Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence (AI) based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. It is used in many different types of technology and businesses, including healthcare. Healthcare Analytics News brought this interesting topic to us in their article, “How Machine Learning Could Detect Medicare Fraud.”
A recent study indicates that machine learning could become a useful tool in helping to detect Medicare fraud. It has the potential to reclaim 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. After 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.
Fraud is prevalent, especially in healthcare, and new technologies are always being sought to fight that battle.
Melody K. Smith
Sponsored by Data Harmony, a unit of Access Innovations, the world leader in indexing and making content findable.
