Automate Clinical Workflows with Custom AI Systems

Many specialty practices rely on the built-in features of their EHR or Practice Management Software, like Practice Fusion or Kareo. These tools can send appointment reminders, but their automation capabilities stop there. They cannot analyze the content of a document. For example, the software cannot flag a new patient form that mentions a specific allergy and route it to a nurse for review. The automation is based on simple triggers, not clinical content analysis.

Consider a 15-person dermatology practice that receives 25 new patient referrals daily via fax and email. The front desk staff manually re-keys patient demographics, insurance details, and referring physician information from PDFs into the EHR. This takes 10-15 minutes per patient, consuming over 4 hours of staff time every day. A single mistyped digit in an insurance ID can cause a claim denial weeks later, requiring hours of follow-up work.

Off-the-shelf document parsing tools fail because they are not trained on the high variability of medical documents. A referral from one physician is a structured PDF, while another is a scanned fax with handwriting. These generic tools cannot reliably extract fields like "Referring NPI" or "Chief Complaint" and require constant manual correction, which defeats the purpose of automation.

The structural problem is that EHRs are built as systems of record, not systems of operational efficiency. Their primary function is compliant data storage. Their APIs often lack the event hooks needed to trigger complex, content-aware workflows. Generic automation tools cannot solve this because they lack the domain-specific logic and HIPAA-compliant architecture required to handle protected health information (PHI).

The first step is an audit of your clinical operations and current EHR. Syntora would map the exact journey of a new patient from referral receipt to the first scheduled appointment. We analyze your document types (faxes, PDFs, portal messages) and your EHR's specific integration capabilities. You receive a process map showing exactly where automation can be applied, the required data access, and a clear timeline.

The technical approach uses Claude API for its strong performance on unstructured medical document parsing. A HIPAA-compliant AWS Lambda function would receive new documents, for instance from a monitored email inbox. Claude API then extracts key entities like patient name, DOB, insurance ID, and chief complaint. The system uses your EHR's API to search for an existing patient or create a new one, populating the fields automatically. All processing is logged in Supabase for a complete, permanent audit trail.

The delivered system integrates a human review gate directly into your workflow. Instead of performing manual data entry, your staff receives a notification with the extracted data pre-filled, ready for one-click approval before it is committed to the EHR. This reduces manual work from over 10 minutes per patient to under 30 seconds of review. You receive the full Python source code, a deployment runbook, and a signed Business Associate Agreement (BAA).