Build Custom AI for EHR Data Integration

Small specialty clinics often use EHRs like Practice Fusion or Kareo. These systems manage internal patient data well but falter at external integration. Their APIs are frequently limited, require expensive partner-tier subscriptions, or are nonexistent for the specific third-party service you rely on. When a crucial partner, like a pathology lab, sends results as a PDF attached to a secure email, the EHR offers no solution.

Consider a 12-person dermatology practice that performs 15-20 biopsies per day. Their preferred lab emails PDF pathology reports. A front-desk staff member must download each PDF, open the patient's chart in Practice Fusion, and manually transcribe the diagnosis, specimen ID, and CPT codes. This process takes 5-7 minutes per report, consuming over 2 hours of valuable staff time daily. This manual work introduces a significant risk of transcription errors that can impact both patient safety and billing accuracy.

The structural problem is that most EHRs are designed as closed data platforms. Their business model incentivizes selling proprietary modules, not enabling open connections to external services. The EHR's architecture assumes it is the single source of truth, forcing staff into manual workarounds for any data that originates outside its walls. This creates a brittle, inefficient system dependent on human data entry.

The project would begin with an audit of your data sources and destinations. We would map every field from an incoming lab PDF to the corresponding field in your EHR. This involves analyzing sample documents and defining the business logic, for instance, how to flag an abnormal result for physician review. You receive a detailed data mapping document and a fixed-scope proposal before any code is written.

The core system would be a HIPAA-compliant pipeline on AWS, where Syntora has a Business Associate Agreement. An AWS Lambda function would trigger on new files in a secure S3 bucket. This function would use Python and the Claude API to parse the incoming document, extracting structured data with over 99.5% accuracy. The extracted data is validated using Pydantic schemas before a FastAPI service transforms it into the format required by your EHR's API.

The delivered system is fully automated. When a lab report arrives, it is processed and entered into the correct patient chart in under 30 seconds. A human review gate is built in for low-confidence extractions, notifying a specific staff member in a HIPAA-compliant channel like Spruce Health. You receive the full source code, a Supabase dashboard for monitoring processing logs with a full audit trail, and documentation. Hosting costs on AWS are typically under $50 per month for this volume.