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: 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: Similar to how states had made adjustments to their capitation rates to reflect acuity adjustments caused by the onset of the pandemic, states will need to modify the rates to reflect the unwinding since utilization patterns and average acuity per enrolled member will change. When the continuous coverage requirements set forth by the COVID-19 Public Health Emergency (PHE) ended, Medicaid-managed care programs across the country adopted various approaches to phasing out maintenance of effort (MOE) as a result of changes to the PHE-era continuous eligibility requirements. Phasing out the MOE requirement will ultimately result in members who no longer meet eligibility requirements losing their Medicaid coverage. Further, states are facing a wide variety of circumstances and are implementing diverse approaches to the MOE phase-out. This topic will be of significant interest to all actuaries and stakeholders of the capitation rate development process for Medicaid-managed care programs. Join us as we discuss the operationalization of the PHE unwind while referencing the SOA's research project which models the effects of the unwinding on Medicaid eligibility and population costs. You will learn about the various approaches that different states have adopted to manage the unwinding process, which will be instrumental for accurately pricing managed care rates.

Description: Health care has changed rapidly over the past couple of years. The pandemic has shifted consumer behavior in how and where they receive health care as well as consumer attitudes toward health care. Payers continue to design innovative solutions in responses to their patient’s needs and expectations. These changes in consumer behaviors and the responses by payers are expected to impact both health care cost and utilization. Discuss changes in health care that have emerged due to the pandemic and how they are expected to influence actuarial assumptions. Explore epidemiological models commonly used in the industry, the considerations when using these models, as well as the challenges with regards to gathering and interpreting relevant experience data. By attending this session, you will be able to: Identify shifts in consumer behavior that have emerged from the pandemic and describe how payers are responding to new patient expectations. Discuss how these post-pandemic changes by consumers and payers influence medical spending. Determine which actuarial assumptions are impacted and how and if the impact is expected to be temporary or permanent. Discuss considerations taken by actuaries and challenges faced when modelling actuarial assumptions post pandemic.

Description: With the mandated health plan transparency files, health plans and providers are now able to substantially enhance their provider contracting efforts. Although the files are large and cumbersome they are market disruptors and provide new insights for provider contracting efforts. Explore a demonstration on the type of information now available and how it can be used to enhance this from both a health plan and health care provider perspective.

Description: Can you answer the question, 'What are the chances we will lose more than $1 million?' without saying, 'It depends'? If you miss a projection, can you estimate the chances that the miss was due to random variation versus an incorrect projection? Listen in as presenters demonstrate how to answer these questions and others using total risk analysis. Total risk analysis includes quantifying both the random variation risk and the projection risk associated with actuarial functions like pricing and reserving. After attending this session, you will be able to describe total risk analysis and to apply to many health actuarial functions.

Description: Watch as presenters highlight the SOA's work on an updated Medicaid underwriting margin model. This research project includes the model along with a set of instructions and a paper describing the key components of underwriting margin, including cost of capital and risk margin. The model was created as a tool for Medicaid actuaries representing both MCO and state principals to determine appropriate underwriting margin components within Medicaid managed care rates. In addition, explore a brief description of the motivation for an underlying theory behind the model. Walk through different model inputs and run through some realistic scenarios that Medicaid actuaries may encounter in states where they operate.

Description: Level funded products often target the small group market (2-50) and can result in many manual and tedious processes for underwriting to perform risk assessment. Learn how a carrier used technology to help automate risk assessment without the need for paper medical questionnaires, and exposed a risk engine for broker quoting to enable near real-time of pricing for risk-based products. The approach reduced labor and allowed underwriting to focus solely on fallout cases while also improving pricing consistency. Discuss broader trends in the health market for risk assessment, including new machine learning cost predictions that can be used for automation and better predictions of future costs. Discussion topics will include underwriting guidelines, business rules, risk scores, and machine learning. By attending this session, you will have a better idea of new trends in risk assessment for small groups where no claims experience is available. And, better understand business rules that are common in underwriting for flags for review as well as how risk scores and machine learning can be used during the underwriting process.

Description: "A crucial importance in forecasting is to be able to distinguish cases when temporary slowdowns in health care utilization are followed by a “rebound” period of higher utilization. Come explore an analysis of a new mathematical model describing the return-of-care process. By means of simulation and case studies using the R programming language, the relationship between the visibility of these effects and the endogenous variance of the utilization time series is explored. After attending this session, you will be able to: Describe the parameters and mathematical form of the model Apply the model to real-world utilization data"