Technology How Machine Learning Could Detect Medicare Fraud

Discussion in 'Headline News' started by tom_mai78101, Nov 5, 2018.

  1. tom_mai78101

    tom_mai78101 The Helper Connoisseur / Ex-MineCraft Host Staff Member

    Ratings:
    +955 / 4 / -1
    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.”

    In the study, Bauder and colleagues examined Medicare Part B data from 2012 to 2015, which held 37 million cases, for instances such as patient abuse, neglect and billing for medical services that never occurred. The team narrowed the data set to 3.7 million cases, a number that would still represent a challenge for human investigators who are typically charged with pinpointing Medicare fraud.


    Read more here. (Healthcare Analytics News)
     

Share This Page