Automate EHR Data Entry with Custom AI

Many small practices use EHRs like Practice Fusion or athenahealth, which have patient portals but don't solve the document problem. Staff still spend hours transcribing information from scanned intake forms, referral letters, and lab results. Some may try general OCR tools, but these tools don't understand medical context. They can't distinguish between a medication and an allergy, or correctly map 'Lisinopril 10mg' to the structured medication field in the EHR.

Consider a 3-doctor practice with 5 new patients daily. Each patient submits a 5-page intake packet. The front desk staff spends at least 10 minutes per patient manually typing demographic data, insurance details, medical history, and medications into the EHR. That's over an hour per day of high-risk, low-value work. A single typo in a policy number can lead to a rejected claim weeks later, while a mistake in a dosage could create a serious patient safety issue.

The structural problem is that EHRs are designed to be systems of record, not systems of ingestion. Their architecture prioritizes structured data storage, not the messy reality of parsing unstructured documents. Generic automation tools lack the domain-specific intelligence to handle this translation. They cannot reliably interpret handwritten notes or the varied formats of referral documents from other providers. Practices are left with a manual gap that software vendors have not filled.

The engagement would begin with an audit of your current intake process and EHR system. Syntora would analyze your patient forms and investigate your EHR's integration capabilities. The first step is mapping every field on your forms to a specific field in the EHR to create a clear data contract. You receive a scope document detailing the proposed architecture, timeline, and a fixed price for your approval.

The core of the system would be an AWS Lambda function written in Python. When a new form is uploaded to a secure folder, the function triggers, using the Claude API for its advanced document understanding capabilities. Claude can perform OCR and structured data extraction simultaneously, accurately identifying entities like medications, dosages, and insurance IDs. This extracted data is then validated against Pydantic schemas before any attempt to write it to the EHR.

The final deliverable is a secure, HIPAA-compliant pipeline. A simple web interface, built using FastAPI, shows the original document next to the extracted data. Your staff verifies the information with a single click, which then commits the data to the patient's chart via the EHR's API. This human review gate ensures 100% accuracy while eliminating over 95% of the manual keyboard entry.