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: 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: 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: A closer look at family tier factor rating assumptions, and how well they reflect actual claim cost data.

Description: We highlight a new approach leveraging predictive analytics to identify high risk and rising risk opioid users for more effective screening and follow-up care.

Description: Review of a research project, co-sponsored by the SOA, which examined 83 organizations and over 150 programs that measure the quality and efficiency of hospitals and physicians.

Description: Profile of an actuary in a non-traditional actuarial role: focusing on medical cost and analysis, both retrospective and prospective.