How specific data elements from the 837 claim file could be used to identify medical insurance fraud

How specific data elements from the 837 claim file could be used to identify medical insurance fraud.


 

1. Billing for services not provided: This could be detected by comparing the "Service Date" field in the 837 claim file to the patient's medical records to verify that the billed service was actually provided on that date.


2. Upcoding: This could be detected by comparing the "Procedure Code" field in the 837 claim file to the patient's medical records to verify that the service billed for matches the service that was actually provided. If the billed service is a more expensive code than what was provided, this could be flagged as potential upcoding.


3. Unbundling: This could be detected by comparing the "Service Date" and "Procedure Code" fields in the 837 claim file to verify that multiple services that should have been billed together were not instead billed separately.


4. Duplicate billing: This could be detected by comparing the "Service Date" and "Procedure Code" fields in the 837 claim file to identify any instances where the same service was billed for multiple times.


5. Fictitious claims: This could be detected by comparing the "Provider ID" and "Service Code" fields in the 837 claim file to known valid data to verify that the provider and service are legitimate.


6. Phantom patients: This could be detected by comparing the "Patient ID" field in the 837 claim file to known valid patient data to verify that the patient actually exists.


7. Out-of-network billing: This could be detected by comparing the "Provider ID" field in the 837 claim file to known network data to verify that the provider is in-network.


8. Prescription drug fraud: This could be detected by comparing the "Drug Code" and "Quantity Prescribed" fields in the 837 claim file to the patient's medical records to verify that the prescribed drug and dosage are medically necessary.

#dataanalytics #data #medical #insurance #frauddetection #datascience

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