Custom AI Automation for Small Healthcare Providers
Syntora develops custom AI automation for small healthcare providers with a hands-on engineer. All systems are HIPAA-compliant and built from scratch to fit your clinic's specific workflow.
This is not an off-the-shelf product. It is production-grade software engineered for your exact operational needs, from parsing referral faxes to scheduling patient appointments. The person on your discovery call is the same engineer who writes every line of code for your system.
We recently built a patient intake system for a 12-person dermatology practice processing 400 new patients a month. The new system reduced manual data entry time from 15 minutes per patient to under 60 seconds for review. The entire build was deployed in 4 weeks.
What Problem Does This Solve?
Small healthcare providers often rely on the limited automation features within their Electronic Health Record (EHR) system. These modules can create calendar events from a form, but they cannot interpret unstructured data. This means your front-desk staff still manually transcribes information from emailed referrals or scanned intake forms into the EHR, a process that takes 5-10 minutes per patient and is prone to data entry errors.
A 10-provider physical therapy clinic tried to solve this with a popular online form builder connected to their EHR. The connection was brittle and broke whenever the form was updated. More critically, the form builder was not designed for Protected Health Information (PHI). Storing patient data in its logs created a HIPAA compliance risk, as they did not have a Business Associate Agreement (BAA) with the vendor, and a single breach could result in a $50,000 fine.
These generic tools fail because they are not built for the regulatory and operational complexity of healthcare. They lack the logic for insurance verification, cannot handle faxed documents, and do not provide the immutable audit trails required for compliance. You are left with a workflow that is still 80% manual and introduces significant compliance vulnerabilities.
How Does It Work?
We begin by mapping your existing patient intake and referral management process. We use the Claude API to build a document processing pipeline that reads scanned faxes and emailed PDFs. This pipeline, written in Python and running on AWS Lambda, extracts key entities like patient name, date of birth, referring physician, and insurance details with over 98% accuracy. The remaining 2% are automatically flagged for human review.
For a 30-person orthopedic group handling 600 referrals per month, we built a custom validation layer. After the AI extracts the data, a FastAPI service checks the patient's insurance eligibility against the practice's accepted provider list via a third-party API. This pre-verification step eliminated claim denials due to out-of-network issues, which previously affected 7% of new patients. The entire process from receiving a fax to creating a verified patient record in the EHR takes under 90 seconds.
Every action is logged to a dedicated audit trail in a HIPAA-compliant Supabase database. The human review interface is a simple web application deployed on Vercel where staff can view the original document and the extracted data side-by-side. Low-confidence fields are highlighted in yellow, allowing for correction in under 30 seconds. This combination of AI processing and human oversight ensures both speed and accuracy.
We configure monitoring using structlog for structured logging and send alerts through PagerDuty. If the EHR's API response time exceeds 500ms or the AI's extraction accuracy drops, you receive an immediate notification. The entire serverless architecture typically costs less than $100 per month to run on AWS, a fixed cost that does not increase with patient volume.
What Are the Key Benefits?
Live in 4 Weeks, Not 6 Months
From our first call to a production-ready system in 20 business days. We scope tightly and build quickly, focusing on a single high-impact workflow.
A Fixed Build Cost, Not a SaaS Bill
One scoped project fee, followed by minimal monthly cloud hosting costs. No per-seat licenses or surprise fees that scale with your practice's growth.
You Own The Code and The System
You receive the complete Python source code in your own GitHub repository. This is a permanent asset, not a temporary software rental.
HIPAA-Compliant from Day One
We sign a BAA and build with compliance in mind, including encrypted data, access controls, and immutable audit trails for every transaction.
Integrates Directly With Your EHR
We build connectors that write data directly into your existing EHR, whether it is Practice Fusion, Kareo, or another system with API access.
What Does the Process Look Like?
Workflow Discovery (Week 1)
You provide access to your current tools and anonymized sample documents. We sign a BAA and deliver a detailed technical specification for the proposed system.
System Development (Weeks 2-3)
We build the core AI processing pipeline and integration points. You receive access to a staging environment to test the workflow with your team.
Deployment and Training (Week 4)
We deploy the system to production and conduct a one-hour training session with your staff. You receive the system runbook and complete documentation.
Post-Launch Monitoring (Weeks 5-8)
We provide a 30-day monitoring and support period to resolve any issues. At the end, we fully hand off the system and all associated cloud accounts.
Frequently Asked Questions
- What is the typical cost and timeline?
- Pricing is based on the complexity of the workflow and the quality of your EHR's API. A single workflow, like referral processing, typically takes 4 weeks. A multi-stage process involving scheduling and insurance verification may take longer. We provide a fixed-price quote after our initial discovery call, so there are no surprises.
- What happens if the AI makes a mistake on a patient record?
- The system is designed to prevent this. Any data extracted with a confidence score below 95% is automatically flagged for human review. No data is written to your EHR without explicit approval from your staff through the review interface. The AI assists your team, it does not replace their final judgment on clinical data.
- How is this different from hiring a Virtual Assistant (VA)?
- A VA performs manual tasks during business hours. Our system is a software asset that works 24/7 with near-zero marginal cost per task. It provides a consistent, auditable process that eliminates human error. Instead of paying a recurring salary for manual work, you invest once in an automated system you own forever.
- What happens if the founder of Syntora is unavailable?
- This is a critical risk we plan for. Upon project completion, you receive the full source code, deployment scripts, and a detailed runbook. The system is built with standard Python and AWS components. Any competent cloud engineer can take over maintenance. You are not locked into a proprietary platform or a single person.
- Do we need an IT team to maintain this?
- No. The system is built on serverless AWS Lambda functions that require no server management. It is designed to run with minimal intervention. We provide a support plan for practices that want ongoing maintenance, but the runbook covers common troubleshooting steps if you prefer to manage it with your existing technical resources.
- Our practice still receives most referrals by fax. Can you handle that?
- Yes. We integrate with e-fax services that provide a digital copy (PDF or TIFF) of each incoming fax. Our system ingests these files directly from the e-fax provider's API or a designated cloud storage bucket. The AI pipeline is specifically trained to handle the lower quality and varied formats typical of scanned documents.
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