Machine learning can be a useful tool in detecting Medicare fraud, according to a new study that can recover anywhere from $ 19 billion to $ 65 billion lost in fraud each year.

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How Machine Learning Can
Detect Medicare Fraud
Machine learning could become a new weapon in the fight against Medicare
fraud.
Machine learning can be a useful tool in detecting Medicare fraud, according
to a new study that can recover anywhere from $ 19 billion to $ 65 billion
lost in fraud each year.
Researchers at Florida Atlantic University’s College of Engineering and
Computer Science recently published the world’s first study using Medicare
Big data, machine learning, and advanced analytics to automate fraud
detection. They tested six different machine learners on balanced and
unbalanced data sets and eventually found that the RF100 Random Forest
algorithm would be most effective in detecting potential cases of fraud. They
found that unbalanced data sets are more than balanced data sets when
scanning for fraud.
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T
here are many implications in determining what fraud is and what is not,
such as clerical error,” says Richard A. Bowder, senior author and Ph.D.
“Our goal is to allow machine learners to know all this
data and flag anything suspicious. Then we can alert
researchers and auditors, who should focus on 50 cases
instead of 500 cases or more.”
In the study, Bowder and colleagues examined Medicare data, covering 37
million cases from 2012 to 2015, for incidents such as patient abuse, neglect,
and billing for medical services. The team has reduced the data set to 3.7
million cases, which is still a challenge for human researchers charged with
pinpointing Medicare fraud.
The authors used the National Provider Identifier — a government-issued ID
number for health care providers to compare fraud labels with Medicare Part
data, which includes provider details, payment and charge information,
policy codes, all policies, and medical specifications.
When researchers compared NPI with Medicare data, they flagged
fraudulent providers in a separate database.
“If we can accurately assess the physician’s uniqueness based on our
statistical analyses, then we can detect exceptional physician behaviors and
flag as much fraud as possible for further investigation,” said Tagi M.
Khoshgofthar, Ph.D., co-author, and professor at the school.
So, if a cardiologist is wrongly labeled a neurologist, it is a sign of
deception.
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However, the data set remains a challenge. A small number of fraudulent
providers and a large number of onboard providers have made data
imbalance that can fool machine practitioners. So, using random
undersampling, the researchers reduced the set to 12,000 cases, with seven
class distributions ranging from severe to unbalanced.
From there, they unleashed their learners and reached their results with
respect to the random forest and class distribution.
Surprisingly, the researchers found that keeping the data 90 percent simple
and 10 percent fraudulent is a “sweet spot” for machine learning algorithms
that work to detect Medicare frauds. They felt this proportion needed to
include more fraudulent providers in order for learners to be effective.
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