Doctors use ChatGPT, Claude, and Gemini every day, but pasting patient data into a chat box is not safe, and audio scribes miss $205K in undercoded visits and $148K in CCM billing. MedScrub reads your actual EHR data and turns compliance requirements into captured revenue.
Every other clinical AI company builds a proprietary model, locks you in, and processes your raw patient data on their servers. You pay a premium for their model choice, their data handling, their cloud.
We asked a different question: what if you could use any AI model safely?
Consumer LLMs are already the best reasoning engines on the planet, and they're getting better every month. The missing piece is not the model. It's a way to use them with clinical data without violating HIPAA.
MedScrub is not another AI scribe. It's infrastructure that makes any consumer LLM safe for clinical work.
All 18 HIPAA identifiers are stripped before data reaches any AI model. Names, dates, MRNs, addresses: replaced with deterministic tokens that are 100% reversible on the way back.
Patient data syncs from your EHR into a local CDR that your practice owns. Labs, vitals, conditions, medications, encounters: structured FHIR data that powers every AI workflow.
You choose the model: OpenAI, Claude, Gemini, Mistral, or local models via Ollama. API keys stay on your machine. As models improve, MedScrub improves with them, with no vendor lock-in.
EHR → CDR → PHI Proxy → Consumer LLM → Re-identified Output → EHR Write-back
Patient data never reaches an AI model with identifiers attached. The physician sees the final output with all context restored.
We do not train models. We do not run GPU clusters. Consumer LLMs do the heavy lifting; we make them safe. As models get cheaper and better, so does MedScrub.
When a better model comes out (and one always does) you switch in seconds. No migration, no retraining, no contract renegotiation.
The CDR accumulates structured clinical data over time. Every sync makes SOAP notes more accurate, pre-visit summaries more complete, and prior auth letters more evidence-based.
PHI de-identification is not a policy; it is a technical guarantee. The proxy strips identifiers deterministically. There is no way for an AI model to see your patient’s name, even if it tried.
MIPS quality measures, CCM billing, E/M coding accuracy: each generates recurring revenue. A practice that finds $148K/year in CCM billing in year 1 keeps that revenue in year 2 and year 3. The ROI on a CDR that tracks compliance data grows every year.
1PuttHealth helps companies build on healthcare interoperability standards: FHIR, EHR integrations, clinical data infrastructure. MedScrub is our flagship product, the physician sidekick we kept seeing the market need.