Use AI Automation to Reduce Denied Medical Claims

Most small billing companies rely on the claim scrubbers built into their Practice Management System (PMS) like Kareo or AdvancedMD. These tools are useful for catching simple formatting errors, such as a missing date of birth or an invalid Place of Service code. However, they operate on a static, universal rule set. They cannot interpret clinical context, which is the source of most complex denials.

Consider this common scenario: A 10-person billing company receives a charge for an Evaluation and Management (E/M) service coded as 99214, a level 4 visit. The built-in scrubber in their PMS checks the claim and sees a valid patient, a valid provider, and a valid CPT code. It approves the claim for submission. Weeks later, the claim is denied for 'not medically necessary.' The physician’s notes, buried in an unstructured text block, only supported a level 3 visit (99213). A human biller then spends 45 minutes appealing the denial, a low-value task that repeats daily.

The structural problem is that off-the-shelf software is architected for standardization, not interpretation. The scrubbers cannot read unstructured text or cross-reference a specific CPT code against thousands of pages of a single payer’s evolving clinical policies. They are fundamentally incapable of flagging a mismatch between what the doctor wrote and what the biller coded. This leaves small billing companies stuck in a reactive cycle of submitting, getting denied, and appealing.

The first step would be a deep audit of your denial patterns. Syntora would analyze your last 12 months of remittance advice (835/ERA files) to identify the top 5 reasons for denial for your most important payers. We would also map your data flow, from receiving clinical notes and superbills to submitting the final 837 claim file. This discovery phase typically takes 3-5 business days and produces a clear, actionable build plan.

The core of the system would be a Python service using the Claude API for its large context window to parse unstructured clinical notes and match them against CPT and ICD-10 codes. A FastAPI endpoint would accept claim data for audit. Pydantic models would validate the incoming data structure, preventing bad data before processing. For payer-specific rules, we would use a Supabase database to store and query thousands of policies. The entire HIPAA-compliant service would run on AWS Lambda, processing each claim in under 2 seconds.

The delivered system would be a simple API that your team could call before submitting a claim to your clearinghouse. It would return a pass/fail status and a plain-English explanation of any potential issues, like 'Note text supports E/M level 3, but claim bills level 4.' You receive the full source code in your GitHub repository, a runbook, and a monitoring dashboard. The hosting costs on AWS Lambda would typically be under $50/month for a volume of 10,000 claims.