Integrate Custom AI Automation with Your EHR System

Small medical groups often rely on the basic workflow features inside their EHR, such as those in eClinicalWorks or Practice Fusion. These systems can create a task when a new fax arrives in a digital inbox, but they cannot read the document. A staff member must still open each PDF, identify the patient's name, DOB, and referring physician, then manually type all of it into the patient record. This process is slow, repetitive, and prone to costly data entry errors.

Consider a 15-person orthopedic group that receives 40 referral faxes a day. An administrative staff member spends over 3 hours daily just on data entry. They manually transcribe patient demographics, insurance IDs, and clinical notes into the EHR. A single mistyped digit in an insurance policy number can lead to a denied claim weeks later, creating hours of additional work to investigate and resubmit. This is not a failure of the staff; it is a failure of the software.

General-purpose OCR tools are not a solution because they lack medical context. They can extract text from a scanned document but cannot differentiate between a referring provider and a primary care physician, or correctly interpret common medical abbreviations. The output is a block of text that still requires a human to parse and enter correctly into the EHR's structured fields.

The structural problem is that EHRs are built as secure databases, not as intelligent workflow engines. Their core architecture prioritizes data integrity over automation flexibility. Off-the-shelf automation tools fail because they are not HIPAA-compliant by default and cannot be trained on the specific formats of your most common referral forms. A custom solution is required to bridge the gap between unstructured documents and the structured data your EHR requires.

The first step is a discovery process focused on your specific documents and EHR. Syntora would start by analyzing a batch of 20-30 of your recent referral forms to identify all the critical data fields. We would also review your EHR's API documentation to map those fields to the correct endpoints for creating or updating patient records. You receive a technical specification document outlining the exact data flow before any code is written.

The technical approach uses a HIPAA-compliant document processing pipeline built on AWS. An incoming document from an e-fax service triggers an AWS Lambda function. This function uses the Claude API with a carefully engineered prompt to extract and structure the necessary medical and demographic data. A FastAPI service then validates this data against Pydantic models to ensure every field is in the correct format, catching potential errors before they reach your EHR. This entire process takes under 2 seconds per page.

The delivered system provides a simple web interface where a staff member can review the extracted data alongside the original document. With a single click to approve, the system makes a secure API call to your EHR to populate the new patient record. Every action is logged in a Supabase database, creating a permanent, searchable audit trail that meets HIPAA requirements. The system is designed to process over 1,000 documents per month for a hosting cost under $50.