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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: 4, Insightful) by aclarke on Tuesday November 06 2018, @01:07PM
Nobody cares about the kinds of fraud you've mentioned in your last couple paragraphs, as they're fraud committed against the patient. The only kind of fraud Medicare cares about, and by extension the government, industry, and elected officials, is fraud against Medicare.
The point at the end of the summary about a single-payer system is correct in that this should indicate that there's something more fundamentally wrong with the American medical system than fixing Medicare fraud. Certainly with Every.Single.Other.Developed.Country.In.The.World having figured it out (almost not an exaggeration) they don't really have to look very hard or far to come up with better ideas if they want them.