Find an AI Engineering Partner for Your Healthcare Data Project

Many practices try to use their EHR's built-in export/import functions, like those in Practice Fusion or Kareo. These tools work for simple patient list transfers but fail with complex data. They often export to flat CSV files, losing the relational context between appointments, billing codes, and clinical notes. The result is a 'successful' import that leaves patient histories fragmented and unusable for reporting.

Consider a 20-person specialty clinic acquiring a smaller practice. The acquired practice uses an older, on-premise EHR, while the main clinic is on Athenahealth. The goal is to merge patient records. The off-the-shelf migration tool successfully moves demographics but corrupts the appointment history, linking them to the wrong providers. Now, front-desk staff must manually verify every patient's history during check-in, causing 10-minute delays and frustrating patients.

The core problem is that integration platforms like Mirth Connect handle technical translation but not custom business logic. They can map HL7v2 fields to FHIR resources, but they cannot deduplicate patient records based on fuzzy name matching plus date of birth, or map old provider IDs to new ones. They move data but do not clean or validate it in the context of your practice's specific rules.

The consequence is 'data debt' that creates ongoing operational drag. Inaccurate billing codes lead to rejected claims, and incomplete patient histories create clinical risks. The initial cost-saving of using a cheaper tool is erased by months of manual cleanup and increased operational overhead. This is a software engineering problem, not a configuration problem.

The first step is a data mapping workshop. Syntora would work directly with your clinical staff to audit the source and target EHR systems, field by field. This produces a detailed specification document that maps every data point, defines transformation rules, and identifies potential data quality issues. This 5-day audit is critical because it prevents scope creep and ensures the final system meets clinical and operational needs.

The technical approach uses a data processing pipeline built in Python, running on AWS Lambda for event-driven processing. For unstructured data like clinical notes, the Claude API can parse and extract structured entities with a 200ms response time. Data is staged in a HIPAA-compliant Supabase (PostgreSQL) database for validation before being sent to the target system. This architecture handles up to 1,000 records per minute and provides a full audit trail for every transformation.

The final deliverable is a maintainable, automated integration system. The system would include a human-in-the-loop review interface, built with Vercel, for your staff to approve records that fall below a 95% confidence score. You receive all the source code in your GitHub repository and a runbook for operations. A typical build for this system takes 6-8 weeks.