Prior to the ability to electronically process claim submissions, the only choice payers had for fraud, waste and abuse detection were manual auditing processes. This is very timely and costly to a company, and is susceptible to human error. Even now, many payers are still relying on this. However, this is no longer necessary with automation able to audit 100% of your claims without wasting precious time and money.
Examples of Fraud and Abuse
With payers processing thousands of claims every day, it can be very difficult to effectively detect fraud and abuse. A common fraud scheme is when a provider will purposely bill for services that are at a higher level of complexity than the actual service provided. This can be a major sign of fraud if it’s being done consistently. An analysis of the volume of claims coded with a higher reimbursement code vs. the history of provider claims can help identify a pattern.
An additional example of commonly seen fraud is an amount of office visits that looks to be unusually high. Does it make sense that this single provider is seeing double and triple the number of patients per day compared to their colleagues in the same city and specialty? Identifying these patterns can help establish a basis to investigate for fraud.
The key to provider pattern analysis is an understanding of procedures that commonly attract FWA, how these specific procedures are translated into a claim, as well as looking for any patterns that have appeared throughout the history of their claims. If you’re struggling to do this yourself, WhiteHatAI can provide you with the solutions necessary to achieve appropriate analysis.