Automate Prior Authorization Requests with a Custom AI System

Many practices rely on the modules within their EHR or third-party clearinghouses like Availity. Epic’s prior auth module helps track status, but it doesn’t automatically extract the specific clinical justifications needed for a complex procedure. A billing specialist still has to read the doctor's notes, find the relevant lab values, and manually type them into the correct fields on the payer's portal.

Consider a 10-person orthopedic practice trying to get prior authorization for an MRI. The patient's record is in their EHR, Kareo. The billing specialist logs into the Aetna portal and enters the patient's data. Aetna's portal then presents a clinical questionnaire asking for details like 'duration of physical therapy'. The specialist must now open Kareo in another window, find the relevant progress notes, and copy-paste the answers one by one. This takes 20 minutes and is prone to error.

The structural problem is the gap between structured billing data (CPT codes) and unstructured clinical data (doctor's notes). EHRs and clearinghouses are designed for the former. They are not built with modern LLMs that can read and interpret a physician's narrative. Each payer has unique clinical criteria, and off-the-shelf software cannot adapt to the specific requirements of dozens of different insurance plans.

The result is a workflow that costs an estimated $15-20 in staff time per authorization. For a practice submitting 30 authorizations a week, that's over $2,000 a month spent on clerical work. The high cognitive load of switching between systems also leads to data entry errors, causing denials that require even more time to appeal.

The first step is a process audit. Syntora would map the end-to-end prior authorization workflow for your top 5 payers. This involves reviewing the specific clinical data each payer requires, how your EHR exposes that data, and where your staff currently spends the most time. You would receive a technical diagram showing the proposed data flow and a clear list of what information the system will automate.

The core system would be a HIPAA-compliant Python service running on AWS Lambda. When an order requiring prior auth is created, it would use the Claude API to read the relevant clinical notes from your EHR. The system would extract key data points, like symptom duration or treatments attempted. We've used this same document parsing pattern for financial services; applying it to clinical notes follows a similar architecture. All data processing happens in memory and is never stored, maintaining compliance. A response is typically generated in under 30 seconds.

The delivered system would present a pre-filled authorization request to your billing staff for review. It would show the extracted clinical justifications and the target fields in the payer's form. Your staff verifies the AI's work and clicks 'submit'. The system logs every action to a Supabase database, creating a complete audit trail. The goal is to reduce manual entry by 90%, not to eliminate human oversight.