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: Discussion of how to mature value-based contracts between health insurers and provider organizations to improve population health and financial sustainability. Centers around contract parameter consistency, revenue predictability and risk assessment. Intended audience are health plan and ACO actuaries and leaders. Authors are writing from the provider perspective in value-based contracts.

Description: The MACRA/QPP Provider Revenue Model is designed to provide an estimate of the revenue impact of the QPP (MIPS, AAPM) on a provider group or ACO, based on the required inputs and scenarios.

Description: Early in his career, Ehren Nagel, FSA, MAAA, scoured through podcasts and articles about predictive analytics, long suspecting this was a field with potential for him.

Description: Yi-Ling Lin, FSA, MAAA, FCA, started the SOA’s Predictive Analytics Certificate Program to keep pace with the impact big data and predictive modeling is having on the health care industry

Description: Discussion and Opinion on how randomness affects measurement of provider contracting risk-sharing agreements

Description: This article details the medical stop-loss coverages purchased by HMOs, small insurers and providers taking delegated financial risk in payor agreements. Along the way we will identify strategies purchasers often use to save money on medical stop-loss.

Description: In the evolving realm of healthcare actuarial practice, value-based contracts are reshaping the landscape. This article explores the pivotal role of AI-enhanced Electronic Medical Records (EMRs) in empowering actuaries to navigate this dynamic sector. Despite challenges in data standardization, AI-enhanced EMRs offer a solution by extracting and standardizing diverse medical records. The article delineates how AI-EMR integration benefits actuaries and provider organizations, enabling more accurate forecasting, earlier shared savings prepayments, and real-time quality metric aggregation. Moreover, it discusses how this fusion addresses historical data limitations, can help project catastrophic claims, and can help identify risk score capture opportunities. Ultimately, it indicates the transformative potential of AI-EMR synergy, positioning actuaries at the forefront of healthcare analytics with unprecedented precision and insight.

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: Introducing Random Effects into Risk Score Models