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: Results of an SOA survey regarding the expectation for 2015 accruals for 3Rs and cost sharing reduction payments

Description: Discover the key components of value-based care (VBC) contracts and their impact on provider credibility. Learn about benchmarks, measurement period results, and the unique challenges providers face in managing insurance risk. Gain insights into reducing costs and aligning financial incentives in the US healthcare market.

Description: This article provides a brief overview of the current state of machine learning (ML) and artificial intelligence (AI) as they apply to actuarial science, including discussion around the integration of actuarial judgment with ML and AI. It concludes with an example of explainable ML through a python tool known as SHAP (SHapley Additive exPlanations), which is able to explain the key drivers behind individual ML or AI predictions.

Description: Adjusting the 1985 NAIC Cancer Claim Cost Tables, a collection of morbidity tables, for Inflation and benefit changes Issue No. 34.

Description: Realizing the impact of medical errors on the U.S. health care system, the Health Section commissioned a research project to measure the annual frequency of medical errors and their total measurable direct and indirect cost to the economy.

Description: This article takes a comparative review on Medicare and Private Health Insurance and examines the cost of administration. From Health Watch Newsletter, May 2006, Issue No. 52.

Description: This article is a brief overview of what digital health is, how it is regulated, and who some of the big unicorns are.

Description: Article explores why many integrated delivery systems are struggling and what actions they need to take to succeed.

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.