Implement a HIPAA-Compliant AI Scheduling System

Most medical offices rely on their EHR's built-in scheduler, such as those in Athenahealth or Epic. These tools are excellent for record-keeping but are fundamentally rigid. They require staff to act as human translators, converting a patient's conversational request like "I have knee pain and can only do afternoons" into a series of structured clicks and form fields. This manual translation is slow and error-prone.

To supplement this, some practices adopt tools like Calendly for online booking, but these are too simplistic for healthcare. Calendly can show an open slot, but it cannot perform insurance verification, triage a patient based on symptoms to the correct specialist, or handle the complex, multi-step logic required for booking many medical appointments. A patient ends up booking a slot, but a staff member still has to call them back to collect the critical details, creating double work.

Consider a 20-person orthopedic practice receiving 40 new patient calls daily. A front desk employee spends over five minutes per call, navigating between the EHR calendar, a separate insurance portal, and the patient's questions. This process consumes hours of skilled staff time that could be spent on higher-value patient interaction. The web forms on the practice's website capture only basic contact info, initiating a long game of phone tag.

The structural problem is that off-the-shelf tools are designed for structured data input. They force the patient and staff to conform to the software's predefined workflow. A real patient request is an unstructured narrative. Existing tools cannot parse this narrative to simultaneously extract entities (symptom, insurance, availability), apply business logic, and confirm an appointment in one seamless interaction.

The engagement would start with an audit of your current patient intake process. Syntora would map the entire workflow, from a patient's first call or web visit to a confirmed appointment in your EHR. We would document your specific scheduling rules, practitioner availability logic, and insurance verification steps. This discovery phase produces a detailed scope document outlining the system architecture.

The core of the system would be a FastAPI service deployed on AWS Lambda for HIPAA compliance. When a patient interacts via a web chat or phone IVR, their request is transcribed and sent to the Claude API. Claude extracts structured data like symptoms, insurance details, and availability from the unstructured text. This data is validated using Pydantic schemas before being processed by the scheduling logic. This serverless approach keeps hosting costs under $50 per month.

The final deliverable is a HIPAA-compliant AI agent that integrates with your website and phone system. Patients can book complex appointments conversationally, 24/7. Your front desk staff receives confirmed appointments directly in the EHR, with a complete audit trail of the AI's interaction and data extraction. You receive the full Python source code, a runbook for maintenance, and control over all cloud infrastructure.