Improve Medical Billing Accuracy with a Custom AI System

Independent physician groups often rely on the coding suggestion tools built into their EMR, like those in eClinicalWorks or Allscripts. These tools offer basic, rule-based suggestions but frequently miss the nuance in a physician’s narrative. They cannot reliably interpret complex comorbidities, link diagnoses to procedures, or apply the correct CPT modifiers, leaving your expert coders to manually decipher every note.

Consider a common scenario: a cardiologist's note details a procedure while also mentioning a significant, unrelated comorbidity. The EMR's tool suggests a standard CPT code for the procedure but completely misses the ICD-10 code for the comorbidity. The claim is submitted, denied for lack of medical necessity, and sent back. A biller now spends 25 minutes re-reading the note, identifying the missed diagnosis, finding the right code, and resubmitting the appeal. This single error delays payment by 30 days.

The structural problem is that EMR software is built for a massive, general audience. The vendors cannot build and maintain sophisticated, specialty-specific coding models for cardiology, oncology, and orthopedics simultaneously. Their tools are a generic feature, not a core competency. This forces your practice into a permanent, costly cycle of manual review, data entry, and denial management that off-the-shelf software cannot solve because it was never designed to address your specific workflow.

The engagement starts with a data audit and workflow analysis. Syntora would review 3-6 months of de-identified clinical notes and their corresponding claim outcomes. This process maps your most common denial reasons and identifies the specific coding patterns an AI model must learn to be effective. You receive a report detailing this analysis and confirming the feasibility of an AI-assisted workflow.

The technical approach is a HIPAA-compliant API built with Python and FastAPI, deployed on AWS Lambda for security and scalability. A large language model like the Claude API would parse unstructured clinical notes to extract key entities and relationships. This output is then cross-referenced against a Supabase database containing your specific payer rules, NCCI edits, and LCDs to generate highly accurate code suggestions. Pydantic models ensure all data handling is strictly validated.

The delivered system integrates directly into your team's existing software. For each claim, your biller sees the physician's note alongside a short list of suggested CPT and ICD-10 codes, each with a confidence score and a plain-English explanation of the reasoning. The biller always makes the final decision, maintaining human oversight while using the AI as an expert assistant. This creates a full audit trail and accelerates the review process from minutes to seconds.