Description: Identifying and submitting all appropriate ICD-9 diagnosis codes to CMS will result in a higher risk score for the member and increased payments to Medicare Advantage plans. Milliman's HCC prevalence study illustrates the significant variations in HCC prevalence rates by age/gender, geography, and population type and will assist plans in benchmarking the effectiveness of their own diagnosis code capture efforts.

Description: Explores the open vs. closed systems debate with regards to actuarial modeling and exposes the over-simplification. Valuable for anyone who uses models.

Description: Although rate structures where low-cost cohorts subsidize high cost cohorts may be justified from a actuarial, marketing, or legal standpoint, the subsidization creates risks which must be understood and monitored. This article presents the concept of subsidization signature to measure and interpret the effects of rate subsidization, and to assess the sustainability of rating structures and the sensitivity of business mix assumptions.

Description: A new health insurance pricing model takes external market prices into account, as well as regulatory rate restrictions and other traditional pricing factors. Rather than being assumed at the outset, lapse rates emerge from the model, along with assessment spiral & loss ratio projections. Under the model optimal pricing strategy depends on whether the class being rated has a healthier than average mix of risks.

Description: This chapter focuses on creating practical models that aid in the prediction of the risk of readmission to a specific hospital within 30 days of discharge for Medicare patients, to aid in the identification and stratification of at-risk patients for hospitals considering building an intervention program in a cost-effective manner. For readers who wish to develop their own predictive model, a dataset is provided on the Actexmadriver.com website, and R code is provided in the appendix to this chapter.

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: This paper examines a differential equation model, whose solutions have yield curve shapes. The paper also examines the relevance of the model with historical monthly U. S. Treasury nominal rates. From the Actuarial Research Clearing House 1992 Vol. 1 .

Description: Discussion of a newly released LTC experience study, including description of data and its increased scope compared to the prior report, reporting specifications, and findings.

Description: The CSTEP 'Cluster Sampling for Tail Estimation of Probability' system is a desktop-based application to assist actuaries in efficient stochastic modeling through the implementation of Representative Scenario approaches. Its goal is to provide actuaries with an open-source HPC 'High Performance Computation' research platform to select a sample set of representative 'pivot' risk scenarios through user-specified or user-researched distance definitions.

Description: The conventional way to analyze grouped observations on a single variable is to construct histograms or, sometimes, fit a normal distribution. We extend this latter approach by allowing for transformations to near normality.