Automate Denied Claims Appeals with AI Engineering

Most small healthcare facilities rely on the denial management module within their Practice Management (PM) or EHR system, like Kareo or eClinicalWorks. These systems are good at flagging a denied claim, but they offer no help in resolving it. The workflow remains entirely manual: a biller must look up the denial reason code, manually hunt for the patient's clinical notes to justify the service, and then type or paste that information into a generic letter template.

Consider this scenario: a small physical therapy clinic receives a denial from Aetna for "services not medically necessary." The biller has to manually pull the patient's treatment notes, find the specific progress records that justify the therapy, copy-paste them into an appeal letter, and then upload the final document to Aetna's provider portal. This same task repeats 15 times a day with minor variations. If that biller is sick or on vacation, the appeals queue grows, revenue gets delayed, and you risk missing timely filing deadlines.

The structural problem is that PM systems and clearinghouses like Availity are databases designed for structured data and transaction processing. They can store denial codes and track statuses, but they cannot synthesize unstructured text from clinical notes to build a coherent, persuasive argument. They lack the language generation capabilities required to combine a denial code, patient history, and a specific payor's appeal requirements into a finished document.

The first step would be a data audit of your last 6 months of claims data from your PM system or clearinghouse. This audit identifies the top 5 most frequent denial reasons and the specific payors involved. We would simultaneously map the data fields available in your EHR, such as patient progress notes and physician orders, which are essential for building the appeal narrative. The outcome is a clear plan targeting the 80% of denials that can be automated.

The core system would be a Python service running on AWS Lambda, triggered whenever a new denial is recorded. The service uses the Claude API to parse the denial reason and search the relevant patient chart data for supporting evidence. Based on a repository of payor-specific rules built with your team, it generates a draft appeal letter. This process is HIPAA-compliant, as all data is handled within a secure environment covered by a Business Associate Agreement (BAA).

The delivered system provides a simple web interface where your billing staff can review, edit, and approve the AI-generated drafts. A FastAPI endpoint handles these interactions, ensuring the human review step is seamlessly integrated into your existing workflow. The entire process, from denial flag to approved letter, would take under 30 seconds. You receive all the source code, a runbook, and a system with hosting costs typically under $50/month.