Healthcare Fraud: Auditing and Detection Guide, 2nd Edition

Chapter 20

Data Analysis Models

I’m a great believer in luck, and I find the harder I work, the more I have of it.

–Thomas Jefferson (1743–1826)

The harder you work on the front end of your analysis, the luckier you will be in materializing information that will generate productive results. In healthcare, this blend of luck is generated from well-organized and structured audits and investigations. This chapter applies a sample series of data analysis models. These models are the results of output activity from data mining. In this book, we have discussed the primary healthcare continuum (P-HCC), secondary healthcare continuum (S-HCC), information healthcare continuum (I-HCC), consequence healthcare continuum (C-HCC), the transparency healthcare continuum (T-HCC), and the rules based healthcare continuum (R-HCC). Please note, this chapter will illustrate the use of the anomaly models by applying the attributes of the P-HCC. In each example, issues with benchmarks, information systems, consequences, transparency issues and relevant rules are inherent in each application. In the data output world, we have a new continuum to monitor, understand, and use as a guide in the audit and investigation of healthcare fraud. This is referred to as the anomaly continuum. View these models as potential frameworks for building data warehouses of information for analysis.