Build a Custom AI Scheduler for Your Clinic

Many small clinics use scheduling tools like Zocdoc for patient acquisition or platforms like Solutionreach for reminders. Zocdoc is a public directory that cannot enforce a clinic's complex internal rules. Solutionreach is effective for one-way communication but its scheduling modules are often rigid and cannot optimize for provider time or equipment availability.

Consider a 3-provider dermatology clinic using Practice Fusion as their EMR. A patient needs a follow-up for a procedure requiring a specific laser, which is only available in one room. The front desk staff must check the provider's calendar in Practice Fusion, cross-reference a separate Google Sheet for the laser's schedule, find a matching slot, and then call the patient back. This multi-system, manual process takes over 10 minutes and is prone to double-booking the laser.

The core problem is that off-the-shelf tools are built on a fixed data model. They allow configuration of appointment types and times, but they cannot treat a piece of equipment or a specific room as a core scheduling constraint with its own calendar. You cannot build custom logic that says, 'Find a 30-minute slot where Dr. Smith, Room 2, and the Sonogram Machine are all available.' This requires a custom-engineered system that models all of your clinic's unique constraints.

The first step is an audit of your current scheduling workflow and EMR system. Syntora would map every appointment type, provider preference, and resource constraint. We would investigate your EMR's API documentation to determine the most reliable way to read calendar data and write new appointments, ensuring HIPAA compliance at every step. This discovery phase results in a clear architectural plan before any code is written.

The technical approach would use a Python service running on AWS Lambda, providing a cost-effective, serverless architecture. We would use FastAPI to create an internal API that powers a simple web interface for your staff. The Claude API would handle parsing unstructured patient requests from emails or web forms into structured data for scheduling. All patient data is stored securely in a HIPAA-eligible Supabase database with encryption at rest and in transit.

The delivered system would be a simple web application that suggests the top 3 optimal appointment slots based on all your rules. It would connect directly to your EMR, writing appointments back automatically and triggering confirmation messages. You receive the full source code, a runbook for maintenance, and documentation, all deployed within your own AWS account.