Description: ACA risk adjustment is intended to "bridge the gap" between allowed rates and actuarial rates. If risk adjustment methodology is not bridging that gap & becomes an additional input to pricing formulae, it is not serving its intended purpose.

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: This article is intended to inform readers about the current state of mental health and health care in the US, as were it's heading moving forward.

Description: Examines the question of whether health-care costs, or health insurance costs, are in crisis.

Description: Article discussing the use of enterprise risk management in the health care industry.

Description: The SOA provides information about the actuarial career path in health care and predictive analytics. Throughout 2016, we will share more about actuaries who are tackling some of the industry's biggest challenges and hear from top employers in healthcare analytics.

Description: Countries developing universal health care via an insurance vehicle face a variety of challenges. Deciding which benefits to include, where to proactively manage health, and how to reimburse network providers are just a sampling of the issues governments concurrently manage. Optimal choices are data-driven responses. For example, high rates of cervical cancer or increasing narcotics prescriptions are trends that can be managed through product changes, modified network payment structures, or even employer based outreach. This kind of information is typically gleaned from established health insurance claims data. However, in emerging health insurance markets, this level of granular claims data is largely missing. It is difficult to manage medical trend when the root causes are unclear. This article explores a pilot medical underwriting project in Jordan.