Automate Patient Intake and Scheduling with Custom AI

Many small medical practices use their Practice Management System's (PMS) built-in patient portal or a generic tool like JotForm for digital intake. While better than paper, these tools create new bottlenecks. A PMS form is often rigid; you cannot add complex conditional logic for a specific specialty, forcing every patient through the same generic questionnaire. The data often saves as a flat PDF in the patient's file, which a staff member must still read and manually re-enter into the structured fields of the patient record.

Consider a 3-physician orthopedic practice using Kareo. A new patient with a complex knee injury fills out a JotForm intake packet online. The form submission triggers an email with a PDF to the front desk. A staff member opens the PDF on one screen and Kareo on another, then spends 10 minutes manually transcribing allergies, medications, and insurance details. This repetitive work is prone to transcription errors, where a mistyped medication dosage can have serious consequences. The process for scheduling is similar, involving phone calls and back-and-forth emails to find a time that works.

The structural problem is a lack of deep integration. Generic form builders are not designed to understand medical terminology or map answers to the specific database fields inside a PMS. The PMS itself may have an API, but it's not built to receive and parse unstructured text from a third-party form. You are left with a data silo problem where patient information is collected in one place but needs to be manually moved to the system of record. This is not a problem you can solve with a simple connector; it requires an intelligent intermediary.

The engagement would begin with a thorough audit of your existing intake forms and scheduling logic. Syntora would map every field on your forms to its corresponding destination in your PMS. We would also document your scheduling rules: appointment durations by type, physician availability, and rules for new vs. established patients. This audit produces a clear data-mapping document and technical specification that you approve before any code is written.

The technical approach would use a HIPAA-compliant stack. A front-end built with Vercel would present patients with an intelligent, multi-step form. On submission, the data is sent to an AWS Lambda function running Python. This function uses the Claude API to parse the form's contents, reliably extracting structured data like medication lists, allergies, and insurance policy numbers. That structured JSON is then securely transmitted to your PMS API, creating or updating the patient record in under 5 seconds. All processing events, including the raw data and the Claude API's output, are logged in a Supabase database to provide a complete, immutable audit trail for HIPAA compliance.

The delivered system is a secure web application that your practice can link to from your website. Patients can complete their intake and book appointments from any device, and the data appears in your PMS instantly and accurately. You receive the full source code, a runbook for maintenance, and a signed Business Associate Agreement (BAA). The entire system is built for a 4-6 week timeline and runs on serverless infrastructure for less than $50 per month in hosting fees.