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: Many states have recently introduced mandatory paid family and medical leave (PFML) programs, which require employers to provide PFML benefits to their employees. This session explores recent trends in PFML programs, including plan design, pricing, employee perception, employer challenges, and current activity in various states.

Description: The purpose of this analysis is to provide a practical set of changes to the incidence rate modifiers of the 2013 Individual Disability Income Valuation Table to reflect recent industry individual disability income (IDI) claim experience trends from 2005 through 2015. The goal is to have resulting 2005-2015 experience-based incidence modifiers in a workable format to facilitate its use.

Description: This tool created graphs of Google's mobility Data. This version of the tool contains state-level U.S. data through July 31, 2020

Description: This SOA report describes the components of margin for calculating capitation rates in a Medicaid context along with practical issues that MCOs may face.

Description: This is an article that highlights recommended reading by the Health Practice area communications committee. The readings cover a broad subject matter from risk assessment, medical technology to uninsured.

Description: The Centers for Medicare and Medicaid Services (CMS) recently released the final 2020 Actuarial Value Calculator and Actuarial Value Calculator Methodology

Description: In this video Mitchell Momanyi, FSA MAAA presents an informative breakdown of Medicare Advantage plans, delving into the intricacies of this alternative to traditional Medicare. Covering the inception of Medicare Advantage and its evolution, Momanyi explores the managed care companies offering Part C coverage, emphasizing the role of provider networks coordinated care. Concluding with key considerations for beneficiaries, Momanyi stresses the importance of weighing the pros and cons between Traditional Medicare and Medicare Advantage, while also emphasizing the evolving nature of the Medicare landscape and the regulatory nuances influencing enrollment decisions. Conributors: Mitchell Momanyi, FSA, MAAA, Deana Bell FSA, MAAA, Matt Emery, FSA, CERA, MAAA, Jon Forster, ASA, MAAA

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.