Build Custom AI Automation for Your Practice's EHR

Most small practices try to solve data entry problems with their EHR's built-in tools or generic office software. EHRs like Athenahealth or Practice Fusion have patient portals, but they can't process a referral faxed from another provider. Your front-office staff is left manually transcribing patient names, dates of birth, and insurance IDs from a scanned PDF into the EHR. This is slow, tedious work where a single typo can lead to a rejected insurance claim.

Then you might look at a general-purpose document parsing tool. These tools are often not HIPAA-compliant. Sending a PDF with Protected Health Information (PHI) to a generic online service for processing is a significant compliance violation if there is no Business Associate Agreement (BAA) in place. These tools also lack the medical context to correctly identify specific billing codes or clinical terms.

Consider a 15-person specialty clinic that receives 30 referrals a day via a secure e-fax portal. An administrative staff member spends over 5 hours every day opening each PDF, identifying the patient demographics, and re-typing everything into 20 different fields in the EHR. If the referring doctor's fax is poorly scanned, the staff member has to call their office to confirm details, adding more delay. The process is a full-time job that creates a bottleneck in patient scheduling.

The structural issue is that EHR systems are designed as closed databases, not open platforms. Their APIs, if they exist, are often based on complex healthcare standards like HL7 or FHIR that require specialized engineering knowledge. Off-the-shelf automation tools are not built to handle this complexity or the stringent security and compliance requirements of handling PHI. They cannot provide the audit trails and granular control needed for healthcare.

The first step is a technical discovery and compliance review, starting with a signed BAA. Syntora would audit your current referral workflow, the exact data fields your EHR requires, and your EHR's API documentation (or lack thereof). This initial phase produces a detailed data map that traces information from the source document to the final field in the patient record. This map becomes the blueprint for the entire system.

The technical approach would use a series of HIPAA-compliant AWS services. Incoming faxes or documents would land in a secure AWS S3 bucket. An AWS Lambda function, written in Python, would trigger on each new file. This function would send the document to the Claude API to extract structured data like 'patient_name', 'dob', and 'insurance_id'. Pydantic models would validate the extracted data against the required EHR format. All processing happens in a secure, isolated environment with detailed logging for audit purposes.

The delivered system provides a 'human-in-the-loop' review interface built with Supabase. Instead of re-typing, your staff sees the extracted data next to the original document and simply clicks 'Approve'. Upon approval, another Lambda function pushes the validated data into your EHR via its API. This approach reduces manual work by over 90% while ensuring a human always has the final say, maintaining clinical accuracy. You receive the full source code and control of the AWS infrastructure.