Custom AI for Patient Scheduling Efficiency

Most specialty clinics run on the scheduling module built into their EHR. These systems are rigid, calendar-based tools that cannot interpret the unstructured data where most scheduling requests originate, like a referral fax from a primary care physician. Staff must manually read the fax, decipher the physician's notes, identify the required specialty and urgency, and then begin the phone tag dance with the patient.

A common scenario involves a 30-person cardiology practice receiving a faxed referral. A staff member prints it, highlights key terms, and opens the EHR calendar. They must manually cross-reference three cardiologists' schedules, remembering that one only does new patient consults on Tuesdays and another requires a recent EKG. After finding a slot, they call the patient, leave a voicemail, and place the fax in a "pending" tray. This cycle repeats for dozens of referrals daily, leading to a 3-day scheduling backlog and a 10% error rate.

Some clinics try to solve this with interactive voice response (IVR) phone systems, but these often frustrate patients with rigid, numbered menus. They cannot handle the nuance of a complex referral. The fundamental problem is a gap between unstructured clinical requests and the structured data an EHR requires. Without an intelligent translation layer, human labor is the only bridge, and that labor is expensive and error-prone.

Syntora's approach would begin with connecting to your data sources. We would establish secure, read-only access to your EHR, often through a REST API for systems like Cerner or Athenahealth. For incoming referrals, we would set up a service to ingest documents from digital fax lines and dedicated email inboxes. Initial discovery would include analyzing your historical scheduling data to map provider-specific rules and appointment types.

The core of the proposed system is a Python-based processing pipeline. Syntora would configure the Claude API to parse and structure data from an incoming referral PDF or email body. This API is capable of extracting patient demographics, referring physician details, insurance information, and the clinical reason for the visit. The structured JSON output would then be passed to a custom rules engine, also built in Python, designed to match the clinical reason to the appropriate specialist, appointment duration, and required pre-visit tests. All logic and parsed data would be stored in a HIPAA-compliant Supabase database.

This entire pipeline would be deployed as serverless functions on AWS Lambda. When a new referral arrives, the function would execute automatically, process the document, and present a scheduling suggestion to your staff. This would occur via a simple Vercel-hosted web dashboard, which would display the parsed data and optimal appointment slots. Staff would review the suggestion and click to confirm, triggering a booking action via the EHR's API.

This architecture is designed for high availability and security with minimal overhead. The system would maintain a complete, immutable audit trail for every automated action in Supabase, fulfilling HIPAA requirements for traceability. For low-confidence extractions, the document would be flagged and routed to a human review queue within the dashboard. This design keeps staff in control while automating a significant portion of the initial data entry and decision-making. We estimate the total hosting cost on AWS and Vercel to be under $70 per month.