect to check out sample actuarial analyses that will be used to identify areas of focus in lab management. Join us and take a deeper look into lab work in the actuarial health industry.

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: 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.

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: How do we take a Minimum Viable Products (MVP) approach to launch new insurance products? Join this moderated discussion and focus on three key development aspects: Distribution, Systems, and the design and pricing of the Insurance product itself. Come discuss reasons to choose an MVP approach and how it solves many of the product launch challenges.

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: Review a recently completed research project for a multi-stakeholder data consortium on social determinants of health (SDOH). In this project, claims, external SDOH data, geospatial analysis, and machine learning models were used to examine several questions: How to identify SDOH vulnerabilities? How sensitive are the findings with respect to provider coding pattern variations? How much variability in cost can be explained by SDOH vs. morbidity? What are the key risk drivers revealed by machine learning models? The session will review the methodology and main findings from this project.

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.