How One Specialty Clinic Clawed Back 10x ROI in Months

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%.

~75%

Cut from expert lookup time

2–4 months

Implementation Time

Not disclosed

Project Cost

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.

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.

Ai ROLE

AI Agent parses unstructured documents, interprets the documents, normalizes the data and empowers the navigator workflow.

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.

Clint Irwin

VP, AI Strategy & Implementation at ScrumLaunch

ScrumLaunch

Clint leads AI Strategy at ScrumLaunch, uniting client business objectives with technological solutions across healthcare, manufacturing, and media & entertainment.

GEt an intro

industry

Healthcare & Life Sciences

business organization

Sales & Revenue

AI TYpe

Data Synthesis & Reporting

value type

Revenue Growth

other(firm) Case studies

frequently asked questions

How did a specialty healthcare clinic use AI to claw back a 10x return in months?

A mid-to-large healthcare clinic built an OpenAI-powered agent that parsed unstructured clinical notes, PDFs, fax scans, and historical archives, normalizing them into structured patient profiles searchable by clinical criteria. The harder part was securing data access from legacy EMR systems, which the team forced through. Once records flowed, navigators could query any cohort in seconds instead of hours, with payback measured in months rather than the years typical of comparable healthcare data investments.

What AI tools and models did the healthcare clinic use?

The system used the OpenAI API to power an agent that analyzed millions of pages of medical records, built with Figma, LangChain, Notion, Cursor, and Lovable. The approach centered on data synthesis and reporting, making previously invisible patients searchable by clinical criteria.

What results did the healthcare clinic achieve?

The clinic saw a roughly 10x return relative to investment, a 50% increase in procedural revenue, and faster patient identification that let navigators surface eligible patients quicker and increase velocity, alongside improvements in revenue cycle management.

How long did the AI patient-search system take?

Time to results was under four weeks, with payback measured in months rather than the years typical of comparable healthcare data infrastructure investments.

Who is this AI patient-search approach best for?

Clinics of any specialty looking to increase procedural volume, and those wanting clean, structured data suitable for clinical trials and other data monetization strategies.

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