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from the Robert's-your-mother's-brother dept.
https://www.hcanews.com/news/how-machine-learning-could-detect-medicare-fraud
Machine learning could become a useful tool in helping to detect Medicare fraud, according to a new study, potentially reclaiming anywhere from $19 billion to $65 billion lost to fraud each year.
Researchers from Florida Atlantic University's College of Engineering and Computer Science recently published the world's first study using Medicare Part B data, machine learning and advanced analytics to automate fraud detection. They tested six different machine learners on balanced and imbalanced data sets, ultimately finding the RF100 random forest algorithm to be most effective at identifying possible instances of fraud. They also found that imbalanced data sets are more preferable than balanced data sets when scanning for fraud.
"There are so many intricacies involved in determining what is fraud and what is not fraud, such as clerical error," Richard A. Bauder, senior author and a Ph.D. student at the school, said. "Our goal is to enable machine learners to cull through all of this data and flag anything suspicious. Then we can alert investigators and auditors, who will only have to focus on 50 cases instead of 500 cases or more."
[...] "If we can predict a physician's specialty accurately based on our statistical analyses, then we could potentially find unusual physician behaviors and flag these as possible fraud for further investigation," Taghi M. Khoshgoftaar, Ph.D., co-author and a professor at the school, said.
So, if a cardiologist were incorrectly labeled a neurologist, that could be a sign of fraud.
Still, the data set itself remained a challenge. The small number of fraudulent providers and the large number of above-board providers made the data set imbalanced, which can fool machine learners. So, using random undersampling, investigators whittled down the set to 12,000 cases, with seven class distributions ranging from severely imbalanced to balanced.
[...] Surprisingly, researchers found that keeping the data set 90 percent normal and 10 percent fraudulent was the "sweet spot" for machine-learning algorithms tasked with identifying Medicare fraud. They thought the ratio would need to include more fraudulent providers for the learners to be effective.
Actually a compelling argument for single payer.
(Score: 3, Interesting) by Anonymous Coward on Tuesday November 06 2018, @02:40PM
Guess they're screwed then, since healthcare fraud is the rule rather the exception. All you have to look at to know that, is the only "affordable" plans have a 50% copay on a service that is marked up 1000%. Or did you really think the ER charging you $200 per stitch to do what any seamstress or sailor can do, is reasonable?
Recently I went to a new doctor. I gave them an incorrect middle initial on purpose. The reason wasn't to commit fraud, it was to see if they were selling my medical data. Sure enough, got my first junk mail with the matching incorrect middle initial the other day. So mutch for patients bill of rights.