Build Custom AI for Patient Scheduling and Intake

Medical offices often use their EHR's built-in tools or third-party platforms like Phreesia for patient intake. An EHR like AdvancedMD might have a patient portal, but it forces patients into a rigid, non-customizable workflow. If you need to add specific questions for a new service line, you often cannot. The platform's data model is fixed, locking you into their predefined fields.

Consider a 10-person orthopedic practice. A new patient emails a 7-page PDF. The front desk staff spends 15 minutes manually re-typing patient demographics, insurance details, and medical history into Practice Fusion. If they miskey a single policy number or a date of birth, the initial claim gets rejected a month later, creating a 60-day accounts receivable delay from one clerical error.

Form-parsing tools like Jotform can digitize the form, but they do not solve the core data entry problem. They collect the data but cannot intelligently map "Primary Care Physician" from the form to the `referring_provider_NPI` field in your EHR. This is because they lack the domain-specific logic to understand healthcare data structures. They just deliver a structured JSON file, leaving the final, error-prone manual entry step to your staff.

The structural issue is that EHRs are built as monolithic systems of record, not flexible automation platforms. Their intake modules are designed for generic data collection, not for the specific workflows of a specialty practice. Third-party intake platforms are built to serve thousands of clinics with one-size-fits-all software, so they cannot accommodate the unique data fields your 10-person office needs.

The engagement would begin with an audit of your current intake process. Syntora would review your intake forms (PDFs or web forms), map out every field, and analyze your EHR's API documentation. We've built document processing pipelines using the Claude API for complex financial reports, and the same pattern of entity extraction applies directly to parsing medical history and insurance cards. The output of this phase is a clear data map and a technical plan you approve.

A HIPAA-compliant system would be built using Python and AWS Lambda. For each submitted form, a Lambda function triggers. The Claude API reads the document, extracts key entities like patient name and insurance info, and structures it as JSON. A FastAPI service then validates this data against Pydantic models and prepares it for insertion into your EHR. This serverless architecture ensures hosting costs remain under $50/month for typical volumes.

The final system presents a human-in-the-loop review interface. Your staff sees the original form next to the extracted, color-coded data. They click a single button to approve and push the data into the EHR. This review gate takes less than 60 seconds per patient and maintains a full audit trail in a Supabase database, logging who approved what data and when.