Big Data has a key role to play in keeping a close eye on FWA - which has the “potential for huge impact on people’s health and pocketbooks,” the report said.
“Advanced data analytics that incorporate diverse data sources are a key component of modern approaches, and they give payers a needed edge.”
In a typical scenario, payers hire professionals who build rules and processes around issues that are both known and suspected. However, the report claims that there are issues with this approach.
“The rules change frequently, and physicians do not receive billing training in school, leading to errors. Also, the sophisticated players know that industry is watching and go to great lengths to obscure what they’re doing,” the report says.
“The system’s inherent structure of trust enables both simple billings errors and illicit actors to hide in the shadows of the murky deep as overpayments quietly siphon money away from legitimate care.”
The report goes on to say that fraud results from repeated misrepresentations that create patterns. These are easily detectable compared with claims that are legitimate.
“Advanced big data analytics that digest many different data sources give payers the means to look at benchmark patterns and results, and identify claims and patterns of billing sufficiently different to merit review. The best analytics are also adept at eliminating false positives so provider audit groups and special investigation units can focus their efforts where they are most likely to yield results.”
Traditional analysis has, first and foremost, used claims data, which is very detailed. However, claims data can be inaccurate or incomplete.
“Another way payers can use advanced analytics to uncover FWA is by analysing links between multiple providers. After all, why settle for sinking one enemy ship when you can potentially cripple the whole fleet?” asks the report.
It goes on to exemplify this with the case of provider A who is involved in irregular billing. Other providers with which they are associated may also be involved in improper billing, the report says. “Thus, many payers will work to analyse connected providers. “
Monitoring information on corporate ownership, billing and management companies, social media interactions of doctors and staff can also help reveal if other players are involved in “a broader pattern of referral and collusion.”
Instead of depending on the status quo, advanced analytics can study patterns that deviate from industry standards or the Office of the Inspector General benchmarks. “The key is to build innovative algorithms and data models around known issues, using as many data sources as possible, and train them with known patterns and issues.”
Regardless of how sophisticated advanced analytics are though, FWA detection also needs the skills of experts who can interpret the analytic output. Nurses are vital in this process as they can see what others may overlook in medical records. Former law enforcement officers who understand criminal behaviour can also assist as can claims adjustors that can pinpoint how bills violate Current Procedural Terminology (CPT) and the Healthcare Common Procedure Coding System (HCPCS) codes.
Insights from these experts must go hand-in-hand with the analytics in order to prevent false conclusions.
One more point is important: Prevention is better than cure in FWA analytics as in health. Detection of improper claims is more effective than claims for an erroneous payment. “The more payers can avoid “pay and chase” scenarios the better it will be for all involved.”
In order to keep a step ahead of FWA, the report concludes, payers need to take advantage of all the data and analytics tools available to meet the increasing threat of fraud. “By thinking creatively, mining all available data sources with advanced analytic tools, and involving experts with specialised knowledge who can find the hidden clues, they can significantly reduce the impact of FWA on the healthcare industry.”
Source: RevCycle Intelligence
Image Credit: FHealthlaw
