Medicare Fraud Detection Becoming Possible Through Machine-Learning Algorithms

Researchers from Florida Atlantic University's College of Engineering and Computer Science published a study in Health Information Science and Systems that shows how machine learning and advanced analytics could lead to Medicare fraud detection. The breakthrough could lead to $19-65 billion annual savings of Medicare funds lost to fraud. The researchers tested six different machine learners on both balanced and imbalanced data sets using Medicare Part B data, ultimately finding the RF100 random forest algorithm to be the most effective in detecting potential fraudulent claims, and that imbalanced data sets provided the most accurate results. The research team used four years worth of Medicare Part B data totaling 37 million cases and examined them for potential patient abuse, neglect, and overcharging or charging for services that were never provided. They used the NPI -- National Provider Identifier, which is a unique identification number issued to healthcare providers by the government -- to match fraud labels to the data, checking against provider details, payment and charges, procedure codes, total procedures performed, and medical specialty.