How One Specialty Clinic Clawed Back 10x ROI in Months With AI
A specialty practice's navigators wired clinical notes, PDFs, and fax scans into an AI agent — surfacing eligible patients in seconds and lifting procedural revenue 50%.
10x
Returned in months, not years
The Challenge
Despite managing 100,000+ patient
populations, practices could only identify a fraction of patients
eligible for profitable procedures and clinical trials. Patient identification took hours. Patient recall and outreach was partial. Finding patients to optimize staffing and scheduling was a bottleneck.
What They Built
AI agent-based system that analyzes millions of
pages of medical records, making every patient instantly
searchable by clinical criteria. High value patients existed in their current data but were operationally invisible across unstructured clinical notes, PDFs, faxes scans, EMR systems and historical archives.
Healthcare companies sit on vast unstructured clinical data. AI that extracts intelligence improves efficiency and unlocks entirely new revenue streams.
ScrumLaunch's team built the system in two parallel tracks: the AI logic and the EMR plumbing. The AI side moved fast — an OpenAI-powered agent was wired to parse unstructured clinical notes, PDFs, fax scans, and historical archives, interpreting medical context and normalizing the output into structured patient profiles searchable by clinical criteria. The harder challenge was access. Specialty practices' EMR systems are legacy platforms with clunky interfaces and opaque gate-keeping. Traditional vendor-support paths stalled; the team learned that progress only came through brute force — making the integration as painful for the EMR vendor as it was for them — to secure the data access required. Once the records flowed reliably, the agent was embedded directly into the patient navigator workflow, so navigators could query any cohort by clinical criteria in seconds instead of combing records for hours. Time-to-results was under four weeks, with payback measured in months rather than the years typical of comparable healthcare data infrastructure investments.
AI Role
AI Agent parses unstructured documents, interprets the documents, normalizes the data and empowers the navigator workflow.
Infrastructure
OpenAI API — LLM runtime powering the agent • Custom AI agent layer for parsing and interpreting unstructured medical documents • Structured patient data store indexed by clinical criteria • Legacy EMR systems at the specialty practice (multiple platforms)
Integration Points
EMR systems — direct data extraction from legacy specialty-practice platforms • Unstructured document ingestion: clinical notes, PDFs, fax scans, historical archives • Patient navigator workflow — agent outputs surfaced inline for cohort search and outreach
Impact
10x Revenue vs Return on Investment
Payback in months vs years. Unlocked latent revenue.
50% increase in procedural revenue.
Improvement in revenue cycle management.
Cut time for patient identification
Navigators able to surface eligible patients quicker, increasing 'sales' velocity.
Implementation Complexity
Best Fit For
Any clinic from any specialty would benefit from this. We delivered increased procedural volume. Further, we delivered clean, structured data suitable for clinical trials and other data monetization strategies.
