How Providers Can Use AI to Improve Payment Integrity

How many times have you sent a payer a bill only to be told they’re not going to pay it?

When healthcare services are billed incorrectly, your organization loses valuable time and money.

Fortunately, Lumiata’s payment integrity solution can help you identify incorrect claims before you submit them to a payer. We use artificial intelligence and machine learning to automate the payment integrity process, helping you save time and money. 

No one wants to deal with payment integrity struggles 

Did you know that an estimated 80% of medical bills have errors1?

Submitting incorrect claims can cause problems for providers and payers, so it’s important to make sure claims are submitted correctly. Plus, if a claim is flagged as being suspicious, it can keep you from being paid properly. Using a payment integrity solution that’s powered by artificial intelligence and machine learning – like Lumiata is – can help you identify suspicious claims before you submit them so you can correct them and get paid. 

Ensure claims are paid correctly the first time 

Screening your claims for suspicious care patterns and suspicious billing patterns helps ensure you’re only sending accurate claims to payers, allowing you to get paid for the services you provide. Lumiata uses innovative technology to identify outlier claims and make sure the bills you submit are paid the first time. This helps make your cash flow as predictable and smooth as possible. 

How it works 

Using artificial intelligence and machine learning, we automate the claims review process to achieve better outcomes for your organization. We can detect outlier care patterns and billing patterns in the data using outlier model techniques such as principal component analysis (PCA), autoencoder, isolation forest, and more.

Lumiata can also identify claims where service is part of a bundle but was billed twice, claims that break clear rules, the specific attribute that causes a claim to be an outlier, and more. The ability to see outlier patterns also allows us to flag risk factors, assign risk scores to each outgoing bill, and improve existing rules. 

Our payment integrity solution is made up of two phases. Phase one consists of getting your data ready for machine learning through data normalization, cleansing, and enrichment. We then onboard your data and run the first versions of our models using what we already know from our large data asset. In phase two, we tweak our models

based on your specific set of data and patterns of care. This allows us to identify additional outlier claims. 

We determine a claim’s risk score by looking at targeted probabilistic features and targeted SME/expertly defined edits. Lumiata offers a comprehensive approach to identifying suspicious claims for your team to review. 

See how AI can improve your payment integrity strategy 

You deserve to get paid for the services you provide – correctly and for the first time. Lumiata helps your team identify suspicious claims so you can review and fix them as needed and submit accurate bills to payers. Want a closer look at how Lumiata’s AI can help your organization? Click here to request a demo.