Start an EHR Data De-Identification and Research Data Service

1. 1

   Master both legal pathways cold

   HIPAA offers two de-identification routes: safe harbor (remove the 18 identifier categories, with real subtlety around dates, geography, and free text) and expert determination (a qualified statistician documents that re-identification risk is very small, allowing richer datasets researchers actually want). Free-text notes are the hard part: names and details hide in narrative text, and modern NLP-assisted scrubbing plus human QA is the standard your methodology must meet. Partner with a credentialed statistician and healthcare privacy counsel from day one; their sign-offs are part of the product.

2. 2

   Build the pipeline as a repeatable system

   Extraction from the major EHR data models, transformation into research formats (OMOP common data model fluency is a differentiator the academic world pays for), automated identifier scrubbing with human review sampling, risk assessment documentation, and delivery into secure environments. Every run produces an auditable package: methodology, risk analysis, and certification. That paper trail is what lets a hospital's compliance office say yes.

3. 3

   Serve both sides of the market

   Provider-side engagements: preparing data for their own research programs, registry submissions, quality collaboratives, and AI vendor partnerships they are negotiating (where your governance work protects them). Buyer-side engagements: helping research teams and health-AI startups specify, source, and validate compliant datasets. Same pipeline, two invoices; conflicts disclosed plainly when you sit between them.

4. 4

   Handle the ethics and the optics deliberately

   'Selling patient data' is a headline risk for your clients even when legal, and patient trust surveys show real sensitivity. Guide clients toward defensible postures: IRB engagement where research applies, data use agreements limiting re-identification and resale, transparency language in privacy notices, and walking away from gray-zone requests. State privacy laws (Washington's My Health My Data and successors) now reach beyond HIPAA; track them as part of the service.

5. 5

   Price the trust, not the gigabytes

   Project fees for extraction and de-identification commonly run tens of thousands per dataset at hospital scale, expert determination engagements carry premium statistician-backed pricing, and ongoing arrangements (refreshing datasets quarterly for a research partnership) become retainers. The recurring version is the goal: a provider with one successful data partnership almost always signs more.

6. 6

   Grow along the AI demand curve

   Health AI development has made compliant training data one of the scarcest inputs in the industry, and every model developer needs exactly what this service produces. Relationships with academic medical centers, health-AI startups, and the interoperability and archival consultancies in this library (whose projects surface research-worthy data constantly) keep the pipeline warm without cold sales.

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Best for: Health data engineers, informaticists, and biostatistics-minded builders who love governance

Not for: Anyone who will under-scrub free-text notes or skip statistical and legal sign-off, since those are what make de-identification defensible. It is also not for people who take gray-zone requests, because 'selling patient data' is a headline risk for clients even when legal. This is not legal advice.