Description: This article provides an overview of behavioral economic theory as it relates to health and welfare insurance plans and provides summaries of two sample applications of the theories: enrollment in health insurance exchanges created by the Patient Protection and Affordable Care Act, and product design considerations in light of vast health illiteracy.

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

Description: This is one of a series of SOA-sponsored papers on the subject of Evaluating the Results of Care Management Interventions. This paper addresses the theory of measurement design and provides a practical evaluation of the most common designs for the practitioner.

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.