Build Custom AI Automation for Your Healthcare Practice

Many small practices rely on their Practice Management System, like Kareo or athenahealth, for basic workflows. These systems are excellent for billing and scheduling but offer limited, rule-based automation. They cannot, for example, read an unstructured referral PDF from another provider and automatically create a patient record with the correct data fields.

Consider a 3-person admin team at a specialty clinic receiving 20-30 faxed or emailed referrals a day. Each referral is a PDF with a different format. A team member must manually open each file, find the patient's name, DOB, referring physician, and reason for visit, then copy that data into the EHR. This task takes 5-10 minutes per referral, consuming over 10 hours a week in pure data entry.

The structural issue is that EHR systems are built around structured data entry, not unstructured data interpretation. Their architecture assumes a human will read the document and type data into the correct fields. They lack the built-in AI models needed to parse natural language and extract information from diverse document layouts. Adding this capability is not a simple feature; it requires a new data processing pipeline.

This manual bottleneck creates a 24-48 hour delay in scheduling new patients and increases the risk of data entry errors that impact patient safety and billing accuracy. The practice is forced to hire more admin staff just to keep up with document volume, rather than investing in patient-facing roles.

The engagement would begin with an audit of your current clinical workflows and data sources. We would map the exact journey of a patient referral, from the moment the PDF arrives to when the appointment is confirmed in your EHR. This process identifies the specific data fields to be extracted and the business logic for validation, ensuring the AI system fits your practice's exact needs. You receive a detailed scope document outlining the system architecture.

The technical approach involves building a HIPAA-compliant document processing pipeline using AWS Lambda and the Claude API. When a new referral arrives, a Lambda function triggers. The Claude API parses the document, extracts key entities like patient demographics, and structures the output as a JSON object. We use Python with Pydantic for strict data validation, ensuring the data conforms to your EHR's format before any record is created.

The delivered system operates automatically in the background. Extracted data is presented to your admin team in a simple review interface before being committed to the EHR, providing a critical safety check. You receive the complete Python source code, a runbook, and a system deployed in your own HIPAA-eligible AWS account. You have full ownership and control, with no ongoing license fees.