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: The Society of Actuaries Research Institute developed a new study to explore pharmacy financing. The goals of the SOA related to this research are to identify opportunities to increase transparency, encourage competition, align stakeholder incentives and mitigate increases in total cost of care. The research uses a value-based methodology.

Description: On average, contracts receiving their first star rating receive 3.20 stars, compared to existing or established contracts receiving 3.90 stars on average. This 0.70 disparity in quality ratings creates significant hardship on new contracts when this lower star rating decreases their payments in the following calendar year. This article explores the disparity in Medicare Advantage Star Ratings between contracts receiving their first star ratings (“New” contracts) and existing contracts, including: 1. Understanding what happens to Medicare Advantage Payments when contracts receive lower star ratings? – What is the Star Rating cliff and why does it matter to new contracts? 2. Explaining when a contract receives their first star rating and how much enrollment this requires – When does the Star Rating cliff occur? 3. Noting which specific star measures “New” contracts struggle with, including those contracts under large parent organizations, contracts with rapid growth, and all other new contracts. – How can my contract avoid the Star Rating cliff?

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: 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.