Description: Identifying and submitting all appropriate ICD-9 diagnosis codes to CMS will result in a higher risk score for the member and increased payments to Medicare Advantage plans. Milliman's HCC prevalence study illustrates the significant variations in HCC prevalence rates by age/gender, geography, and population type and will assist plans in benchmarking the effectiveness of their own diagnosis code capture efforts.

Description: This article describes a method that can be useful when re-fitting a risk score model to a population of only moderate size. Learn how to use concepts like ridge regression to train a model with partial credibility.

Description: Analysis of possible mathematical relationships between hospital quality and hospital billed or allowed charges or other measures, using quality data as defined by the U.S. Department of Health and Human Services along with the Hospital Quality Alliance for their Hospital Quality Initiative.

Description: Discussion of the separate and joint effects of obesity and diabetes on disability and mortality, and ways these effects can be used to improved insurance pricing and reserving, reverse the increasing rates of obesity and diabetes, and improve current projections of disability and mortality risks.

Description: This article describes methodologies for detection and deterrence of Medicare fraud. It also describes the validation of PRIDIT for fraud detection in Medicare.

Description: This dissertation by Tia Sawhney will help you understand this issue and start measuring it, or start promoting equitable public policy.

Description: Data requirements for the Society of Actuaries cancer experience study, for those submitting exposure and claim information.

Description: An economic analysis of bariatric surgery and health care costs supports the case for health plans to cover these procedures.

Description: A study of disability experience examines cost burden and degree of disability, and how these measures vary across different subpopulations.

Description: We highlight a new approach leveraging predictive analytics to identify high risk and rising risk opioid users for more effective screening and follow-up care.