Automate New Patient Intake and Consent Forms

Many clinics try to solve this with their EHR's built-in forms, like those in Practice Fusion or Kareo. These tools are rigid and only work for patients who fill them out online. They offer no solution for the patient who walks in with a paper form from a referring physician, forcing your staff back into manual data entry.

Another approach involves digital form builders like Jotform. While these create a clean digital workflow, they fail to bridge the gap to the physical world. A new patient arriving with a printed, hand-filled 5-page packet still requires a staff member to manually transcribe every field. These tools solve only one intake channel, not the entire mixed paper-and-digital reality of a clinic.

Consider a 10-person dermatology clinic seeing 20 new patients a day. The front desk staff spends 7-10 minutes per patient typing demographics, insurance IDs, and medical history from a scanned PDF into Athenahealth. That's over 2 hours of clerical work daily. A single typo in a 12-digit policy number can cause a claim denial weeks later, requiring 30 minutes of follow-up to correct.

The structural issue is that off-the-shelf tools lack medical context. Generic OCR software can read text but cannot accurately distinguish 'Patient's Primary Phone' from 'Emergency Contact's Phone'. EHR-native forms cannot read paper. A custom solution is needed to connect the unstructured data on a scanned page to the structured fields of an EHR.

The first step is a workflow audit. Syntora would review your current patient intake forms, your existing EHR system, and the specific data fields you need to capture. We'd map every field from your paper forms to the corresponding field in your EHR's database schema. This discovery phase produces a detailed data mapping document and a technical specification for your approval before any code is written.

The core system would be an AWS Lambda function written in Python, triggered when a new intake form is uploaded to a secure S3 bucket. The function uses the Claude API to perform optical character recognition (OCR) and structured data extraction. Claude's large context window is ideal for processing multi-page documents (up to 150 pages) and understanding the relationship between labels and values. Pydantic models validate the extracted data against expected formats, like date of birth or insurance policy numbers, before attempting to write to the EHR.

The delivered system is a HIPAA-compliant pipeline that connects your scanner to your EHR. Your staff scans the paper forms, and within 60 seconds, the new patient record is created or updated. For quality control, any fields the AI flags with low confidence (below a 95% threshold) are routed to a human review queue in a simple web interface built with Vercel. You receive the full source code, an audit trail, and a maintenance runbook.