Healthcare Risk Adjustment and Predictive Modeling, 2nd edition, provides a comprehensive guide to healthcare actuaries and other professionals interested in healthcare data analytics, risk adjustment and predictive modeling. The book first introduces the topic with discussions of health risk, available data, clinical identification algorithms for diagnostic grouping and the use of grouper models.

The second part of the book presents the concept of data mining and some of the common approaches used by modelers. The third and final section covers a number of predictive modeling and risk adjustment case-studies, with examples from Medicaid, Medicare Advantage, ACA Exchanges, ACOs disability, depression diagnosis and provider reimbursement, as well as the use of predictive modeling and risk adjustment outside the U.S. For readers who wish to experiment with their own models, the book also provides access to test datasets.

“Across the developed world, there is a growing trend for health systems to focus on the Triple Aim of improving quality, improving patients’ experience of care, while reducing per capita costs. One approach to achieving the Triple Aim is to predict and prevent so-called Triple Fail events (i.e., events that are simultaneously low quality, high cost, and represent a poor patient experience). Now in its second edition, this book is rapidly becoming required reading for all those who are interested in adopting this approach to the Triple Aim.”
- Dr. Geraint Lewis, FRCP, FFPH, Chief Data Office NHS England

"All in all, this is the work of a superbly mathematical yet practical mind. Ian Duncan's work is a brilliant exposition of analytical techniques, coupled with a masterful explanation of how to apply health risk analysis in a series of relevant, real world case studies."
- Regina E. Herzlinger, Nancy R. McPherson Professor Business Administration, Harvard Business School

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