Rules Based Post-payment Analysis allows you to look for known fraud patterns, so you can focus your investigations on the claims most likely to be fraudulent. These systems are also known as Expert Systems, because they are built based on information provided by fraud experts. The systems produce reports that identify providers, patients and claims that fit specific criteria. Each report identifies a particular kind of fraud.

Here’s an example of rules-based analysis:

Providers who order chest x-rays for all patients with respiratory distress.

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1) Identify all claims for physician visits with a diagnosis of respiratory distress.

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2) Identify all claims for chest x-rays

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3) Link the results from steps one and two using patient id and date of service

4) Tag the chest x-ray claims to the physician visit claims

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5) Summarize the results from step 4 by provider id, create a unique count of patients with respiratory illness and a unique count of patients with respiratory illness and chest x-rays.

6) Create a new field Percent of Respiratory Patients with Chest X-rays (Chest X-Ray Patients/Respiratory Patients)

7) Sort by Percent of Respiratory Patients with Chest X-rays and look at the top providers.
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With these systems and follow-up investigation, you should be able to get a $6-$12 return for every dollar you spend. Rules based analysis is a great leap forward for fraud detection. However, they miss a lot of fraud, because they require us to define the scam before we can find the perpetrators. If we don’t know about a scam, we can’t use rules to search for it.

Tele-Seminar: Robin’s Fraud Toolbox