Automate Routine Clinical Operations for Nurses

Small healthcare facilities often run on EHRs like Kareo or Practice Fusion. These platforms are excellent for managing structured patient records but offer limited tools for ingesting new data. When a referral arrives as a 12-page faxed PDF, a nurse must manually read through it, find the patient's name, date of birth, insurance ID, and clinical history, then type everything into dozens of separate fields in the EHR. This process is slow and a common source of data entry errors that can impact patient care and billing.

Consider a 10-person specialty clinic that receives 20 referrals a day. A nurse spends 15 minutes on each one, re-keying information from the PDF into the EHR. That's five hours of a skilled nurse's day spent on administrative work. They might try a generic OCR tool, but it will just dump raw text. The tool won't differentiate between the referring physician's NPI and the patient's insurance ID, or understand that "Meds: lisinopril 10mg" belongs in the medications field.

The structural issue is that EHRs are built to be systems of record, not systems of workflow. Their architecture prioritizes data storage and retrieval over intelligent intake. Off-the-shelf automation tools lack the medical context to interpret clinical documents correctly and cannot navigate the strict requirements of HIPAA compliance. This leaves a gap where skilled nurses are forced to act as human data-entry clerks, bridging the divide between unstructured documents and the clinic's core software.

The first step is a workflow audit. Syntora would analyze your most common document types, like referral packets and lab results, to create a data map. This map defines every piece of information that needs to be extracted and specifies exactly where it should go in your EHR. This process ensures the final system captures all required data and matches your existing clinical charting procedures.

The technical system would be a secure, HIPAA-compliant service built with Python and FastAPI, running on AWS Lambda. When a nurse uploads a PDF, the service sends it to the Claude API, which reads and structures the clinical data. Pydantic models validate the extracted information against the data map. The system then connects to your EHR's API to populate a new patient record or update an existing one, flagging any ambiguous fields for human review.

The delivered system is a simple web portal where staff can drag and drop documents. Within 60 seconds, they see a confirmation showing the data that was pushed to the EHR. An audit log, stored in Supabase, tracks every document, who reviewed it, and when the data was sent. The nurse's job shifts from 15 minutes of typing to a 1-minute verification, freeing up hours each day for patient-facing activities.