Automate Patient Intake and Scheduling with Custom AI

Many small clinics rely on the patient portal built into their Practice Management System (PMS), like Kareo or AdvancedMD. These portals are often clunky, leading frustrated patients to abandon the form and call the office anyway. The forms are rigid; adding a custom question for a new service requires a slow and expensive engagement with the PMS vendor.

To get more flexibility, some clinics turn to general tools like Jotform, but this creates new problems. Achieving HIPAA compliance requires expensive tiers and a signed Business Associate Agreement (BAA). Even then, the data does not flow into the EMR. A staff member must still manually copy every field from the form submission email into the patient's official record, re-introducing the risk of human error.

Consider a 5-person pediatric clinic. A parent emails a completed PDF intake form. The front desk admin opens the PDF, then types the patient's name, DOB, and insurance information into the EMR. This takes 10 minutes. Then they begin a two-day game of phone tag to schedule the first appointment. The entire process consumes over 15 minutes of active staff time and creates a 48-hour delay in care.

The structural issue is that EMRs are built to be systems of record, not automation hubs. Their APIs are often limited or nonexistent. General form builders are not designed for the specific, structured data healthcare requires. This means the critical last mile of connecting patient-provided information to the patient record remains a manual, error-prone task.

An engagement would start with an audit of your current intake workflow and EMR system. Syntora would review your intake forms, map out every required field, and investigate your EMR's API to find the most reliable integration path. This initial discovery phase produces a clear technical plan, which you approve before any build work begins.

The technical approach uses a HIPAA-compliant Python service running on AWS Lambda. When a patient submits a form, the service uses the Claude API to parse the document and extract structured data. For scheduling, the system connects to your clinic's calendar, finds open slots, and presents them to the patient via a simple web interface. FastAPI serves the API endpoints, and Pydantic models enforce data validation to ensure clean data enters your EMR. We have built similar secure document processing pipelines for financial documents, and the same architectural pattern applies here.

The delivered system provides a secure portal for patients to submit information. The processed data appears directly in your EMR, flagged for a quick human review before being finalized. AI-booked appointments show up in your calendar just like manually booked ones, fitting directly into your existing workflow. You receive the full source code and a runbook detailing how to operate the system.