Automate Healthcare Data Entry Without Replacing Your EHR

Many SMB healthcare practices use EHRs like Practice Fusion or Kareo. These systems have patient portals for direct data entry but struggle with external documents. When a referral arrives as a PDF from another provider, the built-in tools offer no way to extract the patient's name, DOB, and medical history. The only option is manual data entry, which is slow and error-prone.

Consider a 15-person specialty clinic that receives 30-50 faxed or emailed referrals daily. An admin assistant spends hours each day opening each PDF, identifying the patient's demographics, insurance details, and referring physician. They then manually type this information across 12 different fields in the EHR to create a new patient record. If they mis-key an insurance ID number, the claim gets rejected weeks later, creating a costly billing cycle issue and delaying patient care.

The structural issue is that EHRs are designed as monolithic systems of record, not open platforms for integration. Their APIs, if they exist, are often built for patient scheduling or billing, not for accepting unstructured clinical data from third-party documents. Off-the-shelf document parsing tools can read PDFs but lack the medical context to correctly identify a 'Chief Complaint' versus 'Past Medical History'. They also lack the final, critical step: writing that structured data into the specific fields of your EHR.

This gap forces practices to hire more administrative staff just for data entry or accept that clinical staff are wasting valuable time on clerical work. The risk of burnout increases, and the cost of patient acquisition is inflated by hours of non-billable administrative labor just to get a new patient into the system.

The engagement would begin with an audit of your existing workflows and systems. Syntora would map every document type you process, from new patient intake forms to specialist referral letters. We would also assess your EHR's integration capabilities, determining if it has a modern REST API, an older HL7 interface, or if we need to interact with it at the database level. This discovery phase produces a clear technical plan and a fixed-price proposal.

The core of the system would be an AWS Lambda function written in Python that orchestrates the process. When a new document arrives, a trigger invokes the function. It would use the Claude API to parse the document, extracting up to 50 key data fields with over 99% accuracy for typed text. A FastAPI endpoint provides a human-in-the-loop review interface for low-confidence extractions, ensuring clinical data is verified. The entire process from receiving a fax to having verified data ready for the EHR would take under 90 seconds. This system can be built in a 4-week cycle and hosted for under $50/month on AWS.

The final deliverable is a HIPAA-compliant, serverless pipeline that runs automatically in your own cloud account. New patient records and referral data would simply appear in your EHR, flagged for review. You receive the complete source code in your own GitHub repository, a runbook for maintenance, and detailed audit logs of every automated action stored securely in Supabase.