Excessive or redundant medical services, medical coding errors, improper billing, as well as outright Fraud, continue to be significant challenges for health insurers. The National Health Care Anti-Fraud Association (NHCAA) estimates that the financial losses due to health care fraud are in the tens of billions of dollars each year. The 2017 National Healthcare Fraud Takedown conducted by the Department of Health and Human Services Office of Inspector General was the largest health care fraud takedown in history with about $1.3 billion in identified false billings to Medicare and Medicaid.
Healthcare fraud is very difficult to detect because a variety of nuanced methods are employed, investigative evidence is often buried in text documents, and there can be collusion among network providers.
A national workers’ compensation insurance provider was interested in using analytics to help reduce provider fraud, waste, and abuse (FWA). The main goal was to identify questionable provider practices and to prioritize work for investigators. Second, it was important to measure the impact of the actions taken to further reduce the losses and promote best practices among providers.
This post first appeared on Elder Research Data Science & Machine Learning Blog, please read the originial post: here