Remember when you were growing up and your parents cautioned against getting in with the wrong crowd? It’s the old “one bad apple will spoil the bunch” philosophy. The goal was to avoid bad behaviors and the trouble that follows. How does this translate into healthcare? It comes into play in the area of fraud risk.
How does this translate into healthcare? With millions of consumers enrolling for coverage every year, and provider networks growing, it’s difficult for health plans to identify those that would fall into the “wrong crowd” category. Health insurers are consumed with the task of managing claims, compliance, payments, providers and patients while trying to provide the best customer experience and stay relevant in a competitive market. Identifying the bad actors engaging with their health plans can be a daunting task.
The good news is, we have some pointers to help health plans navigate the scene.
- It’s important to get a complete view of all individuals interacting with a health plan and the corresponding fraud risk they bring. Not just as a standalone person, but as part of a network. That fraud risk potentially includes relatives, associates, businesses, providers and pharmacists.
- Bad people stick together with hidden patterns of information sharing, suspicious behavior and interactions with potentially fraudulent clusters, such as:
- Patient relationships with known perpetrators of healthcare fraud
- Links among recipients, businesses, assets, relatives and associates
- Relationships between licensed and non-licensed providers
- Inappropriate relationships among patients, providers, employees, suppliers and partners
- You can do more with less. Try arming your SIUs with better tools and intelligence as opposed to adding manpower. You could greatly increase your ability to detect fraud, cost inefficiencies and missed recoveries.
So how does this work in the real world? I’m glad you asked. Here’s an example of a LexisNexis client that used LexisNexis® Relationship Mapping. Its data resources and social network analytics identify links and relationships that could suggest fraudulent activity and fraudulent groups.
Working with our organization, our customer, whom we will call “Health plan X” was able to:
- Broaden its focus and see beyond each individual provider into the connections that resulted in more compelling cases.
- Speed up its investigations using a simple search feature that allowed them to see the full picture or risk that an entity or group of entities represent.
- Spend less time doing research and put more resources into pursuing leads that offer the greatest potential return.
One of their many significant findings working closely with LexisNexis was a provider who had a revoked medical license yet was the prescribing provider for 394 paid prescriptions totaling over $250K!
We wrote a case study providing more details about the customer’s processing and findings. Check out “How one health plan is improving its ability to detect fraud and increase recoveries.”