How Payers Can Use AI to Improve Payment Integrity

Is your current payment integrity strategy costing you time and money?

With an estimated 80% of medical bills containing errors [1] paying incorrect claims can get expensive. Sending suspicious claims to be reviewed by vendors can help prevent you from paying incorrect claims, but vendor fees can quickly add up.

Fortunately, Lumiata’s artificial intelligence and machine learning solutions can help you identify suspicious claims before they’re paid so you can save money and send fewer claims to vendors.

Identify suspicious claims before they’re paid 

Paying a claim only to realize later that you shouldn’t have can be extremely frustrating. Lumiata helps you identify suspicious claims before you pay them. Using artificial intelligence and machine learning, our outlier model techniques identify claims that don’t follow typical patterns of care.

Our technology then automatically finds outlier patterns in the data that can be used to flag risk factors, assign risk scores to each incoming claim, and improve existing rules. Lumiata provides an estimated overpayment identification potential of 30-40%. 

Empower your internal team to catch more suspicious claims 

Do you wish your team was more efficient at catching suspicious claims?

For a flat fee, Lumiata will determine the best claims for your internal teams to review. This allows you to focus on auditing higher-cost claims while vendors handle-less significant ones. 

Send fewer and lower-cost requests to vendors 

By helping your team identify the most suspicious and expensive claims, Lumiata enables you to reduce the number of claims sent to vendors as well as empower your team to only send in lower-cost claims. Since vendors will be charging fees on fewer and less expensive claims, your organization can save money. 

How it works 

Lumiata uses autoencoder, isolation forest, principal component analysis (PCA), and other outlier model techniques to identify claims that break clear rules and don’t align with typical care patterns. We can also identify the specific attribute that caused the claim to be an outlier, find claims where a service was billed twice and shouldn’t have been, and more. 

Our payment integrity solution consists of two phases. The first phase is the “Getting ML Ready” phase, where we prepare your data for machine learning through data normalization, cleansing, and enrichment. We also onboard your data and run the first versions of our models using the information we already have in our vast data asset. The

the second phase of our payment integrity solution is the “ML Models” phase, where we adjust our models based on your data and patterns of care to find additional outlier claims. 

By layering machine learning on top of a traditional SME approach, Lumiata provides a more comprehensive payment integrity approach. 

See how AI can improve your payment integrity strategy 

Incorrect claims cost payers valuable time and money. With Lumiata, you can identify more suspicious claims before you pay them and before they’re sent to vendors. Want to see how Lumiata can help your organization? Click here to request a demo.