Predictive Analytics to Detect Missed Charges in a Hospital System

Hospitals are complex environments with numerous charges for a multitude of small items, all administered by humans, so in the industry it is just about considered a given that some charges will slip through the cracks.  Karen Minich-Pourshadi recently wrote a very interesting article about a hospital in Washington which was able to pick up one million dollars of revenue just in the first 90 days of implementing a predictive analytics system to detect which hospital bills were most likely to have been undercharged – charge recovery. There were two benefits to it, the transactions were detected and corrected prior to billing, and it highlighted the areas and doctors most at risk of under-charging so that they could focus on improving in the future.

“For instance, the system flagged specific diagnosis which usually have lab tests associated with them if the lab test codes were missing. In doing so, they were able to capture all the charges associated with a diagnosis and then alert clinicians to be aware of their mistakes.”

This is a great example of searching for patterns in the screeds of data residing in an organization, to deliver massive value. This example of course doesn’t only apply to healthcare, but to any complex billing system that is handled by humans. Conversely you  can imagine that this same approach is very useful for those who are paying the hospital bills (i.e. insurance companies) – those parties of course are more interested in identifying over-billing rather than under-billing.

 At 11Ants Analytics we have recently done some interesting work, the specific application is unfortunately confidential by the customers request, but it is in the space of combing millions of transactions, looking for anomalies which yield up opportunities for major savings.

The advantages of taking a predictive analytics approach of learning from the patterns in the data, as opposed to a rules-based approach, is that a rules based approach requires knowing every potential problem area before starting, while with a predictive analytics approach, there are no assumptions going into it, and the patterns are ‘learned’ whatever they may be. The other benefit is that deployment of the solution is usually much faster, both on the development side and the implementation side as it is used every month.