Build Custom AI for Healthcare Claims Processing

Most small clinics use the built-in billing module of their Practice Management System, like Kareo or DrChrono. These tools are effective for submitting standard claims but lack the flexibility to handle the specific billing rules of specialty practices. For example, a mental health clinic might find its claims for group therapy are constantly denied by one specific insurer who requires a certain modifier. The PMS has no way to learn and apply this rule automatically, forcing a biller to manually inspect and edit every single claim going to that payer.

When claims are denied, the clinic's biller logs into a clearinghouse portal like Availity to see why. The portal provides a cryptic denial code, like CO-22 (Coordination of Benefits), which requires manual investigation. The biller must then go back to the patient's file in the PMS, find the correct insurance information, update the claim, and resubmit it. This multi-system, manual process happens dozens of times a day. For a 20-person clinic submitting 600 claims a week, a 10% denial rate means a full day of work is lost just to rework and resubmit claims.

We have built document processing pipelines using the Claude API for financial services that extract and validate data. This same pattern applies directly to parsing Explanation of Benefits (EOB) documents or unstructured clinician notes to find the source of a claim denial. The core engineering challenge is the same.

The structural problem is that off-the-shelf software is built for generalization, not specialization. A PMS vendor cannot build custom logic for every specialty and every insurer. The systems are closed, preventing clinics from injecting their own rules. Your team is forced to become a human bridge between rigid systems, performing low-value data entry instead of focusing on complex denials that require real expertise.

The engagement would start with a discovery audit of your last 6 months of claim denials. Syntora would analyze the data from your PMS and clearinghouse to identify the top 3-5 denial reasons that consume the most staff time. This analysis produces a clear, data-backed recommendation for the highest-impact automation target. You would receive a scope document detailing the proposed workflow, technical architecture, and a fixed-price quote.

The technical approach would involve a set of HIPAA-compliant AWS Lambda functions written in Python. When a new claim is created in your PMS, a webhook would trigger a function. This function uses the Claude API to read the associated unstructured clinician's notes and compares them against the proposed CPT and ICD-10 codes. If it detects a likely mismatch based on patterns from past denials, the system flags the claim in a simple review dashboard built with FastAPI. The system is designed with Pydantic for strict data validation at every step.

The delivered system is a human-in-the-loop tool, not a black box. Your billing team would see a simple queue of flagged claims with clear explanations, like 'Note mentions 60-minute session, but CPT code is for 45 minutes. Suggest changing to 90837.' The biller makes the final approval with a single click. This pre-submission check would integrate with your existing PMS, preventing bad claims from ever reaching the clearinghouse. You would own the entire codebase, deployed in your own AWS account.