Compliance & Validation — Documented
HIPAA
Flatiron Health
Oncology-focused real-world data and evidence company built on a nationwide network of oncology EHRs.
Flatiron Health, founded in 2012 and headquartered in New York City, curates real-world oncology data drawn from its OncoEMR electronic health record network and partner practices, producing de-identified, research-grade datasets and real-world evidence used by biopharma for research, regulatory, and market access work. The company applies machine learning alongside human abstraction to structure unstructured EHR data. Flatiron was acquired by Roche in 2018 and operates as a separate subsidiary.
- Headquarters
- New York, United States
- Founded
- 2012
- Compliance
- HIPAA
- Deployment options
- cloud
- Website
- https://flatiron.com
- https://www.linkedin.com/company/flatiron-health
Categories
AI Use Cases
- Market Access & HTA Strategy
- Patient Identification & Segmentation
Vendor Scorecard
64% — Partial coverage. Coverage score — how completely Atlas documents Flatiron Health across 7 buyer-evaluation areas. Reflects available Atlas data, not solution quality.
Deployment & Hosting — Documented
cloud
Pricing Transparency — Not documented by Atlas
Ask the vendor: What is your pricing model, and what drives total cost of ownership?
Implementation & Onboarding — Not documented by Atlas
Ask the vendor: What is a realistic implementation timeline, and what onboarding support is included?
Organization & Role Fit — Documented
Mid-Size Biotech, Large Pharma, Top 20 Pharma, Medical Affairs, R&D Leadership, Data Management
Solution Scope — Documented
Natural Language Processing, Real-World Evidence
Company Viability — Partially documented
Private, Founded 2012
Always ask the vendor
Not captured in Atlas data — confirm directly. Excluded from the score above.
Data Rights & IP
Who owns model outputs? Is our proprietary data used to train models others can access, and where does our data reside?
Model Credibility & Explainability
Can you share validation studies or benchmarks, explain how the model reaches its conclusions, and describe how bias and drift are monitored?
Questions to Ask This Vendor
- How is the de-identified dataset composed for our tumor type, and how current is it?
- How is abstraction quality measured for ML-assisted curation?
- Which regulatory or HTA precedents have used Flatiron evidence?
- How does Roche ownership affect data access and independence for our studies?