Description: Python has become the fastest-growing major programming language in the world. It has surged in popularity over the last decade, following the development of its industry-leading capabilities in data science, machine learning and artificial intelligence, and it is now one of the most globally popular and widely used languages. In this article, I discuss some of the reasons for its popularity and explain why many actuaries and insurers have started using Python.

Description: CMS provides ACOs with monthly claims data for members, including their medical visits and ACO program eligibility. This data can be divided into Membership and Claims tables through the ETL process. However, when calculating PMPY at specific levels of detail using these tables, issues arise due to unrelated dimensions, leading to inaccurate and understated results. This article presents a solution to this problem by utilizing Tableau's data blending capabilities.

Description: This article describes Medicare Supplement and short-term care products in more detail, and we compare their risk profiles. We explore the concept that these two products—when offered together—may provide a complementary hedge of certain policyholder behavior risks.

Description: Presents a step-by-step methodology for calculating confidence intervals for IBNR estimates derived using the Averaqe PMPM method. It is a companion article to the January 2023 Health Watch article presenting the Average PMPM method for generating point estimates of IBNR with zero claims runout.

Description: This article attempts to make a modest contribution to the dialogue on the potential and risks associated with novel treatments such as cell & gene therapies. With a fast-expanding roster of what are believed to be one-time treatments at ever increasing price points aimed at curing a wider swatch of the population, they are as much a source of relief for patients stuck in medical limbo, as they are a source of anxiety for payers.

Description: Regression to the mean is a phenomenon that arises when comparing sequential data points. It states that when an initial observation is extreme or an outlier, future observations will be closer to the average. Health care actuaries need to understand and be able to mitigate the effects of regression to the mean to avoid misapplying predictive models or misstating measurements of historical effects.

Description: The article documents the efficacy of infra-low frequency neurofeedback regarding how it improves patient no show rates, decreases mental health symptoms by 40% or more, and self-harm and suicidal ideations.

Description: As actuaries in health care, we spend most of our time focused on reviewing claims, enrollment and clinical data. That information is captured while patients are in health care facilities. Yet most people spend only a small fraction of their time in these facilities. This article explores what types of data are available to actuaries the provide a more holistic view of the member so that a members physical and emotional needs, the social determinants of health can be taken into consideration along with their clinical needs. Two uses cases are presented so actuaries can see how these types of analyses can be leveraged to improve clinical, operational, and financial performance of risk bearing health entities. Understanding the customer (member) not just the patient enables actuaries to provide new insights and values to our stakeholders across a wide range of business and risk applications.

Description: Read the November 14 2019 issue of Health Watch, published by the Health Section. Practical information for actuaries, articles impact sections, section activities.

Description: Article reviews the data a year into the unwinding of continuous coverage provisions in Medicaid, the impact on enrollment counts, how capitation rates have been adjusted, and compares those results to what has been modeled with the SOA Mediciad Unwinding model.