Improve Diagnostic Support and Treatment Plans with Custom AI

Most specialty clinics rely on their EHR's built-in tools. Systems like Practice Fusion or Kareo are excellent for billing and maintaining records, but their clinical decision support is limited to static, rule-based alerts for drug allergies. They cannot read a 20-page PDF of a prior authorization, identify key markers for a specific condition from unstructured text, and compare them against the latest clinical guidelines. The clinician is still forced to perform hours of manual chart review.

Consider a 10-person rheumatology clinic. A new patient arrives with a complex history from three previous providers, delivered as a mix of scanned PDFs and faxed notes. A clinician must manually read hundreds of pages to piece together the diagnostic journey, looking for specific lab values or imaging results that are buried in narrative text. This process can take 45 minutes per patient and is prone to human error or oversight.

General AI copilots that are not built for healthcare are a non-starter, as using them would immediately violate HIPAA. Even compliant ones lack critical context. They do not know your clinic’s specific treatment protocols or preferred referral partners. The output is generic advice, not an integrated step in your clinical workflow. The structural problem is that off-the-shelf software is built for general practice. You are locked into the vendor's data model, unable to create workflows for the niche, evolving guidelines central to your specialty.

The first step is a deep dive into your clinical workflow and data sources. We would map the patient journey from intake to treatment plan, identifying the specific manual review steps that consume the most clinician time. This involves auditing your EHR's API, reviewing your most-used clinical guidelines, and analyzing the format of incoming patient records. You receive a technical specification outlining the data flow and integration points before any code is written.

The technical approach would use a HIPAA-compliant AWS environment as the foundation. We would build a FastAPI service running on AWS Lambda to create a secure API endpoint that your EHR can call. When a new patient record is ready for review, this service uses the Claude API via AWS Bedrock to read and structure data from PDFs and unstructured notes. This structured data is then checked against a digitized version of your clinical guidelines stored in a Supabase database.

The delivered system posts its findings back into your EHR as a draft clinical note. For example: "Patient history suggests potential Sjögren's syndrome. Consider ordering SSA/SSB antibody tests. Patient is currently on Lisinopril, which has a moderate interaction with the proposed Methotrexate treatment." The clinician sees this summary directly in their workflow, can verify the sources, and makes the final decision. The process would take 5-10 seconds.