Predictive modeling technology is capable of sifting through massive data sets and uncovering the patterns and trends that correlate the target data elements to some outcome(s) of interest.
Paul Bradley, chief data scientist for ZirMed in Louisville, Ky.: Predictive modeling empowers organizations to ask fundamentally different questions than they can ask with software built of static, manually created rules.
For example, rather than asking, "How many claims exhibit these exact (and already known/defined) characteristics?"
We can instead ask, "What are the properties of a claim or of the information that goes into a claim that makes it more likely the claim will be denied — or not denied? What is the ultimate impact of that denial (and ones like it)?"
The end result is organizations can be smarter about addressing the root-causes of those patterns upstream. One of the most exciting applications of predictive analytics in healthcare is contract modeling and management — healthcare contracts are becoming more and more complex, and as providers take on additional risk, they need technology that can effectively and rapidly model the impact of these new contracts based on myriad financial, clinical, and patient-population data elements.