The CMS does have a Health Care Fraud and Abuse Control (HCFAC) Program in place, but its efficacy is low, with a fraud recovery rate of only 4% or less. The Fraud Prevention System (FPS) used by CMS has been in place since 2011 and may need a technology refresh to improve its predictive fraud analytics. The article suggests that a modern cloud-based fraud prevention system that can aggregate every claim into a single data lake and analyze it in real time using machine learning, anomaly detection, and artificial intelligence is needed. This would allow fraudulent claims to be detected and investigated before any payment is made to the provider.
Key takeaways:
- Medicare fraud in the U.S. is a significant problem, costing taxpayers more than $100 billion a year, according to estimates from the National Health Care Anti-Fraud Association.
- The Government Accounting Office recognizes the risk of Medicare fraud due to its size, complexity, and susceptibility to mismanagement and improper payments.
- The Centers for Medicare and Medicaid Services (CMS) needs an automated systemwide fraud detection methodology that will scrutinize every claim and alert investigators to the possibility of abuses.
- Modern cloud-based fraud prevention systems that can aggregate every claim into a single data lake, normalize and correlate the data, and analyze it in real time using machine learning, anomaly detection and artificial intelligence could help detect and prevent fraud.