With fraud estimates as high as $272 billion annually across the healthcare industry, there’s always been a good reason for payers to devote significant resources to better detecting and preventing fraud, waste, and abuse. The periodic news stories on large-scale healthcare fraud busts tell an important story—we’re good at spotting the tip of the icebergs, but not so good at seeing “what lies beneath.” It is time now to drive the successes of advanced fraud analytics tools to a higher ROI by building a view of the entire iceberg with a data-focused approach.
Experience demonstrates that the move toward risk-based reimbursement, and the new relationships and incentives it creates, will also create new targets for fraudsters. Spotting the tip of the iceberg comes down to leveraging analytic tools to direct investigatory resources toward claims abnormalities. While we often see the results of these investigations in the news, to make truly meaningful progress in healthcare fraud prevention, we need to focus on how we leverage new technologies that allow us to see the entire iceberg.
Healthcare fraud is very rarely, if ever, the result of a single individual. Organized crime, whether with a capital “O” or lowercase, frequently plays a role, only further demonstrating that a seismic shift in healthcare fraud prevention cannot happen without developing a holistic view.
Fraud prevention comes down to the data
While fraud analytics tools can guide investigatory resources in the right direction, data remains the key to developing this view. For payers, this starts with thinking about fraud analytics tools as one piece of a more comprehensive, data-focused prevention strategy. By focusing on technologies that enable easier data integration and sharing between stakeholders, payers can begin to move away from simply flagging abnormal claims to linking together individuals and funds in ways that uncover fraudulent networks lurking below the surface, as it were.
Stakeholders have staggering volumes of siloed data spread across their enterprises that can be leveraged to augment these efforts. A data-focused approach enhances the utility of fraud algorithms because they are able to pull from more complete datasets, creating less false positives before investigation. A complete data set would include everything from insurance claims to doctors’ notes. Leveraging all available data can increase investigatory accuracy and impact.
Ultimately, real-time prevention strategies can be difficult to achieve without first empowering algorithms to draw on a greater volume of data sources. This approach also benefits from being highly flexible because it can evolve as new data sources become available—and as fraudsters develop new techniques.
Stakeholders should evaluate their outdated and siloed enterprise architectures against the need to integrate and operationalize greater volumes and varieties of data now. With a flexible and cost-effective enterprise architecture in place, stakeholders can use all their data to make sure fraudsters aren’t getting money that should be spent on high-quality health care for those who really need it.