Description: This article explores a potential alternative to the more typical regression-based specification of risk adjustment models. We demonstrate a proof of concept that a nearest-neighbors approach may hold some promise in certain applications of health-based risk adjustment.

Description: Discusses the factors one should take into account when considering the appropriate level of precision for a task or a subsequent communication.

Description: This report presents the results of a study comparing health risk scoring models.

Description: View the following report regarding Risk Scoring in Health Insurance: A Primer.

Description: In health insurance marketplaces, prospective insured individuals and groups are able to choose from a range of available plans. This study uses health care encounter data to simulate the choices faced by health insurance shoppers under a variety of conditions and measures the simulated effect of increased consumer choice and consumer information on medical loss ratios and, as a consequence, insurer profitability.

Description: This article compares three potential approaches to determining new coefficients for health-based risk scoring models: full recalibration, p-value credibility weighting, and penalized ridge regression.

Description: Description of the design and purpose of the military health system and Veterans Health Administration's programs. Covers the types of benefits offered and how these programs differ from typical commercial health systems.

Description: workbook to accompany Modeling Effects of Enrollee Choice

Description: A discussion on podcasts relative to the Predictive Analytics & Futurism Section and how they can generate CE credits.

Description: One of the Health Section's key focuses in 2019 is Initiative 18/11. Last year the SOA joined forces with the Kaiser Family Foundation (KFF) and the Healthcare Financial Management Association (HFMA) to answer the nagging question: “What can we do about the cost of healthcare?” A steering group has been formed to lead a research project as to what managed care companies can do to reduce costs without sacrificing quality. The project [called Managed Care 3.0 in the 18/11 paper] will examine historical models that have worked as well as investigate the impact that new technologies and care models may have on cost and quality, with the goal of better understanding the current environment and identify­ing potential risks and opportunities.