Improve Patient Flow and Resource Allocation with Custom AI Automation

Many clinics rely on the built-in features of their EHR, like Epic or Cerner. These platforms are excellent systems of record but their automation capabilities are rigid. They cannot parse unstructured data from a PDF referral sent by an outside practice, forcing staff to manually re-type patient history, insurance details, and reason for visit.

Consider a cardiology clinic that receives 30-40 faxed or emailed referrals daily. A staff member opens each PDF, which can be a 12-page document from a referring PCP. They must find the patient's name, DOB, insurance ID, and specific diagnosis codes, then copy-paste each field into the EHR to create a new patient record. This takes 15 minutes per referral and is prone to transcription errors, especially with complex insurance policy numbers.

Practice management software like Kareo or AdvancedMD offers some workflow tools, but they operate on structured data only. They cannot 'read' a PDF. This is a structural limitation; their data models are fixed, designed for form-fills, not for interpreting unstructured text from diverse external sources. You cannot add a rule to 'find the ICD-10 code next to the word diagnosis' because the system doesn't understand language, only fields.

The result is a bottleneck. Patients wait days for their referral to be processed before an appointment can even be scheduled. Highly-trained medical staff spend hours on clerical work, and a single mistyped digit in an insurance ID can lead to a rejected claim weeks later, costing an average of $25 to rework.

Syntora would start with an audit of your current patient intake and referral workflows. We would analyze 50-100 sample documents (referrals, new patient forms, insurance cards) to map all data variations. This discovery phase produces a clear data schema and a technical plan, which you approve before any code is written.

The core of the system would be a HIPAA-compliant pipeline on AWS Lambda. When a new document arrives, a function uses the Claude API to parse the text and extract structured data like demographics and clinical notes. We use Python with Pydantic for strict data validation to ensure the output matches your EHR's format. A human review gate would be built for edge cases, flagging low-confidence extractions for manual review.

The final system would automatically populate new patient records in your EHR. Instead of manual entry, your staff would see a pre-filled record ready for a 10-second review. You receive all the source code, deployed in your own AWS account, plus a runbook. Syntora has built similar document processing pipelines for financial services; the architectural pattern of using LLMs for extraction and serverless functions for processing applies directly to clinical documents.