Our machine learning model identifies anomalies between upcoming procedures and insurance pre-approvals, based on the reported narrative description of diagnosis, scheduled procedures and services, and other identifiers.
Exclusive code scrubber identifies anomalies between CPT/ICD-10 procedure codes, the reported narrative description of diagnosis, procedures, services and other identifiers, and their expected coding based on machine learning.
Our Adaptive Expert System (AES) flags missing or incorrect codes for a particular proceedure. For example, a claim for a knee revision that is missing associated codes for the removal of the old hardware can be flagged and corrected. This will allow you to recapture lost revenue stemming from denials (miscoding) and incomplete billing.
Our system will flag a pre-approval submission for simple procedure where all indicators point to the use of robotic techniques (e.g., DaVinci). Such coding errors easily exceed $100,000 per surgery patient.