Automate Patient Intake and Scheduling with Custom AI

Most small medical offices rely on the basic features within their Practice Management System (PMS) like Kareo or Practice Fusion. These systems offer simple web forms and generic appointment reminders, but they fail the moment a workflow deviates from the standard. Their form builders cannot parse a PDF from another doctor's office or extract information from a photo of an insurance card that a patient emails over. Your front desk staff is left to manually re-type everything.

Consider a 3-doctor practice where a new patient emails a scanned copy of their intake form and insurance card. The office manager spends 15 minutes transcribing patient history, allergies, and policy numbers into the EHR. This happens 5 times a day, totaling over an hour of repetitive data entry. The built-in reminder system sends a single text 24 hours before the visit, with no option for custom logic like sending a second reminder to new patients or including different instructions for specific procedures.

Some offices try to patch these gaps with separate tools like Jotform for forms or a basic SMS service. This creates disconnected data islands. Information from a Jotform submission does not automatically appear in the EHR. A staff member must still copy and paste the data, which introduces both human error and a potential HIPAA compliance risk if data is handled improperly between systems.

The structural issue is that EHR and PMS platforms are designed for the average practice, not your specific one. Their automation capabilities are rigid because they must serve thousands of clients with one codebase. They cannot handle the unstructured data (PDFs, images) and custom logic (complex reminder rules) that define your actual day-to-day operations. You are forced to bridge the gaps with expensive manual labor.

The first step is a workflow audit. Syntora would map your end-to-end patient intake process, from first contact to the data appearing in your EHR. We analyze your intake forms, scheduling rules, and the specific API or data entry method for your EHR system. This audit produces a technical plan that ensures a secure, HIPAA-compliant connection is possible before any code is written.

The core of the intake system would be a Python service running on AWS Lambda. When a patient submits a document, the service uses the Claude API to parse it. Claude is highly effective at extracting structured data like 'Patient Name' and 'Insurance ID' from unstructured sources like PDFs and images. We have built similar document processing pipelines for financial services. Pydantic models validate the extracted data for correctness before a secure function writes it to the correct fields in your EHR. This entire process takes under 60 seconds.

For appointment reminders, a separate process would query your schedule, apply your custom rules, and use the Twilio API to dispatch personalized SMS messages. The final system is a secure, serverless pipeline that lives in your own AWS account. You receive the complete Python source code, a Vercel-hosted dashboard for monitoring, and a runbook detailing how to manage the system. Total hosting costs are typically under $50 per month.