Healthcare Fraud: Auditing and Detection Guide, 2nd Edition

What Is Data Mining?

The science of extracting information from large data sets or databases is known as data mining. It is a new discipline that has its roots in statistics, machine learning, data management and databases, pattern recognition, and artificial intelligence. This science derives intelligence from data sets and provides opportunities to create predictive modeling.

This concept can be applied in the context of this audit and investigative guidebook. What does data mining look like in the application of the pipeline audit models? First, the healthcare continuum (HCC) model overview begins with the primary healthcare continuum (P-HCC) overview: What players are involved within the data sets? Within the secondary healthcare continuum model (S-HCC), the information sources and their respective infrastructure should be noted. How will damages be measured (consequence healthcare continuum, or C-HCC) in this audit or investigation? This leads into identifying the level of risk with the transparency healthcare continuum (T-HCC). This will often include components of the last HCC model continuum (rules based healthcare, or R-HCC), which is the identification of the rules that are relevant. Second, in applying the health information pipeline (HIP) model, an HIP data mining question may be, “Within the set of clinical records, can we predict the likely patient outcome of a certain treatment regimen?” The accounts receivable pipeline (ARP) financial analysis of the treatment regimen ...