Graphical models for identifying fraud and waste in healthcare claims

Abstract

We describe graphical model based methods for analyzing prescription and medical claims data in order to identify fraud and waste. Our approach draws on ideas from speech recognition and language modeling to identify patients, doctors and pharmacies whose prescription encounters show significant departure from normative behavior. We have analyzed claims data from a large healthcare provider, consisting of over 53 million individual prescription claims in the calendar year 2011.