Implement AI Patient Scheduling for Your Practice

Most small practices rely on their EMR's built-in calendar or a tool like Solutionreach. These tools are effective for booking simple appointments but fail when scheduling requires judgment. They cannot read a PDF referral from a specialist, understand the urgency, and match it to the right provider's availability. The workflow remains manual, forcing staff to act as human routers.

Consider a 3-provider practice that receives a new patient request via email with two attachments: a scanned intake form and a photo of an insurance card. The office manager must open both files, manually type the patient's name, DOB, and insurance details into the EMR, and then decipher the clinical information on the intake form. They have to decide if this patient needs a 30-minute standard slot or a 45-minute complex consult, a decision that is critical for clinic flow and proper billing. This process takes 10-15 minutes per patient and is prone to transcription errors.

The core problem is that off-the-shelf scheduling tools are built with rigid data models. They expect structured input from a web form, not unstructured text from a doctor's note or a scanned PDF. They lack the ability to run conditional logic specific to your practice, such as 'If the referral mentions 'cardiac evaluation', schedule with Dr. Smith within 7 days and block 45 minutes.' You are forced to adapt your practice's workflow to the software's limitations.

The first step would be a process audit. Syntora would map your entire patient intake and scheduling workflow, from initial contact to confirmed appointment. We would analyze your current intake forms, common referral types, and the specific scheduling preferences of each provider. This discovery phase produces a detailed technical specification that you approve before any code is written.

The technical approach would use a HIPAA-compliant stack on AWS. A Python service on AWS Lambda would use the Claude API to perform Optical Character Recognition (OCR) and data extraction from submitted documents. This process converts a 5-page PDF intake form into structured JSON in under 15 seconds. A FastAPI application would then apply your practice's unique scheduling rules to this data, query your EMR's API for open slots, and rank the top 3 best-fit appointments.

The delivered system would be a simple web interface for your office staff. It would display the extracted patient data alongside the suggested appointment times, allowing for a 1-click confirmation that writes directly to the EMR. You receive the complete source code, a runbook for maintenance, and the system is deployed within your own AWS account. You have full ownership and control, with no ongoing licensing fees.