According to the U.S. General Accounting Office, health care fraud -- estimated at $95 billion in 1995 -- accounts for 10 percent of the nation's total health care spending. For example, some experts say as much as 30 percent of all Workers' Compensation claims are fraudulent.

John Sharp, the Texas comptroller, recently issued a report entitled Disturbing the Peace, which deals with ways of improving state services and controlling costs. According to the report, while the vast majority of Medicaid providers are honest and ethical, even a small amount of fraud in the $10 billion program -- 25 percent of the entire state's budget -- could cost millions.

Texas State Comptroller John Sharp and

ITC president Joe Brown discuss

Texas' Medicaid Fraud Detection System.

One of the report's key recommendations is adapting neural-network technology -- successful in spotting credit card fraud -- to help state investigators identify suspect claims. These recommendations were based in part on a fraud detection pilot in the Comptroller's Office.

"Most fraud detection systems depend on someone dropping a dime," said Guy Denney, director of systems engineering for Intelligent Technologies Corporation (ITC), primary contractor for the pilot. "Using neural-net technology, for the first time Texas has a good way to go off and identify providers electronically."

ITC's neural-net software was used in some of the target recognition technology in Desert Storm.

"We have been developing these systems since about 1983," said ITC President Joe Brown. "We built smart bombs and land mine detectors. We're using the same pattern recognition technology, but now, instead of finding a tank on a desert, we're trying to find fraud."

Rounding Up Fraud in Texas

According to the comptroller's report, a baseline study of the neural-net technology showed good results. Possible targets for investigation were generated from an existing fraud identification system and the neural-net-pilot program. Both sets of targets were passed to investigators to determine which ones warranted follow-up. Of the leads produced by the standard system, 14 percent were deemed worthy of follow-up; of those produced by the neural-net pilot, 39 percent are now being investigated.

The report identified three elements that contribute to the success of the neural-net-based system. "First, it can look at many Medicaid claim details simultaneously to identify subtleties in the data that are not evident when looking at one piece of information at a time. Second, it can combine and analyze data from different databases and apply that information to the problem. Lastly, and perhaps most importantly, it generalizes from its previous 'learning experience' so that it can identify new schemes as they appear."

The pilot system developed for Texas had access to four different state databases populating an Oracle data warehouse. The neural net then ran against the information in the data warehouse. Because detail data is available online through the data warehouse, investigators can also use the system -- drilling down to details that raise the red flag on a particular claim or set of claims.

A bill making its way through the Texas Legislature would implement the recommendations contained in the comptroller's report, including mandating the use of neural-net technology to improve fraud detection. Once passed, the success encountered during the pilot would move forward into a production system.

Utah to Snare Scofflaws

Utah is engaged in a similar effort to adapt neural net and modeling technology to fraud detection, beginning with the Workers' Compensation program. A claimant fraud component will flag possible medical claim fraud. The employer component will look for employers who have misclassified workers to get a break on Workers' Compensation rates. Another component will look for provider fraud -- when a doctor bills for services not rendered, or over-bills by performing too many services or artificially increasing the frequency of service.

David Aden  | 
David Aden is a writer from Washington, D.C.