Automate Revenue Cycle Management for Your 20-Person Practice

Many medical groups rely on their EHR's built-in billing module, from vendors like Practice Fusion or Kareo. These tools offer basic charge entry but lack intelligence. They cannot parse a clinician's unstructured notes to suggest the correct CPT or ICD-10 codes. Your billing staff must still read every note, manually translate clinical terms into codes, and hope they have selected the right ones for a specific payer's arcane rules.

Consider a biller at a 20-person orthopedic practice. The surgeon's note says "arthroscopic debridement of shoulder." The biller has to manually look up the CPT code, check for required modifiers, and enter it. If they choose the wrong modifier for a specific Aetna PPO plan, the claim is denied 30 days later. Now, a different team member must spend 45 minutes on the phone with the payer, correct the claim, and resubmit, delaying revenue by weeks.

Larger RCM platforms like Waystar offer more automation, but they are built for giant hospital systems. They force your small practice into a standardized, inflexible workflow and charge high per-claim fees. You cannot customize their logic to address your practice's most common denial reasons because their system is a black box. You are paying for a massive platform when you only need to solve a few specific, high-impact problems.

The structural problem is that off-the-shelf software is designed for mass-market standardization, not the high-variability world of a specialty medical practice. Your unique mix of payers, procedures, and clinicians creates patterns that generic software cannot see. You need a system trained on your data to solve your problems.

The first step would be a data audit. Syntora would analyze 12-18 months of your historical claims data, including submissions, denials, and appeals. This process maps your top denial reasons and identifies which payers cause the most friction. We would also review your clinical note-taking templates in your EHR to understand the structure of the data the AI will parse. This audit produces a clear target: automating the 2-3 most common and time-consuming billing tasks.

The core of the system would be a HIPAA-compliant document processing pipeline using the Claude API, deployed on AWS Lambda. When a clinician finalizes a note, a webhook sends the text to the API. Claude API parses the unstructured text, identifies procedures and diagnoses, and suggests CPT and ICD-10 codes trained on your historical billing data. We use Python with Pydantic schemas to validate the output before it is presented to a human biller for review.

The delivered system exposes a simple UI inside your existing workflow, often as a browser extension that works with your cloud-based EHR. The UI shows the original note alongside the suggested codes and a confidence score. A biller can approve or edit the codes with one click, which then get posted to your clearinghouse via their API. The entire process for a single claim would take under 10 seconds, and hosting costs on AWS Lambda are typically under $50 per month.