Instant Access to Anonymized Healthcare Data, Validate with Real-World Data

Start exploring immediately with high-quality, anonymized datasets. No IRBs, DUAs, or lengthy approvals required. When you're ready, validate your work on real patient-level data — all in one platform.

Join Waitlist

Our datasets are used by research teams at

Fast Start. Real Data. Serious Tools.

Bring an end to critical bugs in production and accelerate your release cycles by fueling your staging and QA environments with data that mirrors the complexity of production.

Start Fast with Simulated Data

Jump right in with high-quality, anonymized datasets. No approvals needed — just log in and start working.

Test Your Work on Real Patient Data

Once you're ready, seamlessly run your models on real-world, de-identified patient-level data.

AI-Ready Environment

Develop and validate faster in a cloud workspace preloaded with Python, R, and Stata. Just bring your code.

Secure Environment

SOC2 compliant, built on HIPAA and FedRAMP certified infrastructure.

Example Datasets

HealthcareDatahub provides real-world healthcare data—including EHRs, structured data, clinical notes, imaging, and all-payer claims—covering over 100M patients. Start studies faster, enhance research rigor, and make confident, data-driven decisions—all with patient privacy protected.

200M clinical notes from physicians across 50 US states

8M patients from 800 primary care facilities and 12,000 providers

85K patients with multimodal data, including EHRs, clinical notes, and imaging

100M patients represented in nationwide, longitudinal claims data

FAQs

You can begin exploring anonymized simulated patient data immediately after signing up — no IRBs, DUAs, or legal reviews required. Everything is ready to go in a secure, pre-configured environment designed for fast onboarding and rapid iteration.

Simulated data is ideal for exploratory analysis, hypothesis testing, model development, and validation workflows. It mirrors real-world patient distributions and care patterns, letting you build and refine methods before moving to actual patient-level datasets — all without compliance delays.

Once your analysis or model is ready, you can run it on our real-world de-identified patient datasets. We provide a controlled environment to execute your code securely, ensuring your findings are robust, reproducible, and grounded in actual patient data — without compromising privacy.

You can work in Python, R, SQL, or SAS — all accessible through a Jupyter-based environment preconfigured for healthcare data science. Our platform runs on a scalable cloud backend with both CPU and GPU options, so you can go from analysis to model training without changing environments or waiting on infrastructure.

Power Healthcare Innovation, Backed by Millions of Real Patient Records

Work faster with instant simulated datasets, validate with real-world evidence, and develop in a secure, cloud-native environment — no delays, no red tape.